Best Tensorflow Courses

Find the best online Tensorflow 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 Tensorflow Courses.

Python for Data Science and Machine Learning Bootcamp

Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more!

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

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Students: 463995, Price: $89.99

Students: 463995, Price:  Paid

Are you ready to start your path to becoming a Data Scientist! 

This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms!

Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems!

This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!

This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science and machine learning on Udemy!

We'll teach you how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python! Here a just a few of the topics we will be learning:

  • Programming with Python
  • NumPy with Python
  • Using pandas Data Frames to solve complex tasks
  • Use pandas to handle Excel Files
  • Web scraping with python
  • Connect Python to SQL
  • Use matplotlib and seaborn for data visualizations
  • Use plotly for interactive visualizations
  • Machine Learning with SciKit Learn, including:
  • Linear Regression
  • K Nearest Neighbors
  • K Means Clustering
  • Decision Trees
  • Random Forests
  • Natural Language Processing
  • Neural Nets and Deep Learning
  • Support Vector Machines
  • and much, much more!

Enroll in the course and become a data scientist today!

Machine Learning, Data Science and Deep Learning with Python

Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks

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

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Students: 150695, Price: $89.99

Students: 150695, Price:  Paid

New! Updated for 2021 with extra content on generative models: variational auto-encoders (VAE's) and generative adversarial models (GAN's)

Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money - it's interesting work too!

If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path. This comprehensive machine learning tutorial includes over 100 lectures spanning 15 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. I’ll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn’t.

Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won't find academic, deeply mathematical coverage of these algorithms in this course - the focus is on practical understanding and application of them. At the end, you'll be given a final project to apply what you've learned!

The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We'll cover the machine learning, AI, and data mining techniques real employers are looking for, including:

  • Deep Learning / Neural Networks (MLP's, CNN's, RNN's) with TensorFlow and Keras

  • Creating synthetic images with Variational Auto-Encoders (VAE's) and Generative Adversarial Networks (GAN's)

  • Data Visualization in Python with MatPlotLib and Seaborn

  • Transfer Learning

  • Sentiment analysis

  • Image recognition and classification

  • Regression analysis

  • K-Means Clustering

  • Principal Component Analysis

  • Train/Test and cross validation

  • Bayesian Methods

  • Decision Trees and Random Forests

  • Multiple Regression

  • Multi-Level Models

  • Support Vector Machines

  • Reinforcement Learning

  • Collaborative Filtering

  • K-Nearest Neighbor

  • Bias/Variance Tradeoff

  • Ensemble Learning

  • Term Frequency / Inverse Document Frequency

  • Experimental Design and A/B Tests

  • Feature Engineering

  • Hyperparameter Tuning

...and much more! There's also an entire section on machine learning with Apache Spark, which lets you scale up these techniques to "big data" analyzed on a computing cluster.

If you're new to Python, don't worry - the course starts with a crash course. If you've done some programming before, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC's, Linux desktops, and Macs.

If you’re a programmer looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic techniques used by real-world industry data scientists. These are topics any successful technologist absolutely needs to know about, so what are you waiting for? Enroll now!

  • "I started doing your course... Eventually I got interested and never thought that I will be working for corporate before a friend offered me this job. I am learning a lot which was impossible to learn in academia and enjoying it thoroughly. To me, your course is the one that helped me understand how to work with corporate problems. How to think to be a success in corporate AI research. I find you the most impressive instructor in ML, simple yet convincing." - Kanad Basu, PhD

Complete Guide to TensorFlow for Deep Learning with Python

Learn how to use Google's Deep Learning Framework - TensorFlow with Python! Solve problems with cutting edge techniques!

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

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Students: 89033, Price: $124.99

Students: 89033, Price:  Paid

Welcome to the Complete Guide to TensorFlow for Deep Learning with Python!

This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand. Other courses and tutorials have tended to stay away from pure tensorflow and instead use abstractions that give the user less control. Here we present a course that finally serves as a complete guide to using the TensorFlow framework as intended, while showing you the latest techniques available in deep learning!

This course is designed to balance theory and practical implementation, with complete jupyter notebook guides of code and easy to reference slides and notes. We also have plenty of exercises to test your new skills along the way!

This course covers a variety of topics, including

  • Neural Network Basics
  • TensorFlow Basics
  • Artificial Neural Networks
  • Densely Connected Networks
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • AutoEncoders
  • Reinforcement Learning
  • OpenAI Gym
  • and much more!

There are many Deep Learning Frameworks out there, so why use TensorFlow?

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.

It is used by major companies all over the world, including Airbnb, Ebay, Dropbox, Snapchat, Twitter, Uber, SAP, Qualcomm, IBM, Intel, and of course, Google!

Become a machine learning guru today! We'll see you inside the course!

A Complete Guide on TensorFlow 2.0 using Keras API

Build Amazing Applications of Deep Learning and Artificial Intelligence in TensorFlow 2.0

Created by Hadelin de Ponteves - AI Entrepreneur

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Students: 51972, Price: $99.99

Students: 51972, Price:  Paid

Welcome to Tensorflow 2.0!

TensorFlow 2.0 has just been released, and it introduced many features that simplify the model development and maintenance processes. From the educational side, it boosts people's understanding by simplifying many complex concepts. From the industry point of view, models are much easier to understand, maintain, and develop.

Deep Learning is one of the fastest growing areas of Artificial Intelligence. In the past few years, we have proven that Deep Learning models, even the simplest ones, can solve very hard and complex tasks. Now, that the buzz-word period of Deep Learning has, partially, passed, people are releasing its power and potential for their product improvements.

The course is structured in a way to cover all topics from neural network modeling and training to put it in production.

In Part 1 of the course, you will learn about the technology stack that we will use throughout the course (Section 1) and the TensorFlow 2.0 library basics and syntax (Section 2).

In Part 2 of the course, we will dig into the exciting world of deep learning. Through this part of the course, you will implement several types of neural networks (Fully Connected Neural Network (Section 3), Convolutional Neural Network (Section 4), Recurrent Neural Network (Section 5)). At the end of this part, Section 6, you will learn and build their own Transfer Learning application that achieves state of the art (SOTA) results on the Dogs vs. Cats dataset.

After passing the part 2 of the course and ultimately learning how to implement neural networks, in Part 3 of the course, you will learn how to make your own Stock Market trading bot using Reinforcement Learning, specifically Deep-Q Network.

Part 4 is all about TensorFlow Extended (TFX). In this part of the course, you will learn how to work with data and create your own data pipelines for production. In Section 8 we will check if the dataset has any anomalies using the TensorFlow Data Validation library and after learn how to check a dataset for anomalies, in Section 9, we will make our own data preprocessing pipeline using the TensorFlow Transform library.

In Section 10 of the course, you will learn and create your own Fashion API using the Flask Python library and a pre-trained model. Throughout this section, you will get a better picture of how to send a request to a model over the internet. However, at this stage, the architecture around the model is not scalable to millions of request. Enter the Section 11. In this section of the course, you will learn how to improve solution from the previous section by using the TensorFlow Serving library. In a very easy way, you will learn and create your own Image Classification API that can support millions of requests per day!

These days it is becoming more and more popular to have a Deep Learning model inside an Android or iOS application, but neural networks require a lot of power and resources! That's where the TensorFlow Lite library comes into play. In Section 12 of the course, you will learn how to optimize and convert any neural network to be suitable for a mobile device.

