Best Image Processing Courses

Find the best online Image Processing 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 Image Processing Courses.

Deep Learning: Convolutional Neural Networks in Python

Use CNNs for Image Recognition, Natural Language Processing (NLP) +More! For Data Science, Machine Learning, and AI

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


Students: 27466, Price: $109.99

Students: 27466, Price:  Paid


Learn about one of the most powerful Deep Learning architectures yet!

The Convolutional Neural Network (CNN) has been used to obtain state-of-the-art results in computer vision tasks such as object detection, image segmentation, and generating photo-realistic images of people and things that don't exist in the real world!

This course will teach you the fundamentals of convolution and why it's useful for deep learning and even NLP (natural language processing).

You will learn about modern techniques such as data augmentation and batch normalization, and build modern architectures such as VGG yourself.

This course will teach you:

  • The basics of machine learning and neurons (just a review to get you warmed up!)

  • Neural networks for classification and regression (just a review to get you warmed up!)

  • How to model image data in code

  • How to model text data for NLP (including preprocessing steps for text)

  • How to build an CNN using Tensorflow 2

  • How to use batch normalization and dropout regularization in Tensorflow 2

  • How to do image classification in Tensorflow 2

  • How to do data preprocessing for your own custom image dataset

  • How to use Embeddings in Tensorflow 2 for NLP

  • How to build a Text Classification CNN for NLP (examples: spam detection, sentiment analysis, parts-of-speech tagging, named entity recognition)

All of the materials required for this course can be downloaded and installed for FREE. We will do most of our work in Numpy, Matplotlib, and Tensorflow. I am always available to answer your questions and help you along your data science journey.

This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

Suggested Prerequisites:

  • matrix addition and multiplication

  • basic probability (conditional and joint distributions)

  • Python coding: if/else, loops, lists, dicts, sets

  • Numpy coding: matrix and vector operations, loading a CSV file


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


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.


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 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.


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 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]

Master the Fourier transform and its applications

Learn the Fourier transform in MATLAB and Python, and its applications in digital signal processing and image processing

Created by Mike X Cohen - Neuroscientist, writer, professor


Students: 10363, Price: $19.99

Students: 10363, Price:  Paid

The Fourier transform is one of the most important operations in signal processing and modern technology, and therefore in modern human civilization. But how does it work, and why does it work?

What you will learn in this course:

You will learn the theoretical and computational bases of the Fourier transform, with a strong focus on how the Fourier transform is used in modern applications in signal processing, data analysis, and image filtering. The course covers not only the basics, but also advanced topics including effects of non-stationarities, spectral resolution, normalization, filtering. All videos come with MATLAB and Python code for you to learn from and adapt!

This course is focused on implementations of the Fourier transform on computers, and applications in digital signal processing (1D) and image processing (2D). I don't go into detail about setting up and solving integration problems to obtain analytical solutions. Thus, this course is more on the computer science/data science/engineering side of things, rather than on the pure mathematics/differential equations/infinite series side.

This course is for you if you are an aspiring or established:

  • Data scientist

  • Statistician

  • Computer scientist (MATLAB and/or Python)

  • Signal processing or image processing expert (or aspiring!)

  • Biologist

  • Engineer

  • Student

  • Curious independent learner!

What you get in this course:

  • >6 hours of video lectures that include explanations, pictures, and diagrams

  • pdf readers with important notes and explanations

  • Many exercises and their solutions! (Note: exercises are in the pdf readers)

  • MATLAB code, Python code, and sample datasets for applications

With >3000 lines of MATLAB and Python code, this course is also a great way to improve your programming skills, particularly in the context of signal processing and image processing.

Why I am qualified to teach this course:

I have been using the Fourier transform extensively in my research and teaching (primarily in MATLAB) for nearly two decades. I have written several textbooks about data analysis, programming, and statistics, that rely extensively on the Fourier transform. Most importantly: I have taught the Fourier transform to bachelor's students, PhD students, professors, and professionals, and I have taught to people from many backgrounds, including biology, psychology, physics, mathematics, and engineering.

So what are you waiting for??

Watch the course introductory video to learn more about the contents of this course and about my teaching style. And scroll down to see what other students think of this course and of my teaching style.

I hope to see you soon in the course!


