Best Computer Vision Courses

Find the best online Computer Vision Courses for you. The courses are sorted based on popularity and user ratings. We do not allow paid placements in any of our rankings. We also have a separate page listing only the Free Computer Vision Courses.

Convolutional Neural Networks in Python: CNN Computer Vision

Python for Computer Vision & Image Recognition - Deep Learning Convolutional Neural Network (CNN) - Keras & TensorFlow 2

Created by Start-Tech Academy - 3,000,000+ Enrollments | 4+ Rated | 160+ Countries

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Students: 97264, Price: $19.99

Students: 97264, Price:  Paid

You're looking for a complete Convolutional Neural Network (CNN) course that teaches you everything you need to create a Image Recognition model in Python, right?

You've found the right Convolutional Neural Networks course!

After completing this course you will be able to:

  • Identify the Image Recognition problems which can be solved using CNN Models.

  • Create CNN models in Python using Keras and Tensorflow libraries and analyze their results.

  • Confidently practice, discuss and understand Deep Learning concepts

  • Have a clear understanding of Advanced Image Recognition models such as LeNet, GoogleNet, VGG16 etc.

How this course will help you?

A Verifiable Certificate of Completion is presented to all students who undertake this Convolutional Neural networks course.

If you are an Analyst or an ML scientist, or a student who wants to learn and apply Deep learning in Real world image recognition problems, this course will give you a solid base for that by teaching you some of the most advanced concepts of Deep Learning and their implementation in Python without getting too Mathematical.

Why should you choose this course?

This course covers all the steps that one should take to create an image recognition model using Convolutional Neural Networks.

Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business.

What makes us qualified to teach you?

The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Deep learning techniques and we have used our experience to include the practical aspects of data analysis in this course

We are also the creators of some of the most popular online courses - with over 300,000 enrollments and thousands of 5-star reviews like these ones:

This is very good, i love the fact the all explanation given can be understood by a layman - Joshua

Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy

Our Promise

Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.

Download Practice files, take Practice test, and complete Assignments

With each lecture, there are class notes attached for you to follow along. You can also take practice test to check your understanding of concepts. There is a final practical assignment for you to practically implement your learning.

What is covered in this course?

This course teaches you all the steps of creating a Neural network based model i.e. a Deep Learning model, to solve business problems.

Below are the course contents of this course on ANN:

  • Part 1 (Section 2)- Python basics

    This part gets you started with Python.

    This part will help you set up the python and Jupyter environment on your system and it'll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.

  • Part 2 (Section 3-6) - ANN Theoretical Concepts

    This part will give you a solid understanding of concepts involved in Neural Networks.

    In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.

  • Part 3 (Section 7-11) - Creating ANN model in Python

    In this part you will learn how to create ANN models in Python.

    We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. Lastly we learn how to save and restore models.

    We also understand the importance of libraries such as Keras and TensorFlow in this part.

  • Part 4 (Section 12) - CNN Theoretical Concepts

    In this part you will learn about convolutional and pooling layers which are the building blocks of CNN models.

    In this section, we will start with the basic theory of convolutional layer, stride, filters and feature maps. We also explain how gray-scale images are different from colored images. Lastly we discuss pooling layer which bring computational efficiency in our model.

  • Part 5 (Section 13-14) - Creating CNN model in Python
    In this part you will learn how to create CNN models in Python.

    We will take the same problem of recognizing fashion objects and apply CNN model to it. We will compare the performance of our CNN model with our ANN model and notice that the accuracy increases by 9-10% when we use CNN. However, this is not the end of it. We can further improve accuracy by using certain techniques which we explore in the next part.

  • Part 6 (Section 15-18) - End-to-End Image Recognition project in Python
    In this section we build a complete image recognition project on colored images.

    We take a Kaggle image recognition competition and build CNN model to solve it. With a simple model we achieve nearly 70% accuracy on test set. Then we learn concepts like Data Augmentation and Transfer Learning which help us improve accuracy level from 70% to nearly 97% (as good as the winners of that competition).

By the end of this course, your confidence in creating a Convolutional Neural Network model in Python will soar. You'll have a thorough understanding of how to use CNN to create predictive models and solve image recognition problems.

Go ahead and click the enroll button, and I'll see you in lesson 1!

Cheers

Start-Tech Academy

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Below are some popular FAQs of students who want to start their Deep learning journey-

Why use Python for Deep Learning?

Understanding Python is one of the valuable skills needed for a career in Deep Learning.

Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history:

    In 2016, it overtook R on Kaggle, the premier platform for data science competitions.

    In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools.

    In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.

Deep Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well.

What is the difference between Data Mining, Machine Learning, and Deep Learning?

Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions.

Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.

Computer Vision with Python

Learn the latest techniques in computer vision with Python and OpenCV!

Created by Code Warriors - The best place to learn, code and conquer - Once you have it

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Students: 55970, Price: $19.99

Students: 55970, Price:  Paid

Welcome to the ultimate online course on Python for Computer Vision!

This course is your best resource for learning how to use the Python programming language for Computer Vision.

We'll be exploring how to use Python and the OpenCV (Open Computer Vision) library to analyze images and video data.

The most popular platforms in the world are generating never before seen amounts of image and video data. Every 60 seconds users upload more than 300 hours of video to Youtube, Netflix subscribers stream over 80,000 hours of video, and Instagram users like over 2 million photos! Now more than ever it's necessary for developers to gain the necessary skills to work with image and video data using computer vision.

Computer vision allows us to analyze and leverage image and video data, with applications in a variety of industries, including self-driving cars, social network apps, medical diagnostics, and many more.

As the fastest growing language in popularity, Python is well suited to leverage the power of existing computer vision libraries to learn from all this image and video data.

In this course, we'll teach you everything you need to know to become an expert in computer vision! This $20 billion dollar industry will be one of the most important job markets in the years to come.

We'll start the course by learning about numerical processing with the NumPy library and how to open and manipulate images with NumPy. Then will move on to using the OpenCV library to open and work with image basics. Then we'll start to understand how to process images and apply a variety of effects, including color mappings, blending, thresholds, gradients, and more.

Deep Learning and Computer Vision A-Z™: OpenCV, SSD & GANs

Become a Wizard of all the latest Computer Vision tools that exist out there. Detect anything and create powerful apps.

Created by Hadelin de Ponteves - AI Entrepreneur

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Students: 42215, Price: $89.99

Students: 42215, Price:  Paid

*** AS SEEN ON KICKSTARTER ***

You've definitely heard of AI and Deep Learning. But when you ask yourself, what is my position with respect to this new industrial revolution, that might lead you to another fundamental question: am I a consumer or a creator? For most people nowadays, the answer would be, a consumer.

But what if you could also become a creator?

What if there was a way for you to easily break into the World of Artificial Intelligence and build amazing applications which leverage the latest technology to make the World a better place?

Sounds too good to be true, doesn't it?

But there actually is a way..

Computer Vision is by far the easiest way of becoming a creator.

And it's not only the easiest way, it's also the branch of AI where there is the most to create.

Why? You'll ask.

That's because Computer Vision is applied everywhere. From health to retail to entertainment - the list goes on. Computer Vision is already a $18 Billion market and is growing exponentially.

Just think of tumor detection in patient MRI brain scans. How many more lives are saved every day simply because a computer can analyze 10,000x more images than a human?

And what if you find an industry where Computer Vision is not yet applied? Then all the better! That means there's a business opportunity which you can take advantage of.

So now that raises the question: how do you break into the World of Computer Vision?

Up until now, computer vision has for the most part been a maze. A growing maze.

As the number of codes, libraries and tools in CV grows, it becomes harder and harder to not get lost.

On top of that, not only do you need to know how to use it - you also need to know how it works to maximise the advantage of using Computer Vision.

To this problem we want to bring... 

Computer Vision A-Z.

With this brand new course you will not only learn how the most popular computer vision methods work, but you will also learn to apply them in practice!

Can't wait to see you inside the class,

Kirill & Hadelin

Python for Computer Vision with OpenCV and Deep Learning

Learn the latest techniques in computer vision with Python , OpenCV , and Deep Learning!

Created by Jose Portilla - Head of Data Science, Pierian Data Inc.

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Students: 35961, Price: $99.99

Students: 35961, Price:  Paid

Welcome to the ultimate online course on Python for Computer Vision!

This course is your best resource for learning how to use the Python programming language for Computer Vision.

We'll be exploring how to use Python and the OpenCV (Open Computer Vision) library to analyze images and video data.

The most popular platforms in the world are generating never before seen amounts of image and video data. Every 60 seconds users upload more than 300 hours of video to Youtube, Netflix subscribers stream over 80,000 hours of video, and Instagram users like over 2 million photos! Now more than ever its necessary for developers to gain the necessary skills to work with image and video data using computer vision.

Computer vision allows us to analyze and leverage image and video data, with applications in a variety of industries, including self-driving cars, social network apps, medical diagnostics, and many more.

As the fastest growing language in popularity, Python is well suited to leverage the power of existing computer vision libraries to learn from all this image and video data.

In this course we'll teach you everything you need to know to become an expert in computer vision! This $20 billion dollar industry will be one of the most important job markets in the years to come.

We'll start the course by learning about numerical processing with the NumPy library and how to open and manipulate images with NumPy. Then will move on to using the OpenCV library to open and work with image basics. Then we'll start to understand how to process images and apply a variety of effects, including color mappings, blending, thresholds, gradients, and more.

Then we'll move on to understanding video basics with OpenCV, including working with streaming video from a webcam.  Afterwards we'll learn about direct video topics, such as optical flow and object detection. Including face detection and object tracking.

Then we'll move on to an entire section of the course devoted to the latest deep learning topics, including image recognition and custom image classifications. We'll even cover the latest deep learning networks, including the YOLO (you only look once) deep learning network.

This course covers all this and more, including the following topics:

  • NumPy

  • Images with NumPy

  • Image and Video Basics with NumPy

  • Color Mappings

  • Blending and Pasting Images

  • Image Thresholding

  • Blurring and Smoothing

  • Morphological Operators

  • Gradients

  • Histograms

  • Streaming video with OpenCV

  • Object Detection

  • Template Matching

  • Corner, Edge, and Grid Detection

  • Contour Detection

  • Feature Matching

  • WaterShed Algorithm

  • Face Detection

  • Object Tracking

  • Optical Flow

  • Deep Learning with Keras

  • Keras and Convolutional Networks

  • Customized Deep Learning Networks

  • State of the Art YOLO Networks

  • and much more!

Feel free to message me on Udemy if you have any questions about the course!

Thanks for checking out the course page, and I hope to see you inside!

Jose

Computer Vision Masterclass

Learn in practice everything you need to know about Computer Vision! Build projects step by step using Python!

Created by Jones Granatyr - Professor

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Students: 25898, Price: $89.99

Students: 25898, Price:  Paid

Computer Vision is a subarea of Artificial Intelligence focused on creating systems that can process, analyze and identify visual data in a similar way to the human eye. There are many commercial applications in various departments, such as: security, marketing, decision making and production. Smartphones use Computer Vision to unlock devices using face recognition, self-driving cars use it to detect pedestrians and keep a safe distance from other cars, as well as security cameras use it to identify whether there are people in the environment for the alarm to be triggered.