To conclude with the learning process and the Part 5 of the course, in Section 13 you will learn how to distribute the training of any Neural Network to multiple GPUs or even Servers using the TensorFlow 2.0 library.

Data Science: Deep Learning and Neural Networks in Python

The MOST in-depth look at neural network theory, and how to code one with pure Python and Tensorflow

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

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Students: 46598, Price: $89.99

Students: 46598, Price:  Paid

This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE.

We extend the previous binary classification model to multiple classes using the softmax function, and we derive the very important training method called "backpropagation" using first principles. I show you how to code backpropagation in Numpy, first "the slow way", and then "the fast way" using Numpy features.

Next, we implement a neural network using Google's new TensorFlow library.

You should take this course if you are interested in starting your journey toward becoming a master at deep learning, or if you are interested in machine learning and data science in general. We go beyond basic models like logistic regression and linear regression and I show you something that automatically learns features.

This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we'll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited.

Another project at the end of the course shows you how you can use deep learning for facial expression recognition. Imagine being able to predict someone's emotions just based on a picture!

After getting your feet wet with the fundamentals, I provide a brief overview of some of the newest developments in neural networks - slightly modified architectures and what they are used for.

NOTE:

If you already know about softmax and backpropagation, and you want to skip over the theory and speed things up using more advanced techniques along with GPU-optimization, check out my follow-up course on this topic, Data Science: Practical Deep Learning Concepts in Theano and TensorFlow.

I have other courses that cover more advanced topics, such as Convolutional Neural NetworksRestricted Boltzmann MachinesAutoencoders, and more! But you want to be very comfortable with the material in this course before moving on to more advanced subjects.

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.

"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:

  • calculus (taking derivatives)

  • matrix arithmetic

  • probability

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

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

  • Be familiar with basic linear models such as linear regression and logistic regression

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)

Complete Machine Learning & Data Science Bootcamp 2021

Learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence) and Python with Tensorflow, Pandas & more!

Created by Andrei Neagoie - Senior Software Developer / Founder of zerotomastery.io

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Students: 45901, Price: $89.99

Students: 45901, Price:  Paid

This is a brand new Machine Learning and Data Science course just launched and updated this month with the latest trends and skills for 2021! Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 400,000+ engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. Graduates of Andrei’s courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, + other top tech companies. You will go from zero to mastery!

Learn Data Science and Machine Learning from scratch, get hired, and have fun along the way with the most modern, up-to-date Data Science course on Udemy (we use the latest version of Python, Tensorflow 2.0 and other libraries). This course is focused on efficiency: never spend time on confusing, out of date, incomplete Machine Learning tutorials anymore. We are pretty confident that this is the most comprehensive and modern course you will find on the subject anywhere (bold statement, we know).

This comprehensive and project based course will introduce you to all of the modern skills of a Data Scientist and along the way, we will build many real world projects to add to your portfolio. You will get access to all the code, workbooks and templates (Jupyter Notebooks) on Github, so that you can put them on your portfolio right away! We believe this course solves the biggest challenge to entering the Data Science and Machine Learning field: having all the necessary resources in one place and learning the latest trends and on the job skills that employers want.

The curriculum is going to be very hands on as we walk you from start to finish of becoming a professional Machine Learning and Data Science engineer. The course covers 2 tracks. If you already know programming, you can dive right in and skip the section where we teach you Python from scratch. If you are completely new, we take you from the very beginning and actually teach you Python and how to use it in the real world for our projects. Don't worry, once we go through the basics like Machine Learning 101 and Python, we then get going into advanced topics like Neural Networks, Deep Learning and Transfer Learning so you can get real life practice and be ready for the real world (We show you fully fledged Data Science and Machine Learning projects and give you programming Resources and Cheatsheets)!

The topics covered in this course are:

- Data Exploration and Visualizations

- Neural Networks and Deep Learning

- Model Evaluation and Analysis

- Python 3

- Tensorflow 2.0

- Numpy

- Scikit-Learn

- Data Science and Machine Learning Projects and Workflows

- Data Visualization in Python with MatPlotLib and Seaborn

- Transfer Learning

- Image recognition and classification

- Train/Test and cross validation

- Supervised Learning: Classification, Regression and Time Series

- Decision Trees and Random Forests

- Ensemble Learning

- Hyperparameter Tuning

- Using Pandas Data Frames to solve complex tasks

- Use Pandas to handle CSV Files

- Deep Learning / Neural Networks with TensorFlow 2.0 and Keras

- Using Kaggle and entering Machine Learning competitions

- How to present your findings and impress your boss

- How to clean and prepare your data for analysis

- K Nearest Neighbours

- Support Vector Machines

- Regression analysis (Linear Regression/Polynomial Regression)

- How Hadoop, Apache Spark, Kafka, and Apache Flink are used

- Setting up your environment with Conda, MiniConda, and Jupyter Notebooks

- Using GPUs with Google Colab

By the end of this course, you will be a complete Data Scientist that can get hired at large companies. We are going to use everything we learn in the course to build professional real world projects like Heart Disease Detection, Bulldozer Price Predictor, Dog Breed Image Classifier, and many more. By the end, you will have a stack of projects you have built that you can show off to others.

Here’s the truth: Most courses teach you Data Science and do just that. They show you how to get started. But the thing is, you don’t know where to go from there or how to build your own projects. Or they show you a lot of code and complex math on the screen, but they don't really explain things well enough for you to go off on your own and solve real life machine learning problems.

Whether you are new to programming, or want to level up your Data Science skills, or are coming from a different industry, this course is for you. This course is not about making you just code along without understanding the principles so that when you are done with the course you don’t know what to do other than watch another tutorial. No! This course will push you and challenge you to go from an absolute beginner with no Data Science experience, to someone that can go off, forget about Daniel and Andrei, and build their own Data Science and Machine learning workflows.

Machine Learning has applications in Business Marketing and Finance, Healthcare, Cybersecurity, Retail, Transportation and Logistics, Agriculture, Internet of Things, Gaming and Entertainment, Patient Diagnosis, Fraud Detection, Anomaly Detection in Manufacturing, Government, Academia/Research, Recommendation Systems and so much more. The skills learned in this course are going to give you a lot of options for your career.

You hear statements like Artificial Neural Network, or Artificial Intelligence (AI), and by the end of this course, you will finally understand what these mean!

Click “Enroll Now” and join others in our community to get a leg up in the industry, and learn Data Scientist and Machine Learning. We guarantee this is better than any bootcamp or online course out there on the topic. See you inside the course!

Taught By:

Daniel Bourke:
A self-taught Machine Learning Engineer who lives on the internet with an uncurable desire to take long walks and fill up blank pages.

My experience in machine learning comes from working at one of Australia's fastest-growing artificial intelligence agencies, Max Kelsen.

I've worked on machine learning and data problems across a wide range of industries including healthcare, eCommerce, finance, retail and more.

Two of my favourite projects include building a machine learning model to extract information from doctors notes for one of Australia's leading medical research facilities, as well as building a natural language model to assess insurance claims for one of Australia's largest insurance groups.

Due to the performance of the natural language model (a model which reads insurance claims and decides which party is at fault), the insurance company were able to reduce their daily assessment load by up to 2,500 claims.

My long-term goal is to combine my knowledge of machine learning and my background in nutrition to work towards answering the question "what should I eat?".