Image Processing Masterclass in Python For Beginners In 2021

Learn basic Python image processing techniques and also build your Python skill with complete projects in Python 3

Created by Emenwa Global - Senior Developers


Students: 7837, Price: $89.99

Students: 7837, Price:  Paid

Image Processing Masterclass in Python For Beginners In 2021 starts from the very beginning by teaching you image processing with Python programming and Adobe Photoshop. Then it goes into advanced topics and different career fields in Python programming and Adobe Photoshop so you can get real life practice and be ready for the real world.

Some Fundamentals of Image Processing that were covered in this course are as follows:

  • How to install pillow library

  • How to crop images

  • RGB, RGBA, Pallate and so on

  • Image splitting

  • Channels

  • How to mix multiple channels

  • Basic image transformations

  • How to convert images from JPEG to PNG and Vice Versa

  • So many other amazing knowledge is included with full image processing documentation

Why Must I Take This Course And What Benefit Is It To ME As A Python Programmer?

This is the only course on the internet that will help you to become a certified and successful programmer with an in-depth knowledge of the entire aspect of Python programming and prepare you with the required skills necessary to build you to face job interviews and get employed as a full stack Software developer.

Emenwa Global instructors are industry experts with years of practical, real-world experience building software at industry leading companies. They are sharing everything they know to teach thousands of students around the world, just like you, the most in-demand technical and non-technical skills (which are commonly overlooked) in the most efficient way so that you can take control of your life and unlock endless exciting new career opportunities in the world of technology, no matter your background or experience.

Build Your own Self Driving Car | Deep Learning, OpenCV, C++

Learn Raspberry Pi, Arduino UNO, Image Processing and Neural Networks (Machine Learning) for any Embedded IOT Project

Created by Rajandeep Singh - Embedded System Engineer


Students: 3321, Price: $99.99

Students: 3321, Price:  Paid

"Machine Learning will change the lives of all of us. What is Machine Learning? It’s behind what makes self-driving cars a reality"

This unique course is a complete walk-through process to Design, Build and Program a Embedded IOT Project (Self driving Car). Everything is discussed with details and clear explanation. Whole Project is divided into 2 parts.

(Course - 1)

1. Learn to design complete hardware for self driving car

   a. Learn to setup Master device ( Raspberry Pi ) for any project

   b. Learn to setup Slave device ( Arduino UNO ) for any project

  c. Learn to Establish Communication link between Master and Slave device

2. Learn Image Processing using OpenCV4

3. Learn to driver robot on road lanes

(Course - 2)

1. Learn Essentials of Machine Learning

2. Learn to train your own cascade classifier to detect Stop Sign, Traffic Lights and any Object

3. Learn to design LED Dynamic Turn Indicators

4. Create your GitHub Repository

For More Information, don't hesitate to email:

OpenCV on Google Colab using Python

Image Processing on OpenCV using Google Colab and implementing practical algorithms on the Images

Created by ThinkIoT Solution - Expert Trainer in Embedded Devices and IoT


Students: 3063, Price: $19.99

Students: 3063, Price:  Paid

This course is a practical explanation on using the Google Colab for executing the Image Processing algorithms using OpenCV module available in Python. The course starts with explanation about the Google Colab and executing few basic codes in Python and then the basics of Image Processing are explained. Working with gray Images and Colour Images is taken up next and conversion from colour to gray is also explained. Then the Image Threshold and colour detection is explained by taking random images as inputs. The drawing tools are explained using which the images can be marked, lines, polygons and shapes can be drawn using the functions available in python.

This course will explain the concepts of Image Processing and learn how to access the Image data for a 2D and a 3D Image and this course can be used a foundation to build more complex algorithms in Image Processing. The image data for 2D and 3D image is explained and the red, blue and green channel in the image are extracted to understand exactly what a colour image consists of. This helps the students to learn the algorithms better and apply it in any further image processing. The Image Threshold and colour detection concepts are also explained by taking the image data as example which ensures that you understand the concepts very clearly.

Digital Image Processing: Operations and Applications

Learn Image Processing operations with numericals in 2 hours.

Created by Ajay Dhruv - Assistant Professor at VIT Mumbai, India


Students: 2893, Price: Free

Students: 2893, Price:  Free

Image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. Well, don't be overwhelmed by all those technical terms, we will help you with the fundamentals. So now are you interested to know about the field of Image Processing? Then this self-paced course is for you!