In this course you will learn everything you need to know in order to get in this world. You will learn the step-by-step implementation of the 14 (fourteen) main computer vision techniques. If you have never heard about computer vision, at the end of this course you will have a practical overview of all areas. Below you can see some of the content you will implement:

  • Detect faces in images and videos using OpenCV and Dlib libraries

  • Learn how to train the LBPH algorithm to recognize faces, also using OpenCV and Dlib libraries

  • Track objects in videos using KCF and CSRT algorithms

  • Learn the whole theory behind artificial neural networks and implement them to classify images

  • Implement convolutional neural networks to classify images

  • Use transfer learning and fine tuning to improve the results of convolutional neural networks

  • Detect emotions in images and videos using neural networks

  • Compress images using autoencoders and TensorFlow

  • Detect objects using YOLO, one of the most powerful techniques for this task

  • Recognize gestures and actions in videos using OpenCV

  • Create hallucinogenic images using the Deep Dream technique

  • Combine style of images using style transfer

  • Create images that don't exist in the real world with GANs (Generative Adversarial Networks)

  • Extract useful information from images using image segmentation

You are going to learn the basic intuition about the algorithms and implement some project step by step using Python language and Google Colab

Deep Learning: Advanced Computer Vision (GANs, SSD, +More!)

VGG, ResNet, Inception, SSD, RetinaNet, Neural Style Transfer, GANs +More in Tensorflow, Keras, and Python

Created by Lazy Programmer Inc. - Artificial intelligence and machine learning engineer

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Students: 24841, Price: $109.99

Students: 24841, Price:  Paid

Latest update: Instead of SSD, I show you how to use RetinaNet, which is better and more modern. I show you both how to use a pretrained model and how to train one yourself with a custom dataset on Google Colab.

This is one of the most exciting courses I’ve done and it really shows how fast and how far deep learning has come over the years.

When I first started my deep learning series, I didn’t ever consider that I’d make two courses on convolutional neural networks.

I think what you’ll find is that, this course is so entirely different from the previous one, you will be impressed at just how much material we have to cover.

Let me give you a quick rundown of what this course is all about:

We’re going to bridge the gap between the basic CNN architecture you already know and love, to modern, novel architectures such as VGG, ResNet, and Inception (named after the movie which by the way, is also great!)

We’re going to apply these to images of blood cells, and create a system that is a better medical expert than either you or I. This brings up a fascinating idea: that the doctors of the future are not humans, but robots.

In this course, you’ll see how we can turn a CNN into an object detection system, that not only classifies images but can locate each object in an image and predict its label.

You can imagine that such a task is a basic prerequisite for self-driving vehicles. (It must be able to detect cars, pedestrians, bicycles, traffic lights, etc. in real-time)

We’ll be looking at a state-of-the-art algorithm called SSD which is both faster and more accurate than its predecessors.

Another very popular computer vision task that makes use of CNNs is called neural style transfer.

This is where you take one image called the content image, and another image called the style image, and you combine these to make an entirely new image, that is as if you hired a painter to paint the content of the first image with the style of the other. Unlike a human painter, this can be done in a matter of seconds.

I will also introduce you to the now-famous GAN architecture (Generative Adversarial Networks), where you will learn some of the technology behind how neural networks are used to generate state-of-the-art, photo-realistic images.

Currently, we also implement object localization, which is an essential first step toward implementing a full object detection system.

I hope you’re excited to learn about these advanced applications of CNNs, I’ll see you in class!

AWESOME FACTS:

  • One of the major themes of this course is that we’re moving away from the CNN itself, to systems involving CNNs.

  • Instead of focusing on the detailed inner workings of CNNs (which we've already done), we'll focus on high-level building blocks. The result? Almost zero math.

  • Another result? No complicated low-level code such as that written in TensorflowTheano, or PyTorch (although some optional exercises may contain them for the very advanced students). Most of the course will be in Keras which means a lot of the tedious, repetitive stuff is written for you.

"If you can't implement it, you don't understand it"

  • Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".

  • My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

  • Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

  • After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...

Suggested Prerequisites:

  • Know how to build, train, and use a CNN using some library (preferably in Python)

  • Understand basic theoretical concepts behind convolution and neural networks

  • Decent Python coding skills, preferably in data science and the Numpy Stack

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

  • Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)

Deep Learning :Adv. Computer Vision (object detection+more!)

Transfer Learning, TensorFlow Object detection, Classification, Yolo object detection, real time projects much more..!!

Created by Jay Bhatt - Data Scientist by Profession Instructor by Passion

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Students: 23794, Price: $19.99

Students: 23794, Price:  Paid

Latest update: I will show you both how to use a pretrained model and how to train one yourself with a custom dataset on Google Colab.

This course is a complete guide for setting up TensorFlow object detection api, Transfer learning and a lot more

I think what you’ll find is that, this course is so entirely different from the previous one, you will be impressed at just how much material we have to cover.

Here is the details about the project.

Here we will star from colab understating because that will help to use free GPU provided by google to train up our model.

We’re going to bridge the gap between the basic CNN architecture you already know and love, to modern, novel architectures such as ResNet, and Inception.

We will understand object detection modules in detail using both tensorflow object detection api as well as YOLO algorithms.

We’ll be looking at a state-of-the-art algorithm called RESNET and MobileNetV2 which is both faster and more accurate than its predecessors.

One best thing is you will understand the core basics of CNN and how it converts to object detection slowly.

I hope you’re excited to learn about these advanced applications of CNNs Yolo and Tensorflow, I’ll see you in class!

AMAGING FACTS:

· This course give’s you full hand’s on experience of training models in colab GPU.

· Instead of focusing on the detailed inner workings of CNNs (which we've already done), we'll focus on high-level building blocks. The result? Almost zero math.

· Another result? No complicated low-level code such as that written in Tensorflow, Theano,YOLO, or PyTorch (although some optional exercises may contain them for the very advanced students). Most of the course will be in Keras which means a lot of the tedious, repetitive stuff is written for you.

Suggested Prerequisites:

· Know how to build, train, and use a CNN using some library (preferably in Python)

· Understand basic theoretical concepts behind convolution and neural networks

· Decent Python coding skills, preferably in data science and the Numpy Stack

Who this course is for:

· Students and professionals who want to take their knowledge of computer vision and deep learning to the next level

· Anyone who wants to learn about object detection algorithms like SSD and YOLO

· Anyone who wants to learn how to write code for neural style transfer

· Anyone who wants to use transfer learning

· Anyone who wants to shorten training time and build state-of-the-art computer vision nets fast

· Anyone who is starting with computer vison

Master Computer Vision™ OpenCV4 in Python with Deep Learning

Master OpenCV4 like a pro while learning Dlib, Deep Learning Computer Vision (Keras, TensorFlow & Caffe) + 21 Projects!

Created by Rajeev D. Ratan - Data Scientist, Computer Vision Expert & Electrical Engineer

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Students: 19933, Price: $89.99

Students: 19933, Price:  Paid

Welcome to one of the most thorough and well-taught courses on OpenCV, where you'll learn how to Master Computer Vision using the newest version of OpenCV4 in Python!

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NOTE: Many of the earlier poor reviews was during a period of time when the course material was outdated and many of the example code was broken, however, this has been fixed as of early 2019 :)

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Computer Vision is an area of Artificial Intelligence that deals with how computer algorithms can decipher what they see in images! Master this incredible skill and be able to complete your University/College Projects, automate something at work, start developing your startup idea or gain the skills to become a high paying ($400-$1000 USD/Day) Computer Vision Engineer.

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Last Updated Aug 2019, you will be learning:

  1. Key concepts of Computer Vision & OpenCV (using the newest version OpenCV4)

  2. Image manipulations (dozens of techniques!) such as transformations, cropping, blurring, thresholding, edge detection and cropping.

  3. Segmentation of images by understanding contours, circle, and line detection. You'll even learn how to approximate contours, do contour filtering and ordering as well as approximations.

  4. Feature detection (SIFT, SURF, FAST, BRIEF & ORB) to do object detection.

  5. Object Detection for faces, people & cars.

  6. Extract facial landmarks for face analysis, applying filters, and face swaps.

  7. Machine Learning in Computer Vision for handwritten digit recognition.

  8. Facial Recognition.

  9. Motion Analysis & Object Tracking.

  10. Computational photography techniques for Photo Restoration (eliminate marks, lines, creases, and smudges from old damaged photos).

  11. Deep Learning ( 3+ hours of Deep Learning with Keras in Python)

  12. Computer Vision Product and Startup Ideas

  13. Multi-Object Detection (90 Object Types)

  14. Colorize Black & White Photos and Video (using Caffe)

  15. Neural Style Transfers - Apply the artistic style of Van Gogh, Picasso, and others to any image even your webcam input

  16. Automatic Number-Plate Recognition (ALPR

  17. Credit Card Number Identification (Build your own OCR Classifier with PyTesseract)

======================================================

You'll also be implementing 21 awesome projects! 

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OpenCV Projects Include:

  1. Live Drawing Sketch using your webcam

  2. Identifying Shapes

  3. Counting Circles and Ellipses

  4. Finding Waldo

  5. Single Object Detectors using OpenCV

  6. Car and Pedestrian Detector using Cascade Classifiers

  7. Live Face Swapper (like MSQRD & Snapchat filters!!!)

  8. Yawn Detector and Counter

  9. Handwritten Digit Classification

  10. Facial Recognition

  11. Ball Tracking

  12. Photo-Restoration

  13. Automatic Number-Plate Recognition (ALPR)

  14. Neural Style Transfer Mini Project

  15. Multi-Object Detection in OpenCV (up to 90 Objects!) using SSD (Single Shot Detector)

  16. Colorize Black & White Photos and Video

Deep Learning Projects Include:

  1. Build a Handwritten Digit Classifier

  2. Build a Multi-Image Classifier

  3. Build a Cats vs Dogs Classifier

  4. Understand how to boost CNN performance using Data Augmentation

  5. Extract and Classify Credit Card Numbers

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What previous students have said: 

"I'm amazed at the possibilities. Very educational, learning more than what I ever thought was possible. Now, being able to actually use it in a practical purpose is intriguing... much more to learn & apply"

"Extremely well taught and informative Computer Vision course! I've trawled the web looking for Opencv python tutorials resources but this course was by far the best amalgamation of relevant lessons and projects. Loved some of the projects and had lots of fun tinkering them."

"Awesome instructor and course. The explanations are really easy to understand and the materials are very easy to follow. Definitely a really good introduction to image processing."

"I am extremely impressed by this course!! I think this is by far the best Computer Vision course on Udemy. I'm a college student who had previously taken a Computer Vision course in undergrad. This 6.5 hour course blows away my college class by miles!!"

"Rajeev did a great job on this course. I had no idea how computer vision worked and now have a good foundation of concepts and knowledge of practical applications. Rajeev is clear and concise which helps make a complicated subject easy to comprehend for anyone wanting to start building applications."

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Why Learn Computer Vision in Python using OpenCV?

Computer vision applications and technology are exploding right now! With several apps and industries making amazing use of the technology, from billion-dollar apps such as Pokémon GO, Snapchat and up and coming apps like MSQRD and PRISMA.

Even Facebook, Google, Microsoft, Apple, Amazon, and Tesla are all heavily utilizing computer vision for face & object recognition, image searching and especially in Self-Driving Cars!

As a result, the demand for computer vision expertise is growing exponentially!

However, learning computer vision is hard! Existing online tutorials, textbooks, and free MOOCs are often outdated, using older incompatible libraries or are too theoretical, making it difficult to understand. 