Aside from building machine learning models on my own, I love writing about and making videos on the process. My articles and videos on machine learning on Medium, personal blog and YouTube have collectively received over 5-million views.

I love nothing more than a complicated topic explained in an entertaining and educative matter. I know what it's like to try and learn a new topic, online and on your own. So I pour my soul into making sure my creations are accessible as possible.

My modus operandi (a fancy term for my way of doing things) is learning to create and creating to learn. If you know the Japanese word for this concept, please let me know.

Questions are always welcome.

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Andrei Neagoie:
Andrei is the instructor of the highest rated Development courses on Udemy as well as one of the fastest growing. His graduates have moved on to work for some of the biggest tech companies around the world like Apple, Google, Amazon, JP Morgan, IBM, UNIQLO etc... He has been working as a senior software developer in Silicon Valley and Toronto for many years, and is now taking all that he has learned, to teach programming skills and to help you discover the amazing career opportunities that being a developer allows in life. 

Having been a self taught programmer, he understands that there is an overwhelming number of online courses, tutorials and books that are overly verbose and inadequate at teaching proper skills. Most people feel paralyzed and don't know where to start when learning a complex subject matter, or even worse, most people don't have $20,000 to spend on a coding bootcamp. Programming skills should be affordable and open to all. An education material should teach real life skills that are current and they should not waste a student's valuable time.   Having learned important lessons from working for Fortune 500 companies, tech startups, to even founding his own business, he is now dedicating 100% of his time to teaching others valuable software development skills in order to take control of their life and work in an exciting industry with infinite possibilities. 

Andrei promises you that there are no other courses out there as comprehensive and as well explained. He believes that in order to learn anything of value, you need to start with the foundation and develop the roots of the tree. Only from there will you be able to learn concepts and specific skills(leaves) that connect to the foundation. Learning becomes exponential when structured in this way. 

Taking his experience in educational psychology and coding, Andrei's courses will take you on an understanding of complex subjects that you never thought would be possible.  

See you inside the course!

Tensorflow 2.0: Deep Learning and Artificial Intelligence

Neural Networks for Computer Vision, Time Series Forecasting, NLP, GANs, Reinforcement Learning, and More!

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

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Students: 31072, Price: $129.99

Students: 31072, Price:  Paid

Welcome to Tensorflow 2.0!

What an exciting time. It's been nearly 4 years since Tensorflow was released, and the library has evolved to its official second version.

Tensorflow is Google's library for deep learning and artificial intelligence.

Deep Learning has been responsible for some amazing achievements recently, such as:

  • Generating beautiful, photo-realistic images of people and things that never existed (GANs)

  • Beating world champions in the strategy game Go, and complex video games like CS:GO and Dota 2 (Deep Reinforcement Learning)

  • Self-driving cars (Computer Vision)

  • Speech recognition (e.g. Siri) and machine translation (Natural Language Processing)

  • Even creating videos of people doing and saying things they never did (DeepFakes - a potentially nefarious application of deep learning)

Tensorflow is the world's most popular library for deep learning, and it's built by Google, whose parent Alphabet recently became the most cash-rich company in the world (just a few days before I wrote this). It is the library of choice for many companies doing AI and machine learning.

In other words, if you want to do deep learning, you gotta know Tensorflow.

This course is for beginner-level students all the way up to expert-level students. How can this be?

If you've just taken my free Numpy prerequisite, then you know everything you need to jump right in. We will start with some very basic machine learning models and advance to state of the art concepts.

Along the way, you will learn about all of the major deep learning architectures, such as Deep Neural Networks, Convolutional Neural Networks (image processing), and Recurrent Neural Networks (sequence data).

Current projects include:

  • Natural Language Processing (NLP)

  • Recommender Systems

  • Transfer Learning for Computer Vision

  • Generative Adversarial Networks (GANs)

  • Deep Reinforcement Learning Stock Trading Bot

Even if you've taken all of my previous courses already, you will still learn about how to convert your previous code so that it uses Tensorflow 2.0, and there are all-new and never-before-seen projects in this course such as time series forecasting and how to do stock predictions.

This course is designed for students who want to learn fast, but there are also "in-depth" sections in case you want to dig a little deeper into the theory (like what is a loss function, and what are the different types of gradient descent approaches).

Advanced Tensorflow topics include:

  • Deploying a model with Tensorflow Serving (Tensorflow in the cloud)

  • Deploying a model with Tensorflow Lite (mobile and embedded applications)

  • Distributed Tensorflow training with Distribution Strategies

  • Writing your own custom Tensorflow model

  • Converting Tensorflow 1.x code to Tensorflow 2.0

  • Constants, Variables, and Tensors

  • Eager execution

  • Gradient tape

Instructor's Note: This course focuses on breadth rather than depth, with less theory in favor of building more cool stuff. If you are looking for a more theory-dense course, this is not it. Generally, for each of these topics (recommender systems, natural language processing, reinforcement learning, computer vision, GANs, etc.) I already have courses singularly focused on those topics.

Thanks for reading, and I’ll see you in class!

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)

Complete Tensorflow 2 and Keras Deep Learning Bootcamp

Learn to use Python for Deep Learning with Google's latest Tensorflow 2 library and Keras!

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

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Students: 29825, Price: $99.99

Students: 29825, Price:  Paid

This course will guide you through how to use Google's latest TensorFlow 2 framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow 2 framework in a way that is easy to understand.

We'll focus on understanding the latest updates to TensorFlow and leveraging the Keras API (TensorFlow 2.0's official API) to quickly and easily build models. In this course we will build models to forecast future price homes, classify medical images, predict future sales data, generate complete new text artificially and much more!

This course is designed to balance theory and practical implementation, with complete jupyter notebook guides of code and easy to reference slides and notes. We also have plenty of exercises to test your new skills along the way!

This course covers a variety of topics, including

  • NumPy Crash Course

  • Pandas Data Analysis Crash Course

  • Data Visualization Crash Course

  • Neural Network Basics

  • TensorFlow Basics

  • Keras Syntax Basics

  • Artificial Neural Networks

  • Densely Connected Networks

  • Convolutional Neural Networks

  • Recurrent Neural Networks

  • AutoEncoders

  • GANs - Generative Adversarial Networks

  • Deploying TensorFlow into Production

  • and much more!

Keras, a user-friendly API standard for machine learning, will be the central high-level API used to build and train models. The Keras API makes it easy to get started with TensorFlow 2. Importantly, Keras provides several model-building APIs (Sequential, Functional, and Subclassing), so you can choose the right level of abstraction for your project. TensorFlow’s implementation contains enhancements including eager execution, for immediate iteration and intuitive debugging, and tf.data, for building scalable input pipelines.

TensorFlow 2 makes it easy to take new ideas from concept to code, and from model to publication. TensorFlow 2.0 incorporates a number of features that enables the definition and training of state of the art models without sacrificing speed or performance

It is used by major companies all over the world, including Airbnb, Ebay, Dropbox, Snapchat, Twitter, Uber, SAP, Qualcomm, IBM, Intel, and of course, Google!

Become a deep learning guru today! We'll see you inside the course!

Modern Deep Learning in Python

Build with modern libraries like Tensorflow, Theano, Keras, PyTorch, CNTK, MXNet. Train faster with GPU on AWS.