This course has been designed such that we can share our knowledge and help you learn complex theory, techniques and concepts in a simple way. It is a perfect match for all those self-taught students out there!

We will walk you into the World of Image Processing. With every tutorial you will develop new skills and improve your understanding of this field. We have provided lectures with notes and assignments to make your learning process more interactive. While preparing this course special care is taken that the concepts are presented in fun and exciting way but at the same time, we dive deep into Image Processing.

Here is a list of few of the topics we will be learning:

  • Image and Pixels

  • Image Processing model / fundamental steps

  • Color Models

  • Image enhancement

  • Point Processing Operations

  • Neighborhood Processing

  • Histograms

  • Frequency domain

  • Transformations

  • Point, line and Edge detection

  • Local and global processing

  • Real World Applications

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)


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


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.                         


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.


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.


• 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


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!


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

Learn image processing and GUIs while having fun in MATLAB

Improve your digital image processing and image processing programming skills in MATLAB. And have fun in the process!

Created by Mike X Cohen - Neuroscientist, writer, professor


Students: 1557, Price: $19.99

Students: 1557, Price:  Paid

Images are the most important ways of getting information across, ranging from art to marketing to politics. And nowadays, nearly all images are digital. Therefore, it's important to know about image processing and digital image processing.

What will you get from this course?

  1. You will learn fundamental skills in image processing and graphical user interfaces (GUIs) in a way that is fun and engaging. Being bored while learning is a waste of everyone's time and energy, plus you really only learn when you are enjoying the learning experience. You don't need any background in image processing before taking this course.

  2. Improving your MATLAB programming skills. This is not only about image processing related code; you'll also increase your MATLAB coding and programming skills concerning numerical processing, control statements, working with data, and more.

What are the prerequisites?

You need some basic MATLAB programming experience. If you are totally new to MATLAB, then please take an intro-MATLAB programming course first.

If you are familiar with variables, if-then statements, for-loops, and creating functions, then you have the necessary knowledge for this course.

What should you do now?

Check out the list of topics and watch the preview videos to find out if this course is right for you. If you have any questions, send me a message. You should also check out the student reviews of my other courses to see what people think about my teaching style in general.

See you in class!


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


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 )

MATLAB and Digital Image Processing

A comprehensive guide for MATLAB image processing Toolbox

Created by Aakash Saxena - Educator


Students: 799, Price: $19.99

Students: 799, Price:  Paid

-- Join the Course only IF --

+ Looking Systematic approach to the Subject

+ Programming is a problem.

+ Not just Theory but Practicals works for you.

+ Need comprehensive and detailed toolbox idea

+ can't spend more than a cost of book

+ looking for Inside OUT approach in which you can create something on your own with Basics intact.


In the end you will be able to :

  • Perform operations on Images.
  • Easily able to Manipulate, Code and Play with Images.
  • You will be able to optimise your own codes.
  • You will be efficient to work on MATLAB
  • Finally you will be able to use Image Processing Toolbox efficiently.
  • ---------------------------------------------------------------------------------------------

    BONUS --

    • You will get all the MATLAB codes written by me and shown in Tutorial absolutely free !!
    • We will also be developing an Applications at the end of the course !! (to bring Learning into Practical).
    • thats not it --> I will be uploading more applications in the coming weeks and Months.
  • You will also develop skill to program and develop your own way of processing Images.
  • You will learn all the standard methods and techniques used Globally.
  • Image Consulting 101

    All you Need to Know about Image Consulting

    Created by Vidha Sharma - Principal Consultant at Vidha Sharma Consulting


    Students: 657, Price: Free

    Students: 657, Price:  Free

    We often miss out on critical knowledge that can help us get ahead in life, and that stage of not knowing is  called Unconscious Incompetence, in this course I try to enable you to move from Unconscious Incompetence to Conscious Incompetence* i.e. is give you all the awareness you  need to understand Image Consulting, its Impact, The Process and what a Life Transforming tool it can be In your Personal Development journey.

    Our Image and the way we manage it can alter our Personal, Professional and Social Journeys, and Image Consulting is a fantastic faculty to help us do just that. It take more than good clothes or self confidence to ensure consistency in the impressions we make on people around us. 