This was my problem when learning Computer Vision and it became incredibly frustrating. Even simply running example code I found online proved difficult as libraries and functions were often outdated.

I created this course to teach you all the key concepts without the heavy mathematical theory while using the most up to date methods. 

I take a very practical approach, using more than 50 Code Examples.

At the end of the course, you will be able to build 12 Awesome Computer Vision Apps using OpenCV in Python.

I use OpenCV which is the most well supported open-source computer vision library that exists today! Using it in Python is just fantastic as Python allows us to focus on the problem at hand without being bogged down by complex code.

If you're an academic or college student I still point you in the right direction if you wish to learn more by linking the research papers of techniques we use. 

So if you want to get an excellent foundation in Computer Vision, look no further.

This is the course for you!

In this course, you will discover the power of OpenCV in Python, and obtain skills to dramatically increase your career prospects as a Computer Vision developer.

You get 3+ Hours of Deep Learning in Computer Vision using Keras, which includes:

  • A free Virtual Machine with all Deep Learning Python Libraries such as Keras and TensorFlow pre-installed

  • Detailed Explanations on Neural Networks and Convolutional Neural Networks

  • Understand how Keras works and how to use and create image datasets

  • Build a Handwritten Digit Classifier

  • Build a Multi-Image Classifier

  • Build a Cats vs Dogs Classifier

  • Understand how to boost CNN performance using Data Augmentation

  • Extract and Classify Credit Card Numbers

As for Updates and support:

I will be continuously adding updates, fixes, and new amazing projects every month! 

I will be active daily in the 'questions and answers' area of the course, so you are never on your own.    

So, are you ready to get started? Enroll now and start the process of becoming a master in Computer Vision today!

Deploy Computer Vision Flask Web App using Python in CLOUD

Develop and Deploy Machine Learning Web App and Deploy in Python Anywhere Cloud Platform using Python, Flask, Skimage

Created by Data Science Anywhere - Team of Engineers

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Students: 18841, Price: $89.99

Students: 18841, Price:  Paid

Welcome to Deploy End to End Machine Learning-based Image Classification Web App in Cloud Platform from scratch

Image Processing & classification is one of the areas of Data Science and has a wide variety of applications in the industries in the current world. Many industries looking for a Data Scientist with these skills. This course covers modeling techniques for data preprocessing, model building, evaluation, tuning, and production

We start the course by learning Scikit Image for image processing which is the essential skill required and then we will do the necessary preprocessing techniques & feature extraction to an image like HOG.

After that we will start building the project. In this course you will learn how to label the images, image data preprocessing and analysis using scikit image and python.

Then we will train machine learning here we will see Stochastic Gradient Descenct Classifier for image classification and followed by model evaluation proces and pipeline the machine learning model.

After that we will create web app in Flask by rendering HTML, CSS, Boostrap. Then, we finally deploy web app in Python Anywhere which is cloud platform.

WHAT YOU LEARN ?

  • Python

  • Scikit Image

  • Data Preprocessing

  • HOG

  • Base Estimator and TransformerMixIn

  • SGD Classifier

  • Create and Make Pipeline Model

  • Hyperparameter Tuning

  • Flask

  • HTTP methods

  • Deploy in PythonAnywhere

We know that the Image Classification Flask Web App is one of those topics that always leaves some doubts. Feel free to ask question in Q&A, we are happy to answer you question.

I am super excited and see you in the course !!!

Computer Vision In Python! Face Detection & Image Processing

Learn Computer Vision With OpenCV In Python! Master Python By Implementing Face Recognition & Image Processing In Python

Created by Emenwa Global - Senior Developers

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Students: 16663, Price: $89.99

Students: 16663, Price:  Paid

Computer vision is an interdisciplinary field that deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do. Computer vision is concerned with the automatic extraction, analysis and understanding of useful information from a single image or a sequence of images. It involves the development of a theoretical and algorithmic basis to achieve automatic visual understanding. As a scientific discipline, computer vision is concerned with the theory behind artificial systems that extract information from images. The image data can take many forms, such as video sequences, views from multiple cameras, or multi-dimensional data from a medical scanner. As a technological discipline, computer vision seeks to apply its theories and models for the construction of computer vision systems.

Distinctions

The fields most closely related to computer vision are image processing, image analysis and machine vision. There is a significant overlap in the range of techniques and applications that these cover. This implies that the basic techniques that are used and developed in these fields are similar, something which can be interpreted as there is only one field with different names. On the other hand, it appears to be necessary for research groups, scientific journals, conferences and companies to present or market themselves as belonging specifically to one of these fields and, hence, various characterizations which distinguish each of the fields from the others have been presented.

Computer graphics produces image data from 3D models, computer vision often produces 3D models from image data. There is also a trend towards a combination of the two disciplines, e.g., as explored in augmented reality.

The following characterizations appear relevant but should not be taken as universally accepted:

  • Image processing and image analysis tend to focus on 2D images, how to transform one image to another, e.g., by pixel-wise operations such as contrast enhancement, local operations such as edge extraction or noise removal, or geometrical transformations such as rotating the image. This characterization implies that image processing/analysis neither require assumptions nor produce interpretations about the image content.

  • Computer vision includes 3D analysis from 2D images. This analyzes the 3D scene projected onto one or several images, e.g., how to reconstruct structure or other information about the 3D scene from one or several images. Computer vision often relies on more or less complex assumptions about the scene depicted in an image.

  • Machine vision is the process of applying a range of technologies & methods to provide imaging-based automatic inspection, process control and robot guidance in industrial applications. Machine vision tends to focus on applications, mainly in manufacturing, e.g., vision-based robots and systems for vision-based inspection, measurement, or picking (such as bin picking). This implies that image sensor technologies and control theory often are integrated with the processing of image data to control a robot and that real-time processing is emphasized by means of efficient implementations in hardware and software. It also implies that the external conditions such as lighting can be and are often more controlled in machine vision than they are in general computer vision, which can enable the use of different algorithms.

  • There is also a field called imaging which primarily focuses on the process of producing images, but sometimes also deals with processing and analysis of images. For example, medical imaging includes substantial work on the analysis of image data in medical applications.

  • Finally, pattern recognition is a field which uses various methods to extract information from signals in general, mainly based on statistical approaches and artificial neural networks. A significant part of this field is devoted to applying these methods to image data.

Applications

Applications range from tasks such as industrial machine vision systems which, say, inspect bottles speeding by on a production line, to research into artificial intelligence and computers or robots that can comprehend the world around them. The computer vision and machine vision fields have significant overlap. Computer vision covers the core technology of automated image analysis which is used in many fields. Machine vision usually refers to a process of combining automated image analysis with other methods and technologies to provide automated inspection and robot guidance in industrial applications. In many computer-vision applications, the computers are pre-programmed to solve a particular task, but methods based on learning are now becoming increasingly common. Examples of applications of computer vision include systems for:

  • Automatic inspection, e.g., in manufacturing applications;

  • Assisting humans in identification tasks, e.g., a species identification system

  • Controlling processes, e.g., an industrial robot;

  • Detecting events, e.g., for visual surveillance or people counting, e.g., in the restaurant industry;

  • Interaction, e.g., as the input to a device for computer-human interaction;

  • Modeling objects or environments, e.g., medical image analysis or topographical modeling;

  • Navigation, e.g., by an autonomous vehicle or mobile robot; and

  • Organizing information, e.g., for indexing databases of images and image sequences.

Medicine

One of the most prominent application fields is medical computer vision, or medical image processing, characterized by the extraction of information from image data to diagnose a patient. An example of this is detection of tumors, arteriosclerosis or other malign changes; measurements of organ dimensions, blood flow, etc. are another example. It also supports medical research by providing new information: e.g., about the structure of the brain, or about the quality of medical treatments. Applications of computer vision in the medical area also includes enhancement of images interpreted by humans—ultrasonic images or X-ray images for example—to reduce the influence of noise.

Machine Vision

A second application area in computer vision is in industry, sometimes called machine vision, where information is extracted for the purpose of supporting a manufacturing process. One example is quality control where details or final products are being automatically inspected in order to find defects. Another example is measurement of position and orientation of details to be picked up by a robot arm. Machine vision is also heavily used in agricultural process to remove undesirable food stuff from bulk material, a process called optical sorting.

Military

Military applications are probably one of the largest areas for computer vision. The obvious examples are detection of enemy soldiers or vehicles and missile guidance. More advanced systems for missile guidance send the missile to an area rather than a specific target, and target selection is made when the missile reaches the area based on locally acquired image data. Modern military concepts, such as "battlefield awareness", imply that various sensors, including image sensors, provide a rich set of information about a combat scene which can be used to support strategic decisions. In this case, automatic processing of the data is used to reduce complexity and to fuse information from multiple sensors to increase reliability.

Autonomous vehicles

One of the newer application areas is autonomous vehicles, which include submersibles, land-based vehicles (small robots with wheels, cars or trucks), aerial vehicles, and unmanned aerial vehicles (UAV). The level of autonomy ranges from fully autonomous (unmanned) vehicles to vehicles where computer-vision-based systems support a driver or a pilot in various situations. Fully autonomous vehicles typically use computer vision for navigation, e.g. for knowing where it is, or for producing a map of its environment (SLAM) and for detecting obstacles. It can also be used for detecting certain task specific events, e.g., a UAV looking for forest fires. Examples of supporting systems are obstacle warning systems in cars, and systems for autonomous landing of aircraft. Several car manufacturers have demonstrated systems for autonomous driving of cars, but this technology has still not reached a level where it can be put on the market. There are ample examples of military autonomous vehicles ranging from advanced missiles to UAVs for recon missions or missile guidance. Space exploration is already being made with autonomous vehicles using computer vision, e.g., NASA's Curiosity and CNSA's Yutu-2 rover.

Tactile Feedback

Materials such as rubber and silicon are being used to create sensors that allow for applications such as detecting micro undulations and calibrating robotic hands. Rubber can be used in order to create a mold that can be placed over a finger, inside of this mold would be multiple strain gauges. The finger mold and sensors could then be placed on top of a small sheet of rubber containing an array of rubber pins. A user can then wear the finger mold and trace a surface. A computer can then read the data from the strain gauges and measure if one or more of the pins is being pushed upward. If a pin is being pushed upward then the computer can recognize this as an imperfection in the surface. This sort of technology is useful in order to receive accurate data of the imperfections on a very large surface. Another variation of this finger mold sensor are sensors that contain a camera suspended in silicon. The silicon forms a dome around the outside of the camera and embedded in the silicon are point markers that are equally spaced. These cameras can then be placed on devices such as robotic hands in order to allow the computer to receive highly accurate tactile data.

Other application areas include:

  • Support of visual effects creation for cinema and broadcast, e.g., camera tracking (matchmoving).

  • Surveillance.

  • Driver drowsiness detection

  • Tracking and counting organisms in the biological sciences

[Reference: Wikipedia]

Deep Learning Computer Vision™ CNN, OpenCV, YOLO, SSD & GANs

2020 Update with TensorFlow 2.0 Support. Become a Pro at Deep Learning Computer Vision! Includes 20+ Real World Projects

Created by Rajeev D. Ratan - Data Scientist, Computer Vision Expert & Electrical Engineer

"]

Students: 11651, Price: $109.99

Students: 11651, Price:  Paid

Update: June-2020

  • TensorFlow 2.0 Compatible Code

  • Windows install guide for TensorFlow2.0 (with Keras), OpenCV4 and Dlib

Deep Learning Computer Vision™ Use Python & Keras to implement CNNs, YOLO, TFOD, R-CNNs, SSDs & GANs + A Free Introduction to OpenCV.