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

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Students: 28277, Price: $109.99

Students: 28277, Price:  Paid

This course continues where my first course, Deep Learning in Python, left off. You already know how to build an artificial neural network in Python, and you have a plug-and-play script that you can use for TensorFlow. Neural networks are one of the staples of machine learning, and they are always a top contender in Kaggle contests. If you want to improve your skills with neural networks and deep learning, this is the course for you.

You already learned about backpropagation, but there were a lot of unanswered questions. How can you modify it to improve training speed? In this course you will learn about batch and stochastic gradient descent, two commonly used techniques that allow you to train on just a small sample of the data at each iteration, greatly speeding up training time.

You will also learn about momentum, which can be helpful for carrying you through local minima and prevent you from having to be too conservative with your learning rate. You will also learn about adaptive learning rate techniques like AdaGradRMSprop, and Adam which can also help speed up your training.

Because you already know about the fundamentals of neural networks, we are going to talk about more modern techniques, like dropout regularization and batch normalization, which we will implement in both TensorFlow and Theano. The course is constantly being updated and more advanced regularization techniques are coming in the near future.

In my last course, I just wanted to give you a little sneak peak at TensorFlow. In this course we are going to start from the basics so you understand exactly what's going on - what are TensorFlow variables and expressions and how can you use these building blocks to create a neural network? We are also going to look at a library that's been around much longer and is very popular for deep learning - Theano. With this library we will also examine the basic building blocks - variables, expressions, and functions - so that you can build neural networks in Theano with confidence.

Theano was the predecessor to all modern deep learning libraries today. Today, we have almost TOO MANY options. Keras, PyTorch, CNTK (Microsoft), MXNet (Amazon / Apache), etc. In this course, we cover all of these! Pick and choose the one you love best.

Because one of the main advantages of TensorFlow and Theano is the ability to use the GPU to speed up training, I will show you how to set up a GPU-instance on AWS and compare the speed of CPU vs GPU for training a deep neural network.

With all this extra speed, we are going to look at a real dataset - the famous MNIST dataset (images of handwritten digits) and compare against various benchmarks. This is THE dataset researchers look at first when they want to ask the question, "does this thing work?"

These images are important part of deep learning history and are still used for testing today. Every deep learning expert should know them well.

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.

"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 about gradient descent

  • Probability and statistics

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

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

  • Know how to write a neural network with Numpy

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)

Tensorflow 2.0 | Recurrent Neural Networks, LSTMs, GRUs

Sequence prediction course that covers topics such as: RNN, LSTM, GRU, NLP, Seq2Seq, Attention, Time series prediction

Created by Jad Slim - Developer

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Students: 20158, Price: Free

Students: 20158, Price:  Free

This is a preview to the exciting Recurrent Neural Networks course that will be going live soon. Recurrent Networks are an exciting type of neural network that deal with data that come in the form of a sequence. Sequences are all around us such as sentences, music, videos, and stock market graphs. And dealing with them requires some type of memory element to remember the history of the sequences, this is where Recurrent Neural networks come in.

We will be covering topics such as RNNs, LSTMs, GRUs, NLP, Seq2Seq, attention networks and much much more.

You will also be building projects, such as a Time series Prediction, music generator, language translation, image captioning, spam detection, action recognition and much more.

Building these projects will impress even the most senior machine learning developers; and will prepare you to start tackling your own deep learning projects with real datasets to show off to your colleagues or even potential employers.

Sequential Networks are very exciting to work with and allow for the creation of very intelligent applications. If you’re interested in taking your machine learning skills to the next level, then this course is for you!

Deep Learning with TensorFlow 2.0 [2021]

Build Deep Learning Algorithms with TensorFlow 2.0, Dive into Neural Networks and Apply Your Skills in a Business Case

Created by 365 Careers - Creating opportunities for Business & Finance students

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Students: 18581, Price: $124.99

Students: 18581, Price:  Paid

Data scientists, machine learning engineers, and AI researchers all have their own skillsets. But what is that one special thing they have in common?

They are all masters of deep learning.

We often hear about AI, or self-driving cars, or the ‘algorithmic magic’ at Google, Facebook, and Amazon. But it is not magic - it is deep learning. And more specifically, it is usually deep neural networks – the one algorithm to rule them all.

Cool, that sounds like a really important skill; how do I become a Master of Deep Learning?

There are two routes you can take:

The unguided route – This route will get you where you want to go, eventually, but expect to get lost a few times. If you are looking at this course you’ve maybe been there.

The 365 route – Consider our route as the guided tour. We will take you to all the places you need, using the paths only the most experienced tour guides know about. We have extra knowledge you won’t get from reading those information boards and we give you this knowledge in fun and easy-to-digest methods to make sure it really sticks.

Clearly, you can talk the talk, but can you walk the walk? – What exactly will I get out of this course that I can’t get anywhere else?

Good question! We know how interesting Deep Learning is and we love it! However, we know that the goal here is career progression, that’s why our course is business focused and gives you real world practice on how to use Deep Learning to optimize business performance.

We don’t just scratch the surface either – It’s not called ‘Skin-Deep’ Learning after all. We fully explain the theory from the mathematics behind the algorithms to the state-of-the-art initialization methods, plus so much more.

Theory is no good without putting it into practice, is it? That’s why we give you plenty of opportunities to put this theory to use. Implement cutting edge optimizations, get hands on with TensorFlow and even build your very own algorithm and put it through training!

Wow, that’s going to look great on your resume!

Speaking of resumes, you also get a certificate upon completion which employers can verify that you have successfully finished a prestigious 365 Careers course – and one of our best at that!

Now, I can see you’re bragging a little, but I admit you have peaked my interest. What else does your course offer that will make my resume shine?

Trust us, after this course you’ll be able to fill your resume with skills and have plenty left over to show off at the interview.

  • Of course, you’ll get fully acquainted with Google’ TensorFlow and NumPy, two tools essential for creating and understanding Deep Learning algorithms.

  • Explore layers, their building blocks and activations – sigmoid, tanh, ReLu, softmax, etc.

  • Understand the backpropagation process, intuitively and mathematically.

  • You’ll be able to spot and prevent overfitting – one of the biggest issues in machine and deep learning

  • Get to know the state-of-the-art initialization methods. Don’t know what initialization is? We explain that, too

  • Learn how to build deep neural networks using real data, implemented by real companies in the real world. TEMPLATES included!

  • Also, I don’t know if we’ve mentioned this, but you will have created your very own Deep Learning Algorithm after only 1 hour of the course.

  • It’s this hands-on experience that will really make your resume stand out

This all sounds great, but I am a little overwhelmed, I’m afraid I may not have enough experience.

We admit, you will need at least a little understanding of Python programming but nothing to worry about. We start with the basics and take you step by step toward building your very first (or second, or third etc.) Deep Learning algorithm – we program everything in Python and explain each line of code.

We do this early on and it will give you the confidence to carry on to the more complex topics we cover.

All the sophisticated concepts we teach are explained intuitively. Our beautifully animated videos and step by step approach ensures the course is a fun and engaging experience for all levels.

We want everyone to get the most out of our course, and the best way to do that is to keep our students motivated. So, we worked hard to ensure that students with varying skills are challenged without being overwhelmed. Each lecture builds upon the last and practical exercises mean that you can practice what you’ve learned before moving on to the next step.

And of course, we are available to answer any queries you have. In fact, we aim to answer any and all question within 1 business day. We don’t just chuck you in the pool then head to the bar and let you fend for yourself.