    This course does not "teach" you Image Management, it helps you with curated details that might get lost in the information overload and that have the potential to move  you to make the right desicions to set you up for  wholistic success.

    For those who are aware of Image Consulting, this course decodes the general approach and shatters any notions that may have been stopping you from considering an Image Consulting programme.

    * You move from Conscious Incompetence to Conscious Competence when you start applying the knowledge practically.

    Advanced Image Processing

    Learn Advanced Image Processing with live project demonstration in 2 hours.

    Created by Ajay Dhruv - Assistant Professor at VIT Mumbai, India


    Students: 629, Price: Free

    Students: 629, Price:  Free

    Image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. Well, don't be overwhelmed by all those technical terms, we will help you with the fundamentals. So now are you interested to know about the field of Image Processing? Then this self-paced course is for you!

    This course has been designed such that we can share our knowledge and help you learn complex theory, techniques and concepts in a simple way. It is a perfect match for all those self-taught students out there!

    We will walk you into the World of Image Processing. With every tutorial you will develop new skills and improve your understanding of this field. While preparing this course special care is taken that the concepts are presented in fun and exciting way but at the same time, we dive deep into Image Processing. Using the strategies, lessons, and exercises in this course, you will learn all that is necessary to be a master in Image Processing. Here is a list of few of the topics we will be learning:

    · What is Image Compression

    · Lossless compression

    · Lossy compression

    · Lossy algorithms

    · Lossless algorithms

    · Blocks of Machine Learning

    · Convolutional Neural Network

    · Applications of Medical Image Processing

    · Live project demonstration

    Complete Guide to Image Processing with MATLAB

    Understand the Theory of Image Processing, apply it in MATLAB, and design a GUI to interface it!

    Created by Fawaz Sammani - Computer Vision Researcher


    Students: 608, Price: $19.99

    Students: 608, Price:  Paid

    This course focuses on delivering the basics of Image Processing in MATLAB

    The course also provides explanations to the theories.

    You'll learn various tutorials, including:

    • Image Operations

    • Image Histograms

    • Image Filtering 

    • Image Thresholding

    • Edge Detection in MATLAB

    • Image Morphology 

    • Local Binary Patterns

    • Practical Examples 

    •  At the end, all what you have learned and more will be compiled and we you learn how to interface them in a Graphical User Interface (GUI) in MATLAB


    You will also get access to files that explain some theoretical concepts in a friendly-manner that will make you grasp the idea very quick! 

    Finally, there will be some practices after some of the lectures to test your understanding and see how good you can apply the concepts you learned to come up with new ideas! 

    Learning Path: OpenCV: Master Image Processing with OpenCV 3

    Develop interactive computer vision applications with the popular C++ libraries of OpenCV 3

    Created by Packt Publishing - Tech Knowledge in Motion


    Students: 596, Price: $89.99

    Students: 596, Price:  Paid

    OpenCV 3 is a native cross-platform C++ Library for computer vision, machine learning, and image processing. Computer vision applications are the latest buzz of recent time! Big brands such as Microsoft, Apple, Google, Facebook, and Apple are increasingly making use of computer vision for object, pattern, image, and face recognition. This has led to a very high demand for computer vision expertise. So, if you're interested to know how to use the OpenCV library to build computer vision applications, then go for this Learning Path.

    Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.

    The highlights of this Learning Path are:

    • Dive into the essentials of OpenCV and build your own projects
    • Learn how to apply complex visual effects to images
    • Reconstruct a 3D scene from images
    • Master the fundamental concepts in computer vision and image processing

    Let’s take a quick look at your learning journey. This Learning Path helps you to get started with the OpenCV library and shows you how to install and deploy it to write effective computer vision applications following good programming practices. You will learn how to read and display images. You will then be introduced to the basic OpenCV data structures.

    Further, you will start a new project and see how to load an image file and show it. Next, you'll find out how to handle keyboard events in our display window. In the next project, you will jump into interactively adjusting image brightness. You will then learn to add a miniaturizing tilt-shift effect and how to blur images. In the final project, you will learn to apply Instagram-like color ambiance filters to images.

    By the end of this Learning Path, you will be able to build computer vision applications that make the most of OpenCV 3.