If you want to learn all the latest 2019 concepts in applying Deep Learning to Computer Vision, look no further - this is the course for you! You'll get hands  the following Deep Learning frameworks in Python:

  • Keras

  • Tensorflow 2.0

  • TensorFlow Object Detection API

  • YOLO (DarkNet and DarkFlow)

  • OpenCV4

All in an easy to use virtual machine, with all libraries pre-installed!

======================================================

Apr 2019 Updates:

  • How to set up a Cloud GPU on PaperSpace and Train a CIFAR10 AlexNet CNN almost 100 times faster!

  • Build a Computer Vision API and Web App and host it on AWS using an EC2 Instance!

Mar 2019 Updates:

Newly added Facial Recognition & Credit Card Number Reader Projects

  • Recognize multiple persons using your webcam

  • Facial Recognition on the Friends TV Show Characters

  • Take a picture of a Credit Card, extract and identify the numbers on that card!

======================================================

Computer vision applications involving Deep Learning are booming!

Having Machines that can 'see' will change our world and revolutionize almost every industry out there. Machines or robots that can see will be able to:

  • Perform surgery and accurately analyze and diagnose you from medical scans.

  • Enable self-driving cars

  • Radically change robots allowing us to build robots that can cook, clean and assist us with almost any task

  • Understand what's being seen in CCTV surveillance videos thus performing security, traffic management and a host of other services

  • Create Art with amazing Neural Style Transfers and other innovative types of image generation

  • Simulate many tasks such as Aging faces, modifying live video feeds and realistically replace actors in films

Huge technology companies such as Facebook, Google, Microsoft, Apple, Amazon, and Tesla are all heavily devoting billions to computer vision research.

As a result, the demand for computer vision expertise is growing exponentially!

However, learning computer vision with Deep Learning is hard!

  • Tutorials are too technical and theoretical

  • Code is outdated

  • Beginners just don't know where to start

That's why I made this course!

  • I  spent months developing a proper and complete learning path.

  • I teach all key concepts logically and without overloading you with mathematical theory while using the most up to date methods. 

  • I created a FREE Virtual Machine with all Deep Learning Libraries (Keras, TensorFlow, OpenCV, TFODI, YOLO, Darkflow etc) installed! This will save you hours of painfully complicated installs

  • I teach using practical examples and you'll learn by doing 18 projects!

Projects such as:

  1. Handwritten Digit Classification using MNIST

  2. Image Classification using CIFAR10

  3. Dogs vs Cats classifier

  4. Flower Classifier using Flowers-17

  5. Fashion Classifier using FNIST

  6. Monkey Breed Classifier

  7. Fruit Classifier

  8. Simpsons Character Classifier

  9. Using Pre-trained ImageNet Models to classify a 1000 object classes

  10. Age, Gender and Emotion Classification

  11. Finding the Nuclei in Medical Scans using U-Net

  12. Object Detection using a ResNet50 SSD Model built using TensorFlow Object Detection

  13. Object Detection with YOLO V3

  14. A Custom YOLO Object Detector that Detects London Underground Tube Signs

  15. DeepDream

  16. Neural Style Transfers

  17. GANs - Generate Fake Digits

  18. GANs - Age Faces up to 60+ using Age-cGAN

  19. Face Recognition

  20. Credit Card Digit Reader

  21. Using Cloud GPUs on PaperSpace

  22. Build a Computer Vision API and Web App and host it on AWS using an EC2 Instance!

And OpenCV Projects such as:

  1. Live Sketch

  2. Identifying Shapes

  3. Counting Circles and Ellipses

  4. Finding Waldo

  5. Single Object Detectors using OpenCV

  6. Car and Pedestrian Detector using Cascade Classifiers

So if you want to get an excellent foundation in Computer Vision, look no further.

This is the course for you!

In this course, you will discover the power of Computer Vision in Python, and obtain skills to dramatically increase your career prospects as a Computer Vision developer.

======================================================

As for Updates and support:

I will be active daily in the 'questions and answers' area of the course, so you are never on your own.    

So, are you ready to get started? Enroll now and start the process of becoming a Master in Computer Vision using Deep Learning today!

======================================================

What previous students have said my other Udemy Course: 

"I'm amazed at the possibilities. Very educational, learning more than what I ever thought was possible. Now, being able to actually use it in a practical purpose is intriguing... much more to learn & apply"

"Extremely well taught and informative Computer Vision course! I've trawled the web looking for OpenCV python tutorials resources but this course was by far the best amalgamation of relevant lessons and projects. Loved some of the projects and had lots of fun tinkering them."

"Awesome instructor and course. The explanations are really easy to understand and the materials are very easy to follow. Definitely a really good introduction to image processing."

"I am extremely impressed by this course!! I think this is by far the best Computer Vision course on Udemy. I'm a college student who had previously taken a Computer Vision course in undergrad. This 6.5 hour course blows away my college class by miles!!"

"Rajeev did a great job on this course. I had no idea how computer vision worked and now have a good foundation of concepts and knowledge of practical applications. Rajeev is clear and concise which helps make a complicated subject easy to comprehend for anyone wanting to start building applications."

======================================================

PyTorch for Deep Learning and Computer Vision

Build Highly Sophisticated Deep Learning and Computer Vision Applications with PyTorch

Created by Rayan Slim - Teacher

"]

Students: 8916, Price: $99.99

Students: 8916, Price:  Paid

PyTorch has rapidly become one of the most transformative frameworks in the field of Deep Learning. Since its release, PyTorch has completely changed the landscape in the field of deep learning due to its flexibility, and how easy it is to use when building Deep Learning models.

Deep Learning jobs command some of the highest salaries in the development world. This course is meant to take you from the complete basics, to building state-of-the art Deep Learning and Computer Vision applications with PyTorch.

Learn & Master Deep Learning with PyTorch in this fun and exciting course with top instructor Rayan Slim. With over 44000 students, Rayan is a highly rated and experienced instructor who has followed a "learn by doing" style to create this amazing course.

You'll go from beginner to Deep Learning expert and your instructor will complete each task with you step by step on screen.

By the end of the course, you will have built state-of-the art Deep Learning and Computer Vision applications with PyTorch. The projects built in this course will impress even the most senior developers and ensure you have hands on skills that you can bring to any project or company.

This course will show you to:

  • Learn how to work with the tensor data structure

  • Implement Machine and Deep Learning applications with PyTorch

  • Build neural networks from scratch

  • Build complex models through the applied theme of advanced imagery and Computer Vision

  • Learn to solve complex problems in Computer Vision by harnessing highly sophisticated pre-trained models

  • Use style transfer to build sophisticated AI applications that are able to seamlessly recompose images in the style of other images.

No experience required. This course is designed to take students with no programming/mathematics experience to accomplished Deep Learning developers.

This course also comes with all the source code and friendly support in the Q&A area.

Who this course is for:

  • Anyone with an interest in Deep Learning and Computer Vision

  • Anyone (no matter the skill level) who wants to transition into the field of Artificial Intelligence

  • Entrepreneurs with an interest in working on some of the most cutting edge technologies

  • All skill levels are welcome!

Autonomous Cars: Deep Learning and Computer Vision in Python

Learn OpenCV, Keras, object and lane detection, and traffic sign classification for self-driving cars

Created by Sundog Education by Frank Kane - Founder, Sundog Education. Machine Learning Pro

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Students: 7554, Price: $89.99

Students: 7554, Price:  Paid

Autonomous Cars: Computer Vision and Deep Learning

The automotive industry is experiencing a paradigm shift from conventional, human-driven vehicles into self-driving, artificial intelligence-powered vehicles. Self-driving vehicles offer a safe, efficient, and cost effective solution that will dramatically redefine the future of human mobility. Self-driving cars are expected to save over half a million lives and generate enormous economic opportunities in excess of $1 trillion dollars by 2035. The automotive industry is on a billion-dollar quest to deploy the most technologically advanced vehicles on the road.

As the world advances towards a driverless future, the need for experienced engineers and researchers in this emerging new field has never been more crucial.

The purpose of this course is to provide students with knowledge of key aspects of design and development of self-driving vehicles. The course provides students with practical experience in various self-driving vehicles concepts such as machine learning and computer vision. Concepts such as lane detection, traffic sign classification, vehicle/object detection, artificial intelligence, and deep learning will be presented. The course is targeted towards students wanting to gain a fundamental understanding of self-driving vehicles control. Basic knowledge of programming is recommended. However, these topics will be extensively covered during early course lectures; therefore, the course has no prerequisites, and is open to any student with basic programming knowledge. Students who enroll in this self-driving car course will master driverless car technologies that are going to reshape the future of transportation.

Tools and algorithms we'll cover include:

  • OpenCV

  • Deep Learning and Artificial Neural Networks

  • Convolutional Neural Networks

  • Template matching

  • HOG feature extraction

  • SIFT, SURF, FAST, and ORB

  • Tensorflow and Keras

  • Linear regression and logistic regression

  • Decision Trees

  • Support Vector Machines

  • Naive Bayes

Your instructors are Dr. Ryan Ahmed with a PhD in engineering focusing on electric vehicle control systems, and Frank Kane, who spent 9 years at Amazon specializing in machine learning. Together, Frank and Dr. Ahmed have taught over 200,000 students around the world on Udemy alone.

Students of our popular course, "Data Science, Deep Learning, and Machine Learning with Python" may find some of the topics to be a review of what was covered there, seen through the lens of self-driving cars. But, most of the course focuses on topics we've never covered before, specific to computer vision techniques used in autonomous vehicles. There are plenty of new, valuable skills to be learned here!

OpenCV Python For Beginners | Hands on Computer Vision

Hands on Practical Examples with OpenCV & Python

Created by Yogesh Patel - Software Developer and Programming Enthusiast

"]

Students: 6367, Price: $89.99

Students: 6367, Price:  Paid

Welcome to this on OpenCV Python Tutorial For Beginners. OpenCV is an image processing library created by Intel and later supported by Willow Garage and now maintained by Itseez. opencv is available on Mac, Windows, Linux. Works in C, C++, and Python. it is Open Source and free. opencv is easy to use and install.

The goal of this course is to provide you with a working knowledge of OpenCV with Python. We'll start with the basics, starting from installing OpenCV with Python on Windows, Mac, and Ubuntu. Then we will see how to create your first OpenCV python script. Then we will dive deep into the amazing world of computer vision Using OpenCV and learn the most important concepts about computer vision using OpenCV .

So Let's get started !!!

Computer Vision: Face Recognition Quick Starter in Python

Quickly Build Python Deep Learning based Face Detection, Recognition, Emotion , Gender and Age Classification Systems

Created by Abhilash Nelson - Computer Engineering Master & Senior Programmer at Dubai

"]

Students: 3071, Price: $109.99

Students: 3071, Price:  Paid

Hi There!

welcome to my new course 'Face Recognition with Deep Learning using Python'. This is the second course from my Computer Vision series.

Face Detection and Face Recognition is the most used applications of Computer Vision. Using these techniques, the computer will be able to extract one or more faces in an image or video and then compare it with the existing data to identify the people in that image.

Face Detection and Face Recognition is widely used by governments and organizations for surveillance and policing. We are also making use of it daily in many applications like face unlocking of cell phones etc.