Remember, we don’t just want you to enrol – we want you to complete the course and become a Master of Deep Learning.

OK, awesome! I feel much better about my level of experience now, but we haven’t discussed yours! How do I know you can teach me to become a Master of Deep Learning?

That’s an understandable worry, but it’s one we have no problem removing.

We are 365 Careers and we’ve been creating online courses for ages. We have over 220,000 students and enjoy high ratings for all our Udemy courses. We are a team of experts who are all, at heart, teachers. We believe knowledge should be shared and not just through boring text books but in engaging and fun ways.

We are well aware how difficult it is to build your knowledge and skills in the data science field, it’s so new and has grown so fast that the education sector has struggled to keep up and offer any substantial methods of teaching these topic areas. We wanted to change things – to rock the boat – so we developed our unique teaching style, one that countless students have enjoyed and thrived with.

And between us, we think this course is one of our favourites, so if this is your first time with us, you’re in for a treat. If it’s not and you’ve taken one of our courses before, then, you’re still in for a treat!

I’ve been hurt before though, how can I be sure you won’t let me down?

Easy, with Udemy’s 30-day money back guarantee. We strive for the best and believe that our courses are the best out there. But you know what, everyone is different, and we understand that. So, we have no problem offering this guarantee, we want students who will complete and get the most out of this course. If you are one of the few who finds this course not what you wanted or expected then, get your money back. No questions, no risk, no problem.

Great, that takes a load of my shoulders. What next?

Click on the ‘Buy now’ button and take that first step toward a satisfying data science career and becoming a Master of Deep Learning.

Intro to Deep Learning project in TensorFlow 2.x and Python

Advanced implementation of regression modelling techniques like lasso regression in TensorFlow

Created by Data Science Anywhere Team - Team of Engineer and Developers

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Students: 16739, Price: $44.99

Students: 16739, Price:  Paid

Welcome to the Course Introduction to Deep Learning with TensorFlow 2.0:

In this course, you will learn advanced linear regression technique process and with this, you can be able to build any regression problem. Using this you can solve real-world problems like customer lifetime value, predictive analytics, etc.

What you will Learn

· TensorFlow 2.x

· Google Colab

· Linear Regression

· Gradient Descent Algorithm

· Data Analysis

· Regression

· Feature Engineering and Selection with Lasso Regression.

· Model Evaluation

All the above-mentioned techniques are explained in TensorFlow. In this course, you will work on the Project Customer Revenue (Lifetime value) Prediction using Gradient Descent Algorithm

Problem Statement: A large child education toy company that sells educational tablets and gaming systems both online and in retail stores wanted to analyze the customer data. The goal of the problem is to determine the following objective as shown below.

1. Data Analysis & Pre-processing: Analyse customer data and draw the insights w.r.t revenue and based on the insights we will do data pre-processing. In this module, you will learn the following.

1. Necessary Data Analysis

2. Multi-collinearity

3. Factor Analysis

2. Feature Engineering:

1. Lasso Regression

2. Identify the optimal penalty factor.

3. Feature Selection

3. Pipeline Model

4. Evaluation

We will start with the basics of TensorFlow 2.x to advanced techniques in it. Then we drive into intuition behind linear regression and optimization function like gradient descent.

Tensorflow.JS crash course 2020

This course is for all those people who wants to get a brief idea on Tensorflow.JS in 2020

Created by Muhammad Jamal Butt - Computer Scientist

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Students: 10926, Price: Free

Students: 10926, Price:  Free

This course will give you a brief idea in understanding the flow of Tensorflow JS.  I will go through all the steps needed in creating a basic neural network on the browser. Tensorflow JS will provide us with the basic pre-built function, that will help us in creating and using browser to train 'Machine Learning' based models.

Tensorflow and Keras For Neural Networks and Deep Learning

Master the Most Important Deep Learning Frameworks (Tensorflow & Keras) for Python Data Science

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

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Students: 10279, Price: $89.99

Students: 10279, Price:  Paid

THIS IS A COMPLETE NEURAL NETWORKS & DEEP LEARNING TRAINING WITH TENSORFLOW & KERAS IN PYTHON!

It is a full 7-Hour Python Tensorflow & Keras Neural Network & Deep Learning Boot Camp that will help you learn basic machine learning, neural networks and deep learning  using two of the most important Deep Learning frameworks- Tensorflow and Keras.                         

HERE IS WHY YOU SHOULD ENROLL IN THIS COURSE:

This course is your complete guide to practical machine & deep learning using the Tensorflow & Keras framework in 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 Python courses, we dig deep into the statistical modeling features of Tensorflow & Keras and give you a one-of-a-kind grounding in these frameworks!

DISCOVER 8 COMPLETE SECTIONS ADDRESSING EVERY ASPECT OF PYTHON BASED TENSORFLOW DATA SCIENCE:

• A full introduction to Python Data Science and powerful Python driven framework for data science, Anaconda
• Getting started with Jupyter notebooks for implementing data science techniques in Python
• A comprehensive presentation about Tensorflow & Keras installation and a brief introduction to the other Python data science packages
• Brief introduction to the working of Pandas and Numpy
• The basics of the Tensorflow syntax and graphing environment
• The basics of the Keras syntax
• Machine Learning, Supervised Learning, Unsupervised Learning in the Tensorflow & Keras frameworks
• You’ll even discover how to create artificial neural networks and deep learning structures with Tensorflow & Keras

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

You’ll start by absorbing the most valuable Python Tensorflow and Keras basics and techniques.

I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts.

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

After taking this course, you’ll easily use packages like Numpy, Pandas, and Matplotlib to work with real data in Python along with gaining fluency in Tensorflow and Keras. I will even introduce you to deep learning models such as Convolution Neural network (CNN) !!

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

This course will take students without a prior Python and/or statistics background background from a basic level to performing some of the most common advanced data science techniques using the powerful Python based Jupyter notebooks

It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to data science. 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!

TensorFlow Developer Certificate in 2021: Zero to Mastery

Pass the TensorFlow Developer Certification Exam by Google. Become an AI, Machine Learning, and Deep Learning expert!

Created by Andrei Neagoie - Senior Software Developer / Founder of zerotomastery.io

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Students: 9608, Price: $89.99

Students: 9608, Price:  Paid

Just launched with all modern best practices for working with TensorFlow and passing the TensorFlow Developer Certificate exam! Join a live online community of over 500,000+ students and a course taught by a TensorFlow certified expert. This course will take you from absolute beginner with TensorFlow, to becoming part of Google's TensorFlow Certification Network.

TensorFlow experts earn up to $204,000 USD a year, with the average salary hovering around $148,000 USD according to 2021 statistics. By passing this certificate, which is officially recognized by Google, you will be joining the growing Machine Learning industry and becoming a top paid TensorFlow developer! If you pass the exam, you will also be part of Google's TensorFlow Developer Network where recruiters are able to find you.

The goal of this course is to teach you all the skills necessary for you to go and pass this exam and get your TensorFlow Certification from Google so you can display it on your resume, LinkedIn, Github and other social media platforms to truly make you stand out.

Here is a full course breakdown of everything we will teach (yes, it's very comprehensive, but don't be intimidated, as we will teach you everything from scratch!):

This course will be very hands on and project based. You won't just be staring at us teach, but you will actually get to experiment, do exercises, and build machine learning models and projects to mimic real life scenarios. Most importantly, we will show you what the TensorFlow exam will look like for you. By the end of it all, you will develop skillsets needed to develop modern deep learning solutions that big tech companies encounter.