    Meet Your Experts:

    We have combined the best works of the following esteemed authors to ensure that your learning journey is smooth:

    • Robert Laganiere is a professor at the School of Electrical Engineering and Computer Science of the University of Ottawa, Canada. He is also a faculty member of the VIVA research lab and is the co-author of several scientific publications and patents in content-based video analysis, visual surveillance, driver-assistance, object detection, and tracking. Robert authored the OpenCV2 Computer Vision Application Programming Cookbook in 2011 and co-authored Object Oriented Software Development, published by McGraw Hill in 2001. He is also a consultant in computer vision and has assumed the role of Chief Scientist in a number of startups companies such as Cognivue Corp, iWatchlife, and Tempo Analytics.
    • AdiShavit is an experienced software architect and has been an OpenCV user since it was in early beta back in 2000. Since then he has been using it pretty much continuously to build systems and products ranging from embedded, vehicle, and mobile apps to desktops and large, distributed cloud-based servers and services. His specialty is in computer vision, image processing, and machine learning with an emphasis on real-time applications. He specializes in cross-platform, high performance software combined with a high production-quality maintainable code base. He builds many products, apps, and services that leverage OpenCV.

    Matlab Digital Image Processing From Ground Up™

    Image Processing : Edge-Detection Algorithms , Convolution, Filter Design, Gray-Level Transformation, Histograms etc.

    Created by Israel Gbati - Embedded Firmware Engineer


    Students: 292, Price: $89.99

    Students: 292, Price:  Paid

    With a programming based approach, this course is designed to give you a solid foundation in the most useful aspects of Image Processing in an engaging and easy to follow way. The goal of this course is to present practical techniques while avoiding  obstacles of abstract mathematical theories. To achieve this goal, the image processing techniques are explained in plain language, not simply proven to be true through mathematical derivations.

    Still keeping it simple, this course comes in different programming languages so that students can put the techniques to practice using a programming language of their choice. This version of the course uses the Matlab programming language.

    By the end of the course you should be able to perform 2-D Discrete Convolution with images in matlab, perform Edge-Detection in matlab, perform Spatial Filtering in matlab, compute an Image Histogram and Equalize it in matlab,  perform  Gray Level Transformations, suppress noise in images, understand all about operators such as Laplacian, Sobel, Prewitt, Robinson, even give a lecture on image processing and more. Please take a look at the full course curriculum.

    REMEMBER : I have no doubt you will love this course. Also it comes with a  FULL money back guarantee for 30 days!  So put simply, you really have nothing to loose and everything to gain.

    Sign up and lets start manipulating some pixels.

    Complete LabVIEW Image Processing & Machine Vision Course

    Learn Easiest and Simplest LabVIEW Image Processing & LabVIEW Machine Vision Step by Step.

    Created by Milad Ahmadi - Professional Programmer


    Students: 186, Price: $89.99

    Students: 186, Price:  Paid

    Are you going to learn image processing and machine vision? Do you want to learn it in one of the easiest and interesting programming softwares? So join.

    Image processing is a technology by which you can perform some changes on an image, in order to do measurement, calibration, identification, control in different industries such as production, medicine, meteorology, astronomy, military and security.

    Machine vision is a type of technology that provides automatic inspection, identification, measurement, control and analysis. One of the amazing features of machine learning is that it can be integrated with deep learning and machine learning technologies which might help business and organizations a lot.

    In this course we learn LabVIEW image processing and machine learning .This course is a project based in which all subject are taught with their real world application. Gamification and psychological techniques have been used in this course to maximize the efficiency of learning. Also in this course, instead of working with vision assistance, we learn all the subjects block by block which help you learn the real and principle way of LabVIEW image processing and Machine Vision. Therefore, some of the topics we learn in this course are as the following:

    LabVIEW Image Processing:

    • Color processing

    • Morphology

    • Overlay

    • Operators

    • Feature detection

    • Measurement

    • Image analysis

    LabVIEW Machine Vision:

    • Advanced feature detection

    • Pattern matching

    • Measure and Count

    • Tracking

    • Instrument Read

    • Optical Character Recognition

    Hands-On Python & Xcode Image Processing: Build Games & Apps

    Coding masterclass - Learn the Python coding language. Learn image manipulation & recognition techniques for iOS apps.