This course will be a quick starter for people who wants to dive deep into face recognition using Python without having to deal with all the complexities and mathematics associated with typical Deep Learning process.

We will be using a python library called face-recognition which uses simple classes and methods to get the face recognition implemented with ease. We are also using OpenCV, Dlib and Pillow for python as supporting libraries.

Let's now see the list of interesting topics that are included in this course.

At first we will have an introductory theory session about Face Detection and Face Recognition technology.

After that, we are ready to proceed with preparing our computer for python coding by downloading and installing the anaconda package. Then we will install the rest of dependencies and libraries that we require including the dlib, face-recognition, opencv etc and will try a small program to see if everything is installed fine.

Most of you may not be coming from a python based programming background. The next few sessions and examples will help you get the basic python programming skill to proceed with the sessions included in this course. The topics include Python assignment, flow-control, functions and data structures.

Then we will have an introduction to the basics and working of face detectors which will detect human faces from a given media. We will try the python code to detect the faces from a given image and will extract the faces as separate images.

Then we will go ahead with face detection from a video. We will be streaming the real-time live video from the computer's webcam and will try to detect faces from it. We will draw rectangle around each face detected in the live video.

In the next session, we will customize the face detection program to blur the detected faces dynamically from the webcam video stream.

After that we will try facial expression recognition using pre-trained deep learning model and will identify the facial emotions from the real-time webcam video as well as static images

And then we will try Age and Gender Prediction using pre-trained deep learning model and will identify the  Age and Gender from the real-time webcam video as well as static images

After face detection, we will have an introduction to the basics and working of face recognition which will identify the faces already detected.

In the next session, We will try the python code to identify the names of people and their the faces from a given image and will draw a rectangle around the face with their names on it.

Then, like as we did in face detection we will go ahead with face recognition from a video. We will be streaming the real-time live video from the computer's webcam and will try to identify and name the faces in it. We will draw rectangle around each face detected and beneath that their names in the live video.

Most times during coding, along with the face matching decision, we may need to know how much matching the face is. For that we will get a parameter called face distance which is the magnitude of matching of two faces. We will later convert this face distance value to face matching percentage using simple mathematics.

In the coming two sessions, we will learn how to tweak the face landmark points used for face detection. We will draw line joining these face land mark points so that we can visualize the points in the face which the computer is used for evaluation.

Taking the landmark points customization to the next level, we will use the landmark points to create a custom face make-up for the face image.

That's all about the topics which are currently included in this quick course. The code, images and libraries used in this course has been uploaded and shared in a folder. I will include the link to download them in the last session or the resource section of this course. You are free to use the code in your projects with no questions asked.

Also after completing this course, you will be provided with a course completion certificate which will add value to your portfolio.

So that's all for now, see you soon in the class room. Happy learning and have a great time.

Computer Vision: YOLO Custom Object Detection with Colab GPU

YOLO: Pre-Trained Coco Dataset and Custom Trained Coronavirus Object Detection Model with Google Colab GPU Training

Created by Abhilash Nelson - Computer Engineering Master & Senior Programmer at Dubai

"]

Students: 2981, Price: $99.99

Students: 2981, Price:  Paid

Hi There!

welcome to my new course 'YOLO Custom Object Detection Quick Starter with Python'. This is the fourth course from my Computer Vision series.

As you know Object Detection is the most used applications of Computer Vision, in which the computer will be able to recognize and classify objects inside an image.

We will be specifically focusing on (YOLO), You only look once which is an effective real-time object recognition algorithm which is featured in Darknet, an open source neural network framework

This course is equally divided into two halves. The first half will deal with object recognition using a predefined dataset called the coco dataset which can classify 80 classes of objects. And the second half we will try to create our own custom dataset and train the YOLO model. We will try to create our own coronavirus detection model.

Let's now see the list of interesting topics that are included in this course.

At first we will have an introductory theory session about YOLO Object Detection system.

After that, we are ready to proceed with preparing our computer for python coding by downloading and installing the anaconda package and will check and see if everything is installed fine.

Most of you may not be coming from a python based programming background. The next few sessions and examples will help you get the basic python programming skill to proceed with the sessions included in this course. The topics include Python assignment, flow-control, functions and data structures.

Then we will install install OpenCV, which is the Open Source Computer Vision library in Python.

Then we will have an introduction to Convolutional Neural Networks , its working and the different steps involved.

Now we will proceed with the part 1 that involves Object Detection and Recognition using YOLO pre-trained model. we will have an overview about the yolo model in the next session and then we will implement yolo object detection from a single image.

Often YOLO gives back more than one successful detection for a single object in an image. This can be fixed using

a technique called as NMS or Non Maxima Suppression. We will implement that in our next session.

And using that as the base, we will try the yolo model for object detection from a real time webcam video and we will check the performance. Later we will use it for object recognition from the pre-saved video file.

Then we will proceed with part 2 of the course in which we will attempt to train a darknet YOLO model. A model which can detect coronavirus from an electron microscope image or video output.

Before we proceed with the implementation, we will discuss the pros and cons of using a pre-trained dataset model and a custom dataset trained model. Also about the free GPU offered by google colab and its features.

In the next session we will start with phase 1 of our custom model in which we will do the preparation steps to implement custom model. We will at first download the darknet source from github and prepare it. We will then download the weight files required for both testing and training. And then we will edit the required configurations files to make it ready for our custom coronavirus detector.

In the second phase for our custom model, we will start collecting the required data to train the model. We will collect coronavirus images from the internet as much as we could and organize them into folder. Then we will label or annotate the coronavirus object inside these images using an opensource annotation tool called labelImg. Then we will split the gathered dataset, 80% for training and 20% for testing. And finally will edit the prepare the files with the location of training and testing datasets.

Now that we have all our files ready, in our third  phase, we will zip and upload them into google drive. After that we will create a google colab notebook and configure the colab runtime to use the fast, powerful, yet free GPU service provided by google. Then we will mount our google drive to our colab runtime and unzip the darknet zip we uploaded.

Sometimes files edited in non unix environments may be having problems when compiling the darknet. We have to convert the encoding from dos to unix as our next step. Then we will complile the darknet framework source code and proceed with testing the darknet framework with a sample image in our fourth phase.

The free GPU based runtime provided by google colab is volatile. It will get reset every 12 hours. So we need to save our weights periodically during training to our google drive which is a permanent storage. So in our phase five, we will link a backup folder in google drive to the colab runtime.

Finally in our phase 6, we are ready to proceed with training our custom coronavirus model. We will keep on monitoring the loss for every iteration or epoch as we call it in nerual network terms. Our model will automatically save the weights every 100th epoch securely to our google drive backup folder.

We can see a continues decrease in the loss values as we go through the epoch. And after many number of iterations, our model will come into a convergence or flatline state in which there is no further improvement in loss. at that time we will obtain a final weight

Later we will use that weight to do prediction for an image that contains coronavirus in it. We can see that our model clearly detects objects. We will even try this with a video file also.

We cannot claim that its a fully fledged flawless production ready coronavirus detection model. There is still room for improvement. But anyway, by building this custom model, we came all the way through the steps and process of making a custom yolo model which will be a great and valuable experience for you.

And then later in a quick session, we will also discuss few other case studies in which we can implement a custom trained YOLO model, the changes we may  need to make for training those models etc.

That's all about the topics which are currently included in this quick course. The code, images and weights used in this course has been uploaded and shared in a folder. I will include the link to download them in the last session or the resource section of this course. You are free to use the code in your projects with no questions asked.

Also after completing this course, you will be provided with a course completion certificate which will add value to your portfolio.

So that's all for now, see you soon in the class room. Happy learning and have a great time.

Deep Learning for Computer Vision with TensorFlow

ConvNets, VGG-16, ResNet, Inception, Faster R-CNN, TensorFlow Object Detection, YOLO v2-v3-v4. Train your own data.

Created by CARLOS QUIROS - Industrial Engineer and Data Scientist

"]

Students: 2839, Price: $89.99

Students: 2839, Price:  Paid

This course is focused in the application of Deep Learning for image classification and object detection. This course includes a review of the main lbraries for Deep Learning such as Tensor Flow 1.X (not 2.x) and Keras, the combined application of them with OpenCV and also covers a concise review of the main concepts in Deep Learning. We will also enter in the study of Convolutional Neural Networks for image classification reviewing its principal components and different robust architectures such as VGG-16, ResNet and Inception.

We will explore the concepts of Object Detecting and Transfer Learning using the last state of the art algorithms for object detection such as Faster R-CNN, TensorFlow Object Detection API and YOLO, applying this models on images, videos, and webcam images.

Finally you will learn how to construct and train your own dataset through GPU computing running Yolo v2, Yolo v3 and the latest Yolo v4 using Google Colaboratory.
You will find in this course a consice review of the theory with intuitive concepts of the algorithms, and you will be able to put in practice your knowledge with many practical examples.

The student has the opportunity to get a feedback from the instructor through Q&A forums, by email: machine.learning.eirl@gmail.com or by Twitter: @AILearningCQ

Computer Vision: Python OCR & Object Detection Quick Starter

Quick Starter for Optical Character Recognition, Image Recognition Object Detection and Object Recognition using Python

Created by Abhilash Nelson - Computer Engineering Master & Senior Programmer at Dubai

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Students: 2783, Price: $99.99

Students: 2783, Price:  Paid

Hi There!

welcome to my new course 'Optical Character Recognition and Object Recognition Quick Start with Python'. This is the third course from my Computer Vision series.

Image Recognition, Object Detection, Object Recognition and also Optical Character Recognition are among the most used applications of Computer Vision.

Using these techniques, the computer will be able to recognize and classify either the whole image, or multiple objects inside a single image predicting the class of the objects with the percentage accuracy score. Using OCR, it can also recognize and convert text in the images to machine readable format like text or a document.

Object Detection and Object Recognition is widely used in many simple applications and also complex ones like self driving cars.

This course will be a quick starter for people who wants to dive into Optical Character Recognition, Image Recognition and Object Detection using Python without having to deal with all the complexities and mathematics associated with typical Deep Learning process.

Let's now see the list of interesting topics that are included in this course.

At first we will have an introductory theory session about Optical Character Recognition technology.

After that, we are ready to proceed with preparing our computer for python coding by downloading and installing the anaconda package and will check and see if everything is installed fine.

Most of you may not be coming from a python based programming background. The next few sessions and examples will help you get the basic python programming skill to proceed with the sessions included in this course. The topics include Python assignment, flow-control, functions and data structures.

Then we will install the dependencies and libraries that we require to do the Optical Character Recognition. We are using Tesseract Library to do the OCR. At first we will install the Library and then its python bindings. We will also install OpenCV, which is the Open Source Computer Vision library in Python.

We also will install the Pillow library, which is the Python Image Library. Then we will have an introduction to the steps involved in the Optical Character Recognition and later will proceed with coding and implementing the OCR program. We will use few example images to do a Character Recognition testing and will verify the results.

Then we will have an introduction to Convolutional Neural Networks , which we will be using to do the Image Recognition. Here we will be classifying a full image based on the single primary object in it.

We will then proceed with installing the Keras Library which we will be using to do the Image recognition. We will be using the built in , pre-trained Models that are included in Keras. The base code in python is also provided in the Keras documentation.

At first We will be using the popular pre-trained model architecture called the VGGNet. we will have an introductory session about the architecture of VGGNet. Then we will proceed with using the pre-trained VGGNet 16 Model included in keras to do Image Recognition and classification. We will try with few sample images to check the predictions. Then will move on to a deeper VGGNet 19 Model included in keras to do Image Recognition and classification.