0 — TensorFlow Fundamentals

  • Introduction to tensors (creating tensors)

  • Getting information from tensors (tensor attributes)

  • Manipulating tensors (tensor operations)

  • Tensors and NumPy

  • Using @tf.function (a way to speed up your regular Python functions)

  • Using GPUs with TensorFlow


1 — Neural Network Regression with TensorFlow

  • Build TensorFlow sequential models with multiple layers

  • Prepare data for use with a machine learning model

  • Learn the different components which make up a deep learning model (loss function, architecture, optimization function)

  • Learn how to diagnose a regression problem (predicting a number) and build a neural network for it

2 — Neural Network Classification with TensorFlow

  • Learn how to diagnose a classification problem (predicting whether something is one thing or another)

  • Build, compile & train machine learning classification models using TensorFlow

  • Build and train models for binary and multi-class classification

  • Plot modelling performance metrics against each other

  • Match input (training data shape) and output shapes (prediction data target)


3 — Computer Vision and Convolutional Neural Networks with TensorFlow

  • Build convolutional neural networks with Conv2D and pooling layers

  • Learn how to diagnose different kinds of computer vision problems

  • Learn to how to build computer vision neural networks

  • Learn how to use real-world images with your computer vision models

4 — Transfer Learning with TensorFlow Part 1: Feature Extraction

  • Learn how to use pre-trained models to extract features from your own data

  • Learn how to use TensorFlow Hub for pre-trained models

  • Learn how to use TensorBoard to compare the performance of several different models

5 — Transfer Learning with TensorFlow Part 2: Fine-tuning

  • Learn how to setup and run several machine learning experiments

  • Learn how to use data augmentation to increase the diversity of your training data

  • Learn how to fine-tune a pre-trained model to your own custom problem

  • Learn how to use Callbacks to add functionality to your model during training

6 — Transfer Learning with TensorFlow Part 3: Scaling Up (Food Vision mini)

  • Learn how to scale up an existing model

  • Learn to how evaluate your machine learning models by finding the most wrong predictions

  • Beat the original Food101 paper using only 10% of the data

7 — Milestone Project 1: Food Vision

  • Combine everything you've learned in the previous 6 notebooks to build Food Vision: a computer vision model able to classify 101 different kinds of foods. Our model well and truly beats the original Food101 paper.

8 — NLP Fundamentals in TensorFlow

  • Learn to:

    • Preprocess natural language text to be used with a neural network

    • Create word embeddings (numerical representations of text) with TensorFlow

    • Build neural networks capable of binary and multi-class classification using:

      • RNNs (recurrent neural networks)

      • LSTMs (long short-term memory cells)

      • GRUs (gated recurrent units)

      • CNNs

  • Learn how to evaluate your NLP models

9 — Milestone Project 2: SkimLit

  • Replicate a the model which powers the PubMed 200k paper to classify different sequences in PubMed medical abstracts (which can help researchers read through medical abstracts faster)

10 — Time Series fundamentals in TensorFlow

  • Learn how to diagnose a time series problem (building a model to make predictions based on data across time, e.g. predicting the stock price of AAPL tomorrow)

  • Prepare data for time series neural networks (features and labels)

  • Understanding and using different time series evaluation methods

    • MAE — mean absolute error

  • Build time series forecasting models with TensorFlow

    • RNNs (recurrent neural networks)

    • CNNs (convolutional neural networks)

11 — Milestone Project 3: (Surprise)

  • If you've read this far, you are probably interested in the course. This last project will be good.. we promise you, so see you inside the course ;)

TensorFlow is growing in popularity and more and more job openings are appearing for this specialized knowledge. As a matter of fact, TensorFlow is outgrowing other popular ML tools like PyTorch in job market. Google, Airbnb, Uber, DeepMind, Intel, IBM, Twitter, and many others are currently powered by TensorFlow. There is a reason these big tech companies are using this technology and you will find out all about the power that TensorFlow gives developers.

We guarantee you this is the most comprehensive online course on passing the TensorFlow Developer Certificate to qualify you as a TensorFlow expert. So why wait? Make yourself stand out by becoming a Google Certified Developer and advance your career.

See you inside the course!

TensorFlow 2.0 Practical Advanced

Master Tensorflow 2.0, Google’s most powerful Machine Learning Library, with 5 advanced practical projects

Created by Dr. Ryan Ahmed, Ph.D., MBA - Professor & Best-selling Udemy Instructor, 200K+ students

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Students: 4940, Price: $99.99

Students: 4940, Price:  Paid

Google has recently released TensorFlow 2.0 which is Google’s most powerful open source platform to build and deploy AI models in practice. Tensorflow 2.0 release is a huge win for AI developers and enthusiast since it enabled the development of super advanced AI techniques in a much easier and faster way.

The purpose of this course is to provide students with practical knowledge of building, training, testing and deploying Advanced Artificial Neural Networks and Deep Learning models using TensorFlow 2.0 and Google Colab. This course will cover advanced, state-of-the–art AI models implementation in TensorFlow 2.0 such as DeepDream, AutoEncoders, Generative Adversarial Networks (GANs), Transfer Learning using TensorFlow Hub, Long Short Term Memory (LSTM) Recurrent Neural Networks and many more. The applications of these advanced AI models are endless including new realistic human photographs generation, text translation, image de-noising, image compression, text-to-image translation, image segmentation, and image captioning.

The global AI and machine learning technology sectors are expected to grow from $1.4B to $8.8B by 2022 and it is predicted that AI tech sector will create around 2.3 million jobs by 2020. The technology is progressing at a massive scale and being adopted in almost every sector. The course provides students with practical hands-on experience in training Advanced Artificial Neural Networks using real-world dataset using TensorFlow 2.0 and Google Colab. This course covers several technique in a practical manner, the projects include but not limited to:

  1. Develop, train, and test State-of-the art DeepDream algorithm to create AI-based art masterpieces!

  2. Implement revolutionary Generative Adversarial Networks known as GANs to generate brand new images.

  3. Develop Long Short Term Memory (LSTM) networks to generate new Shakespeare-style text!

  4. Deploy AI models in practice using TensorFlow 2.0 Serving.

  5. Apply Auto-Encoders to perform image compression and de-noising.

  6. Apply transfer learning to transfer knowledge from pre-trained networks to classify new images using TensorFlow 2.0 Hub.

The course is targeted towards students wanting to gain a fundamental understanding of how to build, train, test and deploy advanced models in Tensorflow 2.0. Basic knowledge of programming and Artificial Neural Networks is recommended. Students who enroll in this course will master Advanced AI and Deep Learning techniques and can directly apply these skills to solve real world challenging problems.

TensorFlow 2.0 Practical

Master Tensorflow 2.0, Google’s most powerful Machine Learning Library, with 10 practical projects

Created by Dr. Ryan Ahmed, Ph.D., MBA - Professor & Best-selling Udemy Instructor, 200K+ students

"]

Students: 4795, Price: $99.99

Students: 4795, Price:  Paid

Artificial Intelligence (AI) revolution is here and TensorFlow 2.0 is finally here to make it happen much faster! TensorFlow 2.0 is Google’s most powerful, recently released open source platform to build and deploy AI models in practice.