    Created by Mammoth Interactive - Top-Rated Instructor, 800,000+ Students


    Students: 142, Price: $89.99

    Students: 142, Price:  Paid

    "Clear as crystal" ⭐⭐⭐⭐⭐

    Are you ready to learn to code and manipulate images in an engaging and practical course? Sign up now to meet us!

    In Part 1, you learn how to code in the Python 3.5 programming language. Whether you have or have not coded before, you can learn how to use Python. Python is a popular programming language that is useful to know because of its versatility. 

    Python is easy to understand and can be used for many different environments. Cross-platform apps and 3D environments are often made in Python.

    We cover basic programming concepts for people who have never programmed before. This course covers key topics in Python and coding in general, including variables, loops, and classes. Moreover, you learn how to handle input, output, and errors.

    To learn how to use Python, we create our own functioning Blackjack game! In this game, you receive cards, submit bets, and keep track of your score. 

    By the end of this course you'll be able to use the coding knowledge you gained to make your own apps and environments in Python.

    In Part 2, you learn how to add unique features to the images in your apps. A CIImage is a representation of an image that can be altered with Core Image filters. These filters allow users to change and interact with images in cool and useful ways. CIImages provide a lot of power that other image types do not.

    Why Xcode? 

    Xcode is Apple's FREE software for app development. Xcode is user-friendly and has the tools you need to make apps for the iPhone, iPad, Mac, Apple Watch, and Apple TV.

    Learn with us how to add User Interface (UI) elements, including text fields, sliders, and buttons, to make an app. You learn to code in Swift 3.0, Apple's programming language, to make the app function.

    Included in this course is material for beginners to get comfortable with the interfaces. Please note that we reuse this material in similar courses because it is introductory material. You can find some material in this course in the following related courses:

    • Mastering Core Image: XCode's Image Framework  

    • Alter images in Xcode and create 3D characters for games

    • Make 22 GameMaker: Studio Games & 5 Image Data UI Projects  

    • Core Image Filters and SVG with HTML, CSS and Javascript 

    • Everything You Need to Know About Angular & Image Processing

    • C# & Image Processing Masterclass: Make Mobile Games & Apps

    • Ultimate Python Beginner Course. Learn to code today! 

    • Start to Finish Unity Games and Python Coding

    • Hands-On Python & Xcode Image Processing: Build Games & Apps  

    • Build 25 Games in Python and GameMaker (and Learn to Code)

    • Build 23 Games in Python and Construct 2 (and Learn to Code)

    "Is this course for me?" Yes!

    By taking this course, you will gain the tools you need continue improving yourself in the field of app development. You will be able to apply what you learned to further experiment in Xcode and make your own apps able to perform more.

    Let's get started!

    Image Processing using OpenCV from Zero to Hero, 8 Projects

    Complete practical and project based learning on image processing with OpenCV Python

    Created by Data Science Anywhere - Team of Engineers


    Students: 132, Price: $89.99

    Students: 132, Price:  Paid

    Welcome to "Image Processing using OpenCV from Zero to Hero" !!!

    Image Processing 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 is completely project-based learning. Where you will do the project after completion of every module. Here I will cover the image processing from basics to advanced techniques including applied machine learning algorithms and models to images.


    • Image Basics

    • Drawings

    • Image Translation

    • Image Processing Techniques

    • Smoothing Filters

    • Filters

    • Graphical Use Interphase  (GUI) in OpenCV

    Key Highlights in Section 1 to 7

    We will start the course with very basic like load, display images. With that, we will understand the basic mathematics background behind the images. Also, I will teach you the concepts of Drawings and Videos.

    Projects (Object Detection):

    1. Face Detection using Viola-Jones Algorithm

    2. Face Detection using Deep Neural Networks (SSD ResNet 10, Caffe Implementation)

    3. Real-Time Face Detection

    4. Facial Landmark Detection

    Key Highlights in Section 8 to 11

    We will slowly move into image processing concepts related to image transformations like image translation, flipping, rotating, and cropping. I will also teach arithmetic operations in OpenCV.

    Project (Brightness Control):

      5. GUI based Brightness Control in Images

      6. Real-Time Brightness Control

    Key Highlights in Section 12,13

    In these sections, I will introduce new concepts on bitwise operations and masking, where you will learn the truth table and different bitwise operations like "AND", "OR", "NOT", "XOR".