Then we will try the ResNet pre-trained model included with the Keras library. We will include the model in the code and then we will try with few sample images to check the predictions.

And after that we will try the Inception pre-trained model. We will also include the model in the code and then we will try with few sample images to check the predictions. Then will go ahead with the Xception pre-trained model. Here also, we will  include the model in the code and then we will try with few sample images.

And those were Image Recognition pre-trained models, which can only label and classify a complete image based on the primary object in it. Now we will proceed with Object Recognition in which we can detect and label multiple objects in a single image.

At first we will have an introduction to MobileNet-SSD Pre-trained Model, which is single shot detector that is capable of detecting multiple objects in a scene. We will be also be having a quick discussion about the dataset that is used to train this model.

Later we will be implementing the MobileNet-SSD Pre-trained Model in our code and will get the predictions and bounding box coordinates for every object detected. We will draw the bounding box around the objects in the image and write the label along with the confidence value.

Then we will go ahead with object detection from a live video. We will be streaming the real-time live video from the computer's webcam and will try to detect objects from it. We will draw rectangle around each object detected in the live video along with the label and confidence.

In the next session, we will go ahead with object detection from a pre-saved video. We will be streaming the saved video from our folder and will try to detect objects from it. We will draw rectangle around each object detected along with the label and confidence.

Later we will be going ahead with the Mask-RCNN Pre-trained Model. In the previous model, we were only able to get a bounding box around the object, but in Mask-RCNN, we can get both the box co-ordinates as well the mask over the exact shape of object detected. We will have an introduction about this model and its details.

Later we will be implementing the Mask-RCNN Pre-trained Model in our code and as the first step we will get the predictions and bounding box coordinates for every object detected. We will draw the bounding box around the objects in the image and write the label along with the confidence value.

Later we will be getting the mask returned for each object predicted. We will process that data and use it to draw translucent multi coloured masks over each and every object detected and write the label along with the confidence value.

Then we will go ahead with object detection from a live video using Mask-RCNN. We will be streaming the real-time live video from the computer's webcam and will try to detect objects from it. We will draw the mask over the perimeter of each object detected in the live video along with the label and confidence.

And like we did for our previous model, we will go ahead with object detection from a pre-saved video using Mask-RCNN. We will be streaming the saved video from our folder and will try to detect objects from it. We will draw coloured masks for object detected along with the label and confidence.

The Mask-RCNN is very accurate with vast class list but will be very slow in processing images using low power CPU based computers. MobileNet-SSD is fast but less accurate and low in number of classes. We need a perfect blend of speed and accuracy which will take us to Object Detection and Recognition using YOLO pre-trained model. we will have an overview about the yolo model in the next session and then we will implement yolo object detection from a single image.

And using that as the base, we will try the yolo model for object detection from a real time webcam video and we will check the performance. Later we will use it for object recognition from the pre-saved video file.

To further improve the speed of frames processed, we will use the model called Tiny YOLO which is a light weight version of the actual yolo model. We will use tiny yolo at first for the pre-saved video and will analyse the accuracy as well as speed and then we will try the same for a real-time video from webcam and see the difference in performance compared to actual yolo.

That's all about the topics which are currently included in this quick course. The code, images and libraries used in this course has been uploaded and shared in a folder. I will include the link to download them in the last session or the resource section of this course. You are free to use the code in your projects with no questions asked.

Also after completing this course, you will be provided with a course completion certificate which will add value to your portfolio.

So that's all for now, see you soon in the class room. Happy learning and have a great time.

Complete Python Based Image Processing and Computer Vision

Computer Vision Python : Image Recognition & Manipulation : Deep Learning Computer Vision Python : Image Analysis Python

Created by Minerva Singh - Bestselling Instructor & Data Scientist(Cambridge Uni)

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Students: 2144, Price: $99.99

Students: 2144, Price:  Paid

Complete Python Based Image Processing and Computer Vision With Conventional Techniques, Data Science and Deep Learning

THIS IS A COMPLETE PYTHON-BASED IMAGE PROCESSING & COMPUTER VISION COURSE !

It is a full  Python-based image processing and computer vision boot camp that will help you implement basic image processing and computer vision tasks using Jupyter Notebooks.                         

HERE IS WHY YOU SHOULD ENROLL IN THIS COURSE:

This course is your complete guide to practical image processing and computer vision tasks using Python..

This means, this course covers the important aspects of Keras and Tensorflow (Google's powerful Deep Learning framework) and if you take this course, you can do away with taking other courses or buying books on Python Tensorflow and Keras based data science.  

In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal and advent of Tensorflow and Keras is revolutionizing Deep Learning...

By gaining proficiency in Keras and and Tensorflow, you can give your company a competitive edge and boost your career to the next level.

THIS IS MY PROMISE TO YOU: COMPLETE THIS ONE COURSE & BECOME A PRO IN PRACTICAL KERAS & TENSORFLOW BASED DATA SCIENCE!

But first things first. My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation).

I have several years of experience in analyzing real life data from different sources  using data science related techniques and producing publications for international peer reviewed journals.

 Over the course of my research I realized almost all the Python data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably with machine learning..

This gives students an incomplete knowledge of the subject. My course, on the other hand, will give you a robust grounding in all aspects of data science within the Tensorflow framework.

Unlike other courses, we dig deep into both the conventional and data science-centric image processing and computer vision tasks! After learning the most important image processing and computer vision tasks, you will learn to implement both machine learning and deep learning techniques in a hands-on manner. You will be exposed to real life data and learn how to implement and evaluate the performance of the different data science packages, including Keras.

DISCOVER 8 COMPLETE SECTIONS ADDRESSING EVERY ASPECT OF PYTHON BASED IMAGE PROCESSING & COMPUTER VISION

• Detailed introduction to using the powerful Python driven framework for data science Anaconda for image processing and computer vision tasks
• Jargon-free introduction to the relevant theoretical concepts
• Detailed introduction to installing and using the relevant packages including tensor flow and Keras
• Implement Machine Learning algorithms, (both Supervised Learning and Unsupervised Learning ) on real life image data
• You’ll even discover how to create artificial neural networks and deep learning structures to implement on imagery data with Tensorflow & Keras

• Introduction to transfer learning

BUT,  WAIT! THIS ISN'T JUST ANY OTHER DATA SCIENCE COURSE:

You’ll start by absorbing the most commonly used image processing and computer vision basics and techniques.

I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts. This means you get a jargon free introduction to the much-needed theoretical concepts

My course will help you implement the methods using real imagery data obtained from different sources. Many courses use made-up data that does not empower students to implement Python based image processing in real -life.

After taking this course, you’ll easily use image processing and computer vision packages such as OpenCV along with gaining fluency in Tensorflow and Keras. I will even introduce you to deep learning models such as Convolution Neural network (CNN) and their implementation for imagery classification !!

The underlying motivation for the course is to ensure you can apply Python based data science techniques on real image data into practice today, start analyzing  data for your own projects whatever your skill level, and impress your potential employers with actual examples of  abilities.

It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to image processing and computer vision (and assocaited data science methods). However, majority of the course will focus on implementing different  techniques on real data and interpret the results..

After each video you will learn a new concept or technique which you may apply to your own projects!

JOIN THE COURSE NOW!

#computer #vision #python #image #processing #analysis

Hands on Computer Vision with OpenCV & Python

A comprehensive & easy to understand foundation to Computer Vision

Created by Shrobon Biswas - Researcher & OpenCV Lover

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Students: 1089, Price: $89.99

Students: 1089, Price:  Paid

  • Wanted to learn Computer Vision but hurdled by the MATH HEAVY articles ? 
  • Enthusiastic about learning OpenCv and don’t know where to start ?
  • Want to learn about Object Tracking but bogged down by too much theory ? 
  • Wanting to build strong portfolio with Computer Vision & Image Processing Projects ? 
  • Looking to add Computer Vision algorithms in your current software project ?

Whatever be your motivation to learn OpenCV, i can assure you that you’ve come to the right course.

Hands on Computer Vision with OpenCV & Python is THE most comprehensive and cost-effective video course you will find on the web right now. 

This course is tailor made for an individual who wishes to transition quickly from an absolute beginner to an OpenCV expert in just three weeks. I ensure this by breaking down and articulating the most difficult concepts in plain and simple manner, replacing tough equations by examples and concepts by using small code snippets. This course covers topics using a methodical step-by-step approach with increasing difficulty, starting outright with the very basics and fundamentals.

My approach is simple - Don’t parrot rote code , rather Understand. 

I personally guarantee this is the number one course for you. This may not be your first OpenCV course, but trust me - It will definitely be your last. 

I assure you, that you will receive fast, friendly, responsive support by email, and on the Udemy.

Don't believe me? I offer a full money back guarantee, so long as you request it within 30 days of your purchase of the course.

If you're looking for a genuinely effective course that equips you all the tools, and more importantly the knowhow and behind the scenes magic of OpenCV, then look no further. 

Also the course is updated on a regular basis to add more new and exciting content.

Join the course right now. 

So what are you waiting for ?

Let’s meet at the other side of the course. 

Learn Computer Vision with OpenCV and Python

Image processing basics, Object detection and tracking, Deep Learning, Facial landmarks and many special applications

Created by Ibrahim Delibasoglu - Research Assistant in Sakarya University

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Students: 835, Price: $89.99

Students: 835, Price:  Paid

Note: You will find real world examples (not only using implemented functions in OpenCV) and i'll add more by the time. It means that course content will expand with new special examples!.

***New Chapter***: "How to Prepare dataset and Train Your Deep Learning Model" was added to the course. You will learn how to prepare a simple dataset, label the objects and train your own deep learning model.

***New Special App***: "Search team logos" was added to the course. You will learn how you can compare images and find similar image/object in your dataset.

***New Chapter***: "Special Apps - Missing and Abandoned Object Detection" was added to the course. You will learn how to do an application for missing object detection and abandoned object detection

***New Chapter***: Facial Landmarks and Special Applications (real time sleep and smile detection) videos was added to the course!

***Different Special Applications Chapter***:  new videos in different topics will be shared under this chapter. You can look at "Soccer players detection" and "deep learning based API for object detection" examples. 

In this course, you are going to learn computer vision & image processing from scratch. You will reach all resources, have many examples and explanations of these examples.

The explanations are easy to understand and also you can ask the points you need.

I have shared key concepts with you without the heavily mathematical theory, so we can focus the implementation.

Maybe you can find some other resources, videos or blogs to learn about some of these topics explained in my course, but the advantage of this course is that, you will learn computer vision from scratch by following an order, so that you will not loss yourself between many different sources.

You will also find many special examples beside the fundamental topics.

I preferred to use OpenCV which is an open source computer vision library used and supported by many people!. I have used OpenCV with Python, because Python allows us to focus on the problem easily without spending time for programming syntax/complex codes.

I wish this course to be useful for you to learn computer vision, and Actively we can use 'questions and answers' area to share information...

You will learn the topics:

  • The key concepts of computer Vision & OpenCV

  • Basic operations: histogram equalization,thresholding, convolution, edge detection, sharpening ,morphological operations, image pyramids.

  • Keypoints and keypoint matching

  • Special App : mini game by using key points

  • Image segmentation: segmentation and contours, contour properties, line detection, circle detection, blob detection, watershed segmentation.