AI technology is experiencing exponential growth and is being widely adopted in the Healthcare, defense, banking, gaming, transportation and robotics industries. The purpose of this course is to provide students with practical knowledge of building, training, testing and deploying Artificial Neural Networks and Deep Learning models using TensorFlow 2.0 and Google Colab.

The course provides students with practical hands-on experience in training Artificial Neural Networks and Convolutional Neural Networks using real-world dataset using TensorFlow 2.0 and Google Colab. This course covers several technique in a practical manner, the projects include but not limited to:

(1) Train Feed Forward Artificial Neural Networks to perform regression tasks such as sales/revenue predictions and house price predictions

(2) Develop Artificial Neural Networks in the medical field to perform classification tasks such as diabetes detection.

(3) Train Deep Learning models to perform image classification tasks such as face detection, Fashion classification and traffic sign classification.

(4) Develop AI models to perform sentiment analysis and analyze customer reviews.

(5) Perform AI models visualization and assess their performance using Tensorboard

(6) Deploy AI models in practice using Tensorflow 2.0 Serving

The course is targeted towards students wanting to gain a fundamental understanding of how to build and deploy models in Tensorflow 2.0. 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 course will master AI and Deep Learning techniques and can directly apply these skills to solve real world challenging problems using Google’s New TensorFlow 2.0.

Machine Learning in JavaScript with TensorFlow.js

Master machine learning with JavaScript and TensorFlowJS. Add artificial intelligence to websites, Node.js and web apps!

Created by tech.courses team - Learn by Doing - Technical Courses, Professionally Delivered

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Students: 3154, Price: $129.99

Students: 3154, Price:  Paid

Updated for 2021!

Interested in using Machine Learning in JavaScript applications and websites? Then this course is for you!

This is the tutorial you've been looking for to become a modern JavaScript machine learning master in 2021. It doesn’t just cover the basics, by the end of the course you will have advanced machine learning knowledge you can use on you resume. From absolute zero knowledge to master - join the TensorFlow.js revolution.

This course has been designed by a specialist team of software developers who are passionate about using JavaScript with Machine Learning. We will guide you through complex topics in a practical way, and reinforce learning with in-depth labs and quizzes.

Throughout the course we use house price data to ask ever more complicated questions; “can you predict the value of this house?”, “can you tell me if this house has a waterfront?”, “can you classify it as having 1, 2 or 3+ bedrooms?”. Each example builds on the one before it, to reinforce learning in easy and steady steps.

Machine Learning in TensorFlow.js provides you with all the benefits of TensorFlow, but without the need for Python. This is demonstrated using web based examples, stunning visualisations and custom website components.

This course is fun and engaging, with Machine Learning learning outcomes provided in bitesize topics:

  • Part 1 - Introduction to TensorFlow.js

  • Part 2 - Installing and running TensorFlow.js

  • Part 3 - TensorFlow.js Core Concepts

  • Part 4 - Data Preparation with TensorFlow.js

  • Part 5 - Defining a model

  • Part 6 - Training and Testing in TensorFlow.js

  • Part 7 - TensorFlow.js Prediction

  • Part 8 - Binary Classification

  • Part 9 - Multi-class Classification

  • Part 10 - Conclusion & Next Steps

As a bonus, for every student, we provide you with JavaScript and HTML code templates that you can download and use on your own projects.

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

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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

Tensorflow Deep Learning – Data Science in Python

Tensorflow Deep Learning Python : Tensorflow Neural Network Training : Tensorflow Models - Android Java : Tensorflow C#

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

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Students: 2403, Price: $99.99

Students: 2403, Price:  Paid

Complete Tensorflow Mastery For Machine Learning & Deep Learning in Python

THIS IS A COMPLETE DATA SCIENCE TRAINING WITH TENSORFLOW IN PYTHON!

It is a full 7-Hour Python Tensorflow Data Science Boot Camp that will help you learn statistical modelling, data visualization, machine learning and basic deep learning  using the Tensorflow framework in Python..                         

HERE IS WHY YOU SHOULD ENROLL IN THIS COURSE:

This course is your complete guide to practical data science using the Tensorflow framework in Python..

This means, this course covers all the aspects of practical data science with 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 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 is revolutionizing Deep Learning...

By storing, filtering, managing, and manipulating data in Python 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 PYTHON 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 Python courses, we dig deep into the statistical modeling features of Tensorflow and give you a one-of-a-kind grounding in Python based Tensorflow Data Science!

DISCOVER 8 COMPLETE SECTIONS ADDRESSING EVERY ASPECT OF PYTHON BASED TENSORFLOW DATA SCIENCE:

• A full introduction to Python Data Science and powerful Python driven framework for data science, Anaconda
• Getting started with Jupyter notebooks for implementing data science techniques in Python
• A comprehensive presentation about Tensorflow installation and a brief introduction to the other Python data science packages
• Brief introduction to the working of Pandas and Numpy
• The basics of the Tensorflow syntax and graphing environment
• Statistical modelling with Tensorflow
• Machine Learning, Supervised Learning, Unsupervised Learning in the Tensorflow framework
• You’ll even discover how to create artificial neural networks and deep learning structures with Tensorflow

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

You’ll start by absorbing the most valuable Python Tensorflow Data Science basics and techniques.

I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts.

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

After taking this course, you’ll easily use packages like Numpy, Pandas, and Matplotlib to work with real data in Python along with gaining fluency in Tensorflow. I will even introduce you to deep learning models such as Convolution Neural network (CNN) !!

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

This course will take students without a prior Python and/or statistics background background from a basic level to performing some of the most common advanced data science techniques using the powerful Python based Jupyter notebooks

It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to data science. 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!

#tensorflow #python #deeplearning #android #java #neuralnetwork  #models

Convolutional Neural Networks with TensorFlow in Python

Advanced neural networks: Master Computer Vision with Convolutional Neural Networks (CNN) and Deep Learning

Created by 365 Careers - Creating opportunities for Business & Finance students

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Students: 818, Price: $89.99

Students: 818, Price:  Paid

Are you a Deep Learning enthusiast who is now looking for their next challenge?

Are you interested in the field of Computer Vision and the ability of machines to extract insightful information from visuals and images?

Do you want to learn a valuable skill to put yourself ahead of the competition in this AI-driven world?

If you answered with “yes” to any of these questions, you have come to the right place and at the right time!

Here are 5 reasons this is the right course for you:

  1. We have 1,170,000 students on Udemy and we know how to teach a complex topic in an easy to understand way

  2. It contains numerous practical exercises

  3. A real-life case study with 16,000 images

  4. Save time – our course will get you there faster than the average courses on the topic

  5. Notebook files, course notes, quiz questions, practice materials – all materials are inside the course

This course is a fantastic training opportunity to help you gain insights into the rapidly expanding field of Machine Learning and Computer Vision through the use of Convolutional Neural Networks.

Convolutional Neural Networks, or CNNs in short, are a subtype of deep neural networks that are extensively used in the field of Computer Vision. These networks specialize in inferring information from spatial-structure data to help computers gain high-level understanding from digital images and videos. That can be as simple a task as classifying an image to be a dog or a cat, but it can also explode in complexity as is the case with self-driving cars, for example.

This is where most of the active Machine Learning research is concentrated right now, and CNNs are a crucial part of it. So, it is high time to up your game and master this piece of the Deep Learning puzzle.