    Key Highlights in Section 14

    Then we will extend our discussion on Smoothing Filter which is a very important image processing technique. In this section, I will teach smoothing techniques like Average Blur, Gaussian Blur, Median Blur & Bilateral Filter.

    You will have complete access to Images, Data, Jupyter Notebook files that are used in this course. The code used in this course is written in such a way that you can directly plug the function into the real-time scenario and get the output. 

    I will see you inside the course!!!


    Srikanth Guskra

    Big Data code optimization in Python NumPy: sound processing

    Big Data code optimization in Python NumPy + sound processing in MoviePy + binarizing images in computer vision - Pillow

    Created by Mark Misin - Aerospace & Robotics Engineer


    Students: 104, Price: $89.99

    Students: 104, Price:  Paid

    Programming is one of the most flexible fields I know of. You can create a program that achieves a certain task in so many ways. However, that does not mean that all ways are equal. Some are better than others.

    That is especially visible when your program has to work with big data. Working with big data means working with gigantic arrays and matrices.

    You can create a program that achieves the same task like the other one, but it does so 1000 times faster. It all depends on how you code and which coding practices you use.

    And this is what you will learn here. You will learn the good and the bad coding practices, so that you would learn to code the right way when dealing with big data.

    In this 100% project based course, we will use Python, the Numpy and the Moviepy library to create a fully functional sound processing program.

    This program will import your videos in sequence, extract their audio, automatically identify the silent intervals in that audio, and then cut them out while still keeping some silence on the edges to preserve a bit of pause in between sentences.

    Sound processing naturally deals with millions and millions array elements and so it really matters how we write that program. We will do it in a bad way and in a good way, because I want you to see both sides of the coin.

    In the end, you will see that the last version of your Python Numpy code will be more than 1000 times faster than the first version, and so, you will see how to code and how definitely not to code.

    Finally, I really want you to see that this knowledge is universal and can be applied in other fields as well, not only audio processing. And therefore, in the last section, there will be an assignment in computer vision.

    Digital images are in fact, gigantic matrices, and so, it really matters how you handle them in the code. We will build a small program that can binarize these images and we will also do it in a good and in a bad way.

    We will use the Python image processing library called Pillow to process all this big data inside the image matrices.

    After this course, you will know how to approach programming in the right way from the beginning. Take a look at some of my free preview videos and if you like what you see, then, ENROLL NOW and let's get started! I'll see you inside.

    Image Processing

    Digital Image Processing

    Created by Yasir Amir - Learn from a University Professor


    Students: 72, Price: $19.99

    Students: 72, Price:  Paid

    In this course you will learn Digital Image Processing. Initially the fundamental concepts are taught including the origin of digital image processing, image sensing and acquisition, sampling and quantization, intensity resolution and spatial resolution. Gradually this course leads to more in-depth topics such as intensity transformation and spatial filtering, segmentation, color image processing.

    Basics of Digital Image Processing Hands-on Using Matlab

    Learn and implement bit plane slicing, 2 basic methods of change detection, 1 method of image steganography & encryption

    Created by Pragati Sunilkumar Dwivedi - Student of science with interest in programming & teaching


    Students: 61, Price: $19.99

    Students: 61, Price:  Paid

    This course will help you understand image processing using matlab software right from getting the software to  installing the software in your system. Few processes/techniques have been explained in details along with their implementation. After learning few fundamentals one can build project or do research in this field. After learning concepts taught in this course you can learn about remote sensing. I will be taking an advance course on image processing using matlab where I will be teaching novel technique for image classification, also will be implementing the same using matlab.

    Lightroom Image Processing Mastery by SLR Lounge

    Lightroom 4 and 5 Guide, from Basics like color correction and B&W conversion to advanced HDRs and Vintage fades

    Created by Payam Jirsa - Everything Photography


    Students: 45, Price: $34.99

    Students: 45, Price:  Paid

    With over 10 hours of hands on image processing instruction within the Lightroom Develop Module, after completing this workshop, you will be able to post process and fix common image issues as well as create virtually any type of advanced image processing effect. This workshop is designed to help photographers truly master basic to advanced post production techniques.

    From casual street photographers to busy professional wedding photographers to award-winning landscape photographers, we've had thousands of people take this course with satisfied results.