  • Special App: People counter 

  • Object tracking:Tracking APIs, Filtering by Color.

  • Special App: Tracking of moving object

  • Object detection: haarcascade face and eye detection, HOG pedestrian detection

  • Object detection with Deep Learning

  • Extra Chapter: How to Prepare dataset and Train Your Deep Learning Model

  • Extra Chapter: Special Apps - Missing and Abandoned Object Detection

  • Extra Chapter: Facial Landmarks and Special Applications (real time sleep and smile detection)

  • Extra Chapter: Different Special Applications ( will be updated with special examples in different topics )

Computer Vision Bootcamp™ with Python (OpenCV) – YOLO, SSD

Viola-Jones method, HOG features, R-CNNs, YOLO and SSD (Single Shot) Object Detection Approaches with Python and OpenCV

Created by Holczer Balazs - Software Engineer

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Students: 787, Price: $19.99

Students: 787, Price:  Paid

This course is about the fundamental concept of image processing, focusing on face detection and object detection.  These topics are getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to crime investigation.  Self-driving cars (for example lane detection approaches) relies heavily on computer vision.

With the advent of deep learning and graphical processing units (GPUs) in the past decade it's become possible to run these algorithms even in real-time videos. So what are you going to learn in this course?

Section 1 - Image Processing Fundamentals:

  • computer vision theory

  • what are pixel intensity values

  • convolution and kernels (filters)

  • blur kernel

  • sharpen kernel

  • edge detection in computer vision (edge detection kernel)

Section 2 - Serf-Driving Cars and Lane Detection

  • how to use computer vision approaches in lane detection

  • Canny's algorithm

  • how to use Hough transform to find lines based on pixel intensities

Section 3 - Face Detection with Viola-Jones Algorithm:

  • Viola-Jones approach in computer vision

  • what is sliding-windows approach

  • detecting faces in images and in videos

Section 4 - Histogram of Oriented Gradients (HOG) Algorithm

  • how to outperform Viola-Jones algorithm with better approaches

  • how to detects gradients and edges in an image

  • constructing histograms of oriented gradients

  • using suppor vector machines (SVMs) as underlying machine learning algorithms

Section 5 - Convolution Neural Networks (CNNs) Based Approaches

  • what is the problem with sliding-windows approach

  • region proposals and selective search algorithms

  • region based convolutional neural networks (C-RNNs)

  • fast C-RNNs

  • faster C-RNNs

Section 6 - You Only Look Once (YOLO) Object Detection Algorithm

  • what is the YOLO approach?

  • constructing bounding boxes

  • how to detect objects in an image with a single look?

  • intersection of union (IOU) algorithm

  • how to keep the most relevant bounding box with non-max suppression?

Section 7 - Single Shot MultiBox Detector (SSD) Object Detection Algorithm SDD

  • what is the main idea behind SSD algorithm

  • constructing anchor boxes

  • VGG16 and MobileNet architectures

  • implementing SSD with real-time videos

We will talk about the theoretical background of face recognition algorithms and object detection in the main then we are going to implement these problems on a step-by-step basis.

Thanks for joining the course, let's get started!

Computer Vision in Python for Beginners (Theory & Projects)

Computer Vision-Become an ace of Computer Vision, Computer Vision for Apps using Python, OpenCV, TensorFlow, etc.

Created by AI Sciences - AI Experts & Data Scientists |4+ Rated | 160+ Countries

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Students: 462, Price: $99.99

Students: 462, Price:  Paid

Comprehensive Course Description:

Computer vision (CV), a subfield of computer science, focuses on replicating the complex functionalities of the human visual system. In the CV process, real-world images and videos are captured, processed, and analyzed to allow machines to extract contextual, useful information from the physical world.

Until recently, computer vision functioned in a limited capacity. But due to the recent innovations in artificial intelligence and deep learning, this field has made great leaps. Today, CV surpasses humans in most routine tasks connected with detecting and labeling objects.

The high-quality content of the Mastering Computer Vision from the Absolute Beginning Using Python course presents you with a great opportunity to learn and become an expert. You will learn the core concepts of the CV field. This course will also help you to understand the digital imaging process and identify the key application areas of CV. The course is:

  • · Easy to understand.

  • · Descriptive.

  • · Comprehensive.

  • · Practical with live coding.

  • · Rich with state of the art and updated knowledge of this field.

Although this course is a compilation of all the basic concepts of CV, you are encouraged to step up and experience more than what you learn. Your understanding of every concept is tested at the end of each section. The Homework assignments/tasks/activities/quizzes along with solutions will assess your learning. Several of these activities are focused on coding so that you are ready to run with implementations.

The two hands-on projects in the last section—Change Detection in CCTV Cameras (Real-time) and Smart DVRs (Real-time)—make up the most important learning element of this course. They will help you sharpen your practical skills. Successful completion of these two projects will help you enrich your portfolio and kick-start your career in the CV field.

The course tutorials are divided into 320+ videos along with detailed code notebooks. The videos are available in HD, and the total runtime of the videos is 27 hours+.

Now is the perfect time to learn computer vision. Get started with this best-in-class course without any further delay!

Teaching is our passion:

In this course, we apply the proven learning by doing methodology. We build the interest of learners first. We start from the basics and focus on helping you understand each concept clearly. The explanation of each theoretical concept is followed by practical implementation. We then encourage you to create something new out of your learning.

Our aim is to help you master the basic concepts of CV before moving onward to advanced concepts. The course material includes online videos, course notes, hands-on exercises, project work, quizzes, and handouts. We also offer you learning support. You can approach our team in case of any queries, and we respond in quick time.

Course Content:

The comprehensive course consists of the following topics:

1. Introduction

a. Intro

i. What is computer vision?

2. Image Transformations

a. Introduction to images

i. Image data structure

ii. Color images

iii. Grayscale images

iv. Color spaces

v. Color space transformations in OpenCV

vi. Image segmentation using Color space transformations

b. 2D geometric transformations

i. Scaling

ii. Rotation

iii. Shear

iv. Reflection

v. Translation

vi. Affine transformation

vii. Projective geometry

viii. Affine transformation as a matrix

ix. Application of SVD (Optional)

x. Projective transformation (Homography)

c. Geometric transformation estimation

i. Estimating affine transformation

ii. Estimating Homography

iii. Direct linear transform (DLT)

iv. Building panoramas with manual key-point selection

3. Image Filtering and Morphology

a. Image Filtering

i. Low pass filter

ii. High pass filter

iii. Band pass filter

iv. Image smoothing

v. Image sharpening

vi. Image gradients

vii. Gaussian filter

viii. Derivative of Gaussians

b. Morphology

i. Image Binarization

ii. Image Dilation

iii. Image Erosion

iv. Image Thinning and skeletonization

v. Image Opening and closing

4. Shape Detection

a. Edge Detection

i. Definition of edge

ii. Naïve edge detector

iii. Canny edge detector

1. Efficient gradient computations

2. Non-maxima suppression using gradient directions

3. Multilevel thresholding- hysteresis thresholding

b. Geometric Shape detection

i. RANSAC

ii. Line detection through RANSAC

iii. Multiple lines detection through RANSAC

iv. Circle detection through RANSAC

v. Parametric shape detection through RANSAC

vi. Hough transformation (HT)

vii. Line detection through HT

viii. Multiple lines detection through HT

ix. Circle detection through HT

x. Parametric shape detection through HT

xi. Estimating affine transformation through RANSAC

xii. Non-parametric shapes and generalized Hough transformation

5. Key Point Detection and Matching

a. Corner detection (Key point detection)

i. Defining Corner

ii. Naïve corner detector

iii. Harris corner detector

1. Continuous directions

2. Tayler approximation

3. Structure tensor

4. Variance approximation

5. Multi-scale detection

b. Project: Building automatic panoramas

i. Automatic key point detection

ii. Scale assignment

iii. Rotation assignment

iv. Feature extraction (SIFT)

v. Feature matching

vi. Image stitching

6. Motion

a. Optical Flow, Global Flow

i. Brightness constancy assumption

ii. Linear approximation

iii. Lucas–Kanade method

iv. Global flow

v. Motion segmentation

b. Object Tracking

i. Histogram based tracking

ii. KLT tracker

iii. Multiple object tracking

iv. Trackers comparisons

7. Object detection

a. Classical approaches

i. Sliding window

ii. Scale space

iii. Rotation space

iv. Limitations

b. Deep learning approaches

i. YOLO a case study

8. 3D computer vision

a. 3D reconstruction

i. Two camera setups

ii. Key point matching

iii. Triangulation and structure computation

b. Applications

i. Mocap

ii. 3D Animations

9. Projects

a. Change detection in CCTV cameras (Real-time)

b. Smart DVRs (Real-time)

After completing this course successfully, you will be able to:

  • · Relate the concepts and theories in computer vision with real-world problems.

  • · Implement any project from scratch that requires computer vision knowledge.

  • · Know the theoretical and practical aspects of computer vision concepts.

Who this course is for:

  • · Learners who are absolute beginners and know nothing about Computer Vision.

  • · People who want to make smart solutions.

  • · People who want to learn computer vision with real data.

  • · People who love to learn theory and then implement it using Python.

  • · People who want to learn computer vision along with its implementation in realistic projects.

  • · Data Scientists.

  • · Machine learning experts.

The Complete Deep Learning & Computer Vision Course in 2021

Learn Deep Learning & Computer Vision with Python, Tensorflow 2.0, OpenCV, FastAI. Object Detection & GAN and much more!

Created by Shubham Gupta - Machine Learning Engineer

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Students: 336, Price: $89.99

Students: 336, Price:  Paid

This Brand New and Modern Deep Learning & Computer Vision Course will teach you everything you will need to know to become a Computer Vision Expert.

Deep Learning & Computer Vision is currently one of the most increasing fields of Artificial Intelligence and Companies like Google, Apple,

Facebook, Amazon are highly investing in this field. Deep Learning & Computer Vision jobs are increasing day by day & provide some of the highest paying jobs all over the world.

If We Want Machines to Think, We Need to Teach Them to See.-Fei Fei Li, Director of Stanford AI Lab and Stanford Vision Lab

Computer Vision allows us to see the world & process digital images & videos to extract useful information to do a certain task from classification, object detection, and much more. Python is one of the most popular used programming language in Deep Learning and Computer Vision.

All the tools, techniques & technologies used in this course -

  1. Learning Computer Vision & Deep Learning Fundamentals

  2. Setting up Anaconda, Installing Libraries & Jupyter Notebook

  3. Learning fundamentals of OpenCV & Numpy - Reading images, Colorspaces, Drawing & Callbacks

  4. Advanced OpenCV - Image Preprocessing, Geometrical transformations, Perspective transformations & affine transformations, image blending & pyramids, image gradients & thresholding, Canny Edge Detector and contours

  5. Working with videos in OpenCV -  Using webcam, Haar Cascades & Object Detection, Lane Detection

  6. Deep Learning & How Neural Network Works? - Artificial neural networks, Convolution Neural Networks & Transfer Learning

Image Classification - Plant leaf Classification

  • Working on very recent Kaggle Competitions

  • Using Google Colab & Kaggle Kernels

  • Using the latest Tensorflow 2.0 & Keras

  • Using Keras Data Generators & Data Argumentation

  • Using Transfer Learning & Ensemble learning

  • Using State of The Art Deep Learning Models

  • Using GPU & TPU for Model Training

  • Hyperparameter Tuning

  • Using Weights & Biases for recording Deep Learning experimentations

  • Saving & Loading Models

  • Creating a Weights & Biases Report & Showcasing the Project!