To do just that, we have devised this wonderful and engaging course for you. Although a general understanding of TensorFlow and the main deep learning concepts is required, we will start from the CNNs basics and build our way to proficiency. Moreover, we are firm believers that practice makes perfect, that’s why this course offers a comprehensive practical example of a real-world project. What’s more, it contains plenty of exercises, homework, downloadable files and notebooks, as well as quiz questions and course notes.

We’ll start this course by taking a look at Kernels in the context of image processing. Kernels are an essential tool for working with and understanding Convolutional Neural Networks. We’ll explore how to achieve different image transformations and help you understand the role of the mathematical operation of convolution in this process. This will be the basis for our next topic - convolutional layers.

Armed with all that knowledge, we will introduce the main subject of the course: Convolutional Neural Networks. Here, we’ll discuss intriguing concepts such as feature maps and pooling. In addition, we’ll inspect how such a network transforms the dimensions of the tensors.

Then, what follows is a short and optional neural networks revision. CNNs are simply a subtype of deep neural networks, so a general knowledge of NNs is required. That’s why we’ll revise the basics: activation functions, early stopping, and optimizers.

Once we’ve covered all that, you will have the minimum required knowledge to start putting all this theory to practice – by building your first Convolutional Neural Network.

Working on the MNIST dataset, we’ll help you grasp the general workflow of creating a CNN architecture and build one from scratch. You are going to train it to recognize handwritten digits – a very useful tool in the real world. At this point, you will get the hands-on opportunity to tinker and change the network and see the results for yourself.

And we won’t stop at creating the CNNs. We will also spend a good amount of time exploring them through TensorBoard – the go-to visualization and logging tool when working with TensorFlow. This will make your journey and experimentation in the field more straightforward and definitely more memorable. Neural networks are notorious for their difficult interpretation, so we will examine the Confusion Matrix as a tool to help you understand and interpret the results of your networks. Finally, we’ll show you how to easily tune the hyperparameters of your networks.

But there’s more.

We will show you how to master 3 common techniques to improve the performance of your models. In fact, you will have the opportunity to apply those techniques to the networks we create for the next practical section.

You heard that right! The idea of this course is to give you the real CNN experience. We will have an enormous practical exercise so you can work on a real-world project.

To do that, we’ve created our very own custom data set that comes from the fashion industry. It consists of more than 16,000 images of trousers, jeans, shoes, glasses, and sunglasses. And we will be using these for numerous practical examples and problems. We’ve devised a task to classify the different items with a corresponding label. Not only that, but we will also determine other characteristics, such as the items’ subtype and gender. Given the nature of these, we will be able to try out different techniques to achieve our goal and compare how these approaches fare against each other. You’ll get a taste of the real-world challenges of solving such a task, and gain experience with a real project that you can later add to your portfolio.

Finally, to cap it all off, we end this course with a review of the timeline of Convolutional Neural Networks professional research. We will dive into the workings of some popular CNN architectures, and all-stars like AlexNet, GoogLeNet, as well as ResNet will all make an appearance.

By the end of this course, you will be completely equipped with all the tools you need to confidently work on CNN projects!

We, at the 365 Data Science Team are committed to providing only the highest quality content to you – our students. That’s why we have teamed up with a true industry expert – Iskren Vankov. Iskren is a very capable Software developer and Computer Scientist with a Bachelor’s degree in Computer Science and Physics from The University of Edinburgh, and a Master’s degree in Computer Science from The University of Oxford. Iskren has also been engaged in Deep Learning programming for more than 5 years with a focus on Recurrent Neural Networks.

As with all of our courses, you have a 30-day money-back guarantee, if at some point you decide that the training isn’t the best fit for you.

What’s more, the course comes with plenty of exercises, homework, downloadable files, quiz questions, and course notes. Everything you need for a perfect learning experience.

So, what are you waiting for?

Click the ‘Buy now’ button and let’s explore CNNs together!

Googles’ Teachable Machine – No Math, No Code

Classification without any code

Created by Mosin hasan - Engineer - Computer Science

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Students: 609, Price: Free

Students: 609, Price:  Free

Google's Teachable Machine Project

Train a computer to recognize your own images, sounds, & poses.

A fast, easy way to create machine learning models for your sites, apps, and more – no expertise or coding required.

This course has absolutely zero math so anyone who has some basic knowledge can do this course.

This course also has almost zero code so if you do not have strong background on programming still you can do very much everything in this course

6 Projects like mask detection, Yoga Pose detection.

In this course, we are going to see

1. What is Teachable Machine learning?

2. Teachable machine learning applications.

3. 2 Projects on Image Classification - Mask Detection and Indian Currency Identification.

Indian Currency note: total 5 types of currency 20,50,100,200,500.

4. 2 Project on Audio Classification - Men in Black actor audio classification and Word Classification

5. 2 Project on Pose - Bad Posture and Yoga Pose detection.

Yoga Pose Detection has Mountain and Triangle pose detection.

6. Lastly deployment of models on P5, Keras and Android using Tensorflow.js and tensorflowlite.

One More project for Mute and Deaf people which can help them.

Total 6 Project, deployment on web and android app.

Just jump into it and see

Hope you will enjoy the course.

and if time permits I will be adding few quizzes to it so you can check your knowledge about teachable machine.

Supervised Learning for AI with Python and Tensorflow 2

Uncover the Concepts and Techniques to Build and Train your own Artificial Intelligence Models

Created by Jeremy Richard Lai Hong - Data Scientist and Software Engineer

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Students: 218, Price: $89.99

Students: 218, Price:  Paid

Gain a deep understanding of Supervised Learning techniques by studying the fundamentals and implementing them in NumPy.

Gain hands-on experience using popular Deep Learning frameworks such as Tensorflow 2 and Keras.

Section 1 - The Basics:

- Learn what Supervised Learning is, in the context of AI

- Learn the difference between Parametric and non-Parametric models

- Learn the fundamentals: Weights and biases, threshold functions and learning rates

- An introduction to the Vectorization technique to help speed up our self implemented code

- Learn to process real data: Feature Scaling, Splitting Data, One-hot Encoding and Handling missing data

- Classification vs Regression

Section 2 - Feedforward Networks:

- Learn about the Gradient Descent optimization algorithm.

- Implement the Logistic Regression model using NumPy

- Implement a Feedforward Network using NumPy

- Learn the difference between Multi-task and Multi-class Classification

- Understand the Vanishing Gradient Problem

- Overfitting

- Batching and various Optimizers (Momentum, RMSprop, Adam)

Section 3 - Convolutional Neural Networks:

- Fundamentals such as filters, padding, strides and reshaping

- Implement a Convolutional Neural Network using NumPy

- Introduction to Tensorfow 2 and Keras

- Data Augmentation to reduce overfitting

- Understand and implement Transfer Learning to require less data

- Analyse Object Classification models using Occlusion Sensitivity

- Generate Art using Style Transfer

- One-Shot Learning for Face Verification and Face Recognition

- Perform Object Detection for Blood Stream images

Section 4 - Sequential Data

- Understand Sequential Data and when data should be modeled as Sequential Data

- Implement a Recurrent Neural Network using NumPy

- Implement LSTM and GRUs in Tensorflow 2/Keras

- Sentiment Classification from the basics to the more advanced techniques

- Understand Word Embeddings

- Generate text similar to Romeo and Juliet

- Implement an Attention Model using Tensorflow 2/Keras