Object Detection - Wheat heads Detection

  • Working on Kaggle Competitions, again!

  • Using Facebook's Detectron2 for Object Detection

  • Creating COCO Dataset from scratch

  • Training Faster RCNN Model and Custom Weights & Biases callback

  • Using Retinanet

  • Saving & Loading Detectron2 models

Generative Adversarial Networks - Creating Fake Leaf Images

  • Learning How Generative Adversarial Networks works

  • Using FastAI

  • Creating & Training Generative Adversarial Networks

  • Making Fake Images using GAN

Making ML Web Application

  • Getting started with Streamlit

  • Creating an ML Web Application from scratch using Streamlit

  • making a React Web Application

Deploying ML Applications

  • Learning how to use Cloud Services to Deploy Models & Applications

  • Using Heroku

  • Learning how to Open Source Projects on GitHub

  • How to showcase your projects to impress boss & employees & Get Hired!

A lot of bonus lectures!

This is what included in the package

  • All lecture codes are available for downloadable for free

  • 110+ HD video lectures ( over 50 more to come very soon! )

  • Free support in course Q/A

  • All videos with English captions available

This course is for you if..

  • ... you want to learn the Latest Tools & Techniques used in Deep Learning & Computer Vision

  • ... you want to get more experience to Win Kaggle Competitions

  • ... you want to get started with Computer Vision to become a Computer Vision Engineer

  • .. you are interested in learning Image Classification, Object Detection, Generative Adversarial Networks, Making & Deploying Machine Learning Applications

Computer Vision – OCR using Python

Become a Computer Vision expert and learn optical character recognition - OCR using Tesseract, OpenCV and Deep Learning

Created by Vineeta Vashistha - Senior Technical Architect - Machine Learning

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Students: 193, Price: $89.99

Students: 193, Price:  Paid

*This course is a quick starter for people who would like to become Computer Vision - Optical Character Recognition (OCR) Specialist *

Optical Character Recognition commonly called as OCR is the new buzzword in industry which is driving digitization in the enterprises. Every enterprise wants to adopt OCR to achieve easier and quicker access to their streams of data in digital format. An OCR implementation not only speed up the workflow of Text processes across various industries but also help in providing better customer experience. In fact, as per a recent research report, OCR market which was around 7.2 billion US Dollar is expected to see a huge growth in market size and will reach 13.4 billion US dollar by 2025.

Enroll in this course to get a complete understanding of Optical Character Recognition (OCR) for Data Extraction from Images and PDF using Python. The course explains the theory of concepts followed by code demonstration to make you an expert in computer vision OCR. It provides hands-on guidance on Text Detection with OpenCV and Deep Learning Models, Text Recognition with Tesseract and OCR along with Text Labelling through Spacy and Regular Expression. It guides you to create technical solutions on most relevant OCR uses cases in the industry

Here are just few of the topics we will be learning:

  • OCR Architecture

  • Pixels and Image Basics

  • Image Properties

  • Kernel and Feature Map

  • Preprocessing Techniques (Binarisation, Thresholding, Rescaling)

  • Noise Removal Techniques (Morphology, Dilation, Erosion, Blurring, Orientation, Deskewing, Borders, Perspective Transformation)

  • Image Segmentation

  • EasyOCR

  • PyTesseract Operations

  • Tesseract

  • Named Entity Recognition

  • Spacy for Named Entity Recognition

  • Regular Expression for Text and Dates

  • Training of CTPN and EAST Deep Learning Model on SIROE Dataset

  • CTPN Model for Text Detection & Text Recognition

  • EAST Model for Text Detection & Text Recognition

  • Invoice Processing OCR Solution with python code

  • Invoice Structured Output in XML Format Solution with python code

  • Vehicle Nameplate OCR Solution with python code

  • Business Card Recognition OCR Solution with python code

  • KYC Digitization OCR Solution with python code

** May 2021 - The course has been updated with Bonus Lecture on Training of CTPN and EAST on ICDAR - SIROE Dataset**

Computer Vision with OpenCV | Deep Learning CNN Projects

Learn Python OpenCV 4,Computer Vision and Deep Learning Projects from scratch to expert level| Deep Learning CNN Project

Created by Goeduhub Technologies - Technical Training Provider Company.

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Students: 129, Price: $49.99

Students: 129, Price:  Paid

OpenCV-Python is an appropriate tool for fast prototyping of Computer Vision problems.

Hands-on Computer Vision with OpenCV from scratch to real-time project development.

Computer Vision is the hottest field in the era of Artificial Intelligence. It is making enormous advances in Self-driving cars, Robotics, Medical as well as in various image correction apps.

Computer Vision is showing us the future of technology and we can't even imagine what will be the end of its possibilities.

OpenCV library uses NumPy and all its array structures convert to and from NumPy arrays.

OpenCV is used to develop real-time computer vision applications. It is capable of processing images and videos to identify objects, faces, or even handwriting.

You will learn the topics:

  • The key concepts of computer Vision & OpenCV

  • Basic operations: Image read and display, Image Properties, Image resize and write, ROI and Color Mapping, Horizontal & Vertical flipping of images.

  • Drawing function in OpenCV

  • Working with Live Camera

  • Object detection: haarcascade face detection in images and Live camera

  • Convolutional Neural Network (CNN)

  • Deep Learning with Keras

  • Project: Handwritten Digit Classification using MNIST

  • Project: Fashion Classifier using FNIST

  • Project: Dogs vs Cats classifier

  • Project: Object Detection using YOLOv3

  • Project: Social Distancing Detector COVID-19

Feel free to message us on Udemy if you have any questions about the course!

So, are you ready to get started? Enroll now and start the process of becoming a Master in Computer Vision using Deep Learning today!

Advanced Computer Vision with TensorFlow

Exploit the power of TensorFlow to perform image processing

Created by Packt Publishing - Tech Knowledge in Motion

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Students: 119, Price: $89.99

Students: 119, Price:  Paid

TensorFlow has been gaining immense popularity over the past few months, due to its power and simplicity to use. This video will help you leverage the power of TensorFlow to perform advanced image processing. This course is a continuation of the Intro to Computer Vision course, building on top of the skills learned in that course. In this course, you’ll dive deeper as we cover more advanced computer vision concepts.

You will implement multiple state-of-the-art deep learning papers from scratch using the TensorFlow-Keras API. This course will teach you how to construct efficient CNN architectures with CNN Squeeze layers and delayed downsampling . You’ll learn about residual learning with skip connections and deep residual blocks, and see how to implement a deep residual neural network for image recognition. You’ll find out about Google’s Inception module and depthwise separable convolutions and understand how to construct an extreme Inception architecture with TF-Keras.

Finally, you’ll be introduced to the exciting new world of adversarial neural networks, which are responsible for recent breakthroughs in synthetic image generation and implement an auxiliary conditional GAN.

About the Author

Marvin Bertin has authored online Deep Learning courses. Marvin is the technical editor of a deep learning book and a conference speaker. He has a bachelor’s degree in Mechanical Engineering and Masters in Data Science.

Marvin has worked at a deep learning start-up developing neural network architectures. He is currently working in the biotech industry building NLP machine learning solutions. At the forefront of next generation DNA sequencing, he builds intelligent applications with Machine Learning and Deep Learning for precision medicine.

Create Deep Learning Computer Vision Apps using Python 2020

Control Computer using Hand Gestures | Machine Learning Hand Gestures Recognition System | Image Processing Applications

Created by Coding Cafe - Web and Mobile Applications Development

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Students: 98, Price: $19.99

Students: 98, Price:  Paid

In this course you will learn how to create your own Hand Gestures Recognition System in which you can control your Windows Media Player using Hand Gestures.

In this project by using Ai Computer Visions techniques we will get user hand movements (gestures) at real time. And we will defined some specific gestures for Media Player play , pause , next , previous , backward , forward actions and we will control media player using these gestures.

This is a complete step by step Machine Learning | Deep Learning Hand Gestures Recognition System Project. At the end of this series you will be able to make your own Artificial intelligence Powerful Apps using Computer Vision.

Computer Vision – Object Detection on Videos – Deep Learning

Quick Starter on Object Detection and Image Classification on Videos using Deep Learning, OpenCV, YOLO and CNN Models

Created by Vineeta Vashistha - Senior Technical Architect - Machine Learning

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Students: 96, Price: $89.99

Students: 96, Price:  Paid

Machine Learning on Videos has the potential to make a profound impact in a data-driven business and is emerging as the new buzzword in the industry. This course provides an end-to-end coverage of Machine Learning on videos through Video analytics, Object Detection and Image Classification. It is a complete hands-on tutorial that teaches how to implement Video Analytics using the 3-step process of Capture, Process and Save Video, understand various Object Detection Models and implement them for a real-time case study of Social Distancing and last but not the least, take a deep dive into steps involved in using Deep Learning Models, Transfer Learning and learn how to create a model on face mask detection using Image Classification and leverage it to implement a solution of face mask detection.

Here are just few of the topics we will be learning:

  • Machine Learning

  • Deep Learning

  • Video Analytics

  • Object Detection

  • Object Detection Models

  • Image Classification

  • Object Tracking

  • Simple Online and Realtime Tracking (SORT) Framework

  • Deep Learning

  • Deep Learning Image Classifier

  • Convolution Neural Network (CNN)

  • Transfer Learning

  • Model Training on Google CoLab

  • Haar Cascade Classifier

  • HOG Model

  • YOLOV3 Tiny Model

  • Faster R-CNN Model

  • Video codec

  • Haar Cascade Social Distancing Solution with python code

  • Hog Solution Social Distancing Solution with python code

  • YOLO Social Distancing Solution with python code

  • Faster R-CNN Social Distancing Solution with python code

  • Face Mask Detection Solution with python code

  • People Footfall Tracking with python code

  • Automatic Parking Management with python code

2021 Complete Computer Vision Bootcamp, Zero-Hero in Python™

Build Projects and Learn to solve real life problems by using Computer Vision and Python [Highest Rated Course] .

Created by Aman Anand - Computer Science Engineer From Amity University, India

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Students: 70, Price: $89.99

Students: 70, Price:  Paid

Welcome to the Complete Computer Vision Bootcamp Course With OpenCv Python 2020.

Highest Rated Course

This Course is will teach you Computer Vision and Image Processing Techniques From Basic to Advance Level.

This Course Provide all high quality content to learn and become Industry level Expert. We worked Really hard to explain the concepts of Computer Vision and Image Processing and the necessary mathematics behind each concept. You will get a Clear Idea about how computer understand and work with images and video Data.

We will Start with a Short Python course where you will learn to code in python and will have clear understanding of python syntax and some advance concepts like python generators along with Object Oriented Programming. So Even if Your are a complete Beginner, you are going to learn everything provided in this course.

After python Crash Course we will start with numpy and images basics, there we will learn how to read images as numpy array and to manipulate images with numpy.

Then we will move on to Image basics with openCV, there you willl learn how to open, create and to draw on the created blank image.

After that you will learn

Image Processing Techniques Using OpenCv like: Color Mapping, Image Blending, Image Thresholding, Morphology, Real Time Edge Detection Using Webcam and OpenCv in Python.

Then We will Make a Project which is in demand and you can directly put it in your resume.

Overall After Completing This Course You will be Expert in Computer Vision and Image Processing.