7 Completely FREE TensorFlow Online Courses

Time to Complete-2 months

Rating-NA

Best For-Intermediate

This Introduction to TensorFlow Lite is a completely free course to learn Tensorflow. In this course, there are 4 lessons where you will learn how to deploy TF Lite models in various apps.

The course begins with the Introduction to TensorFlow Lite and covers Quantization, Post-Training Quantization with TF Lite, Post-Training Decision Tree, TF Lite Delegates, Testing your TFLite Models in Python, etc. You will also learn how to create a simple linear regression model using tf.keras.

After that, you will learn how to add TF lite on Android and iOS with Swift and understand App Architecture. At the end of this course, you will learn about TF Lite on IoT, Raspberry Pi, Coral, etc. Overall, this is a practical course where you will deploy TF Lite models in various apps including-

An app that classifies images of cats and dogs.

• Image Classification App.
• Objection Detection App.
• Speech Recognition App.

Drawback

• Doesn’t provide a certificate of completion.

Who Should Enroll?

Those who are comfortable programming in Python, Swift, Android, and Linux platforms, depending on which platform you plan to deploy on.

Interested to Enroll?

If yes, then check out all details here- Introduction to TensorFlow Lite

Time to Complete-2 months

Rating-NA

Best For-Intermediate

This Intro to TensorFlow for Deep Learning is another Best Free Tensorflow Course. In this course, there are 10 lessons. This lesson starts with an introduction to machine learning and an interview with Sebastian Thrun, a co-founder at Udacity.

This course will also discuss the Fashion MNIST dataset and how to use a deep neural network that learns how to classify images from the Fashion MNIST dataset.

You will also learn about Convolutional Neural Networks and their layers. After that, you will learn how CNN handles color images of cats and dogs.

This is a detailed lesson where you will learn some other essential topics such as transfer learning, Time Series Forecasting, Natural language processing, Recurrent Neural Networks, and Tensorflow Lite.

Drawback-

• This course doesn’t provide a certificate.

Who Should Enroll?

• Those who have previous Python and Linear Algebra knowledge.

Interested to Enroll?

If yes, then check out all details here-Intro to TensorFlow for Deep Learning

Time to Complete-1hr 1min

Rating-4.3/5

Best For-Intermediate

This “Tensorflow 2.0 | Recurrent Neural Networks, LSTMs, GRUs” Free course is available on Udemy. This is a short course where you will learn about Recurrent Neural networks. And this course is not for beginners.

This course has 3 sections. The first section is an introduction to the course. In the next section, you will learn the RNN basics, Backpropagation, and how to solve a recurrent neural network problem.

The last section is optional and covers NumPy basics. Overall, this is a good course for understanding the basics of RNN with Tensorflow.

Drawback-

• Not a detailed course on RNN using Tensorflow.
• Very basic course.

Who Should Enroll?

• Those who are familiar with Python, NumPy, Tensorflow, or Keras.

Interested to Enroll?

If yes, then check out all details here-  Tensorflow 2.0 | Recurrent Neural Networks, LSTMs, GRUs

Time to Complete-31 hours

Rating-4.7/5

Best For-Intermediate

Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning course is Free to Audit. To enroll in this course for free, click on the “Enroll for Free” button. A new popup window will appear where they ask you to choose the subscription period. On the same popup see the bottom left corner. You will see the option “Audit the Course”. Click on this link and you will be redirected to the course material free of cost.

This course has a 4-week learning plan. In the first week, there is a conversation with Andrew Ng and a basic introduction to the course.

In week 2, you will learn computer vision basics and understand the structure of the Fashion MNIST Dataset. This is a practical section where you will learn how to code a Computer Vision Neural Network.

The next week, you will learn about Convolutional Neural networks and their layers. And the last week is completely practical where you will understand how to implement a CNN and understand ImageDataGenerator.

Drawback-

• In Free to Audit mode, the assignments and quizzes are locked. And you will not receive a certificate.

Who Should Enroll?

• Those who have previous Python experience.

Interested to Enroll?

If yes, then check out all details here- Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

Time to Complete-26 hours

Rating-4.9/5

Best For-Intermediate

This “Getting started with TensorFlow 2” is another Free to Audit course. To access the course material for Free, press-> Enroll for Free and then press-> Audit the Course.

This course has a 5-week study plan. The first week covers the introduction to TensorFlow 2 and TensorFlow in Google Colab. In the next week, you will learn sequential model API, feedforward neural network, CNN, and much more.

In weeks 3 and 4, you will learn model validation, model regularization, Tensorflow Hub Modules, and saving and loading models.

The last week is a Capstone project which is locked in free-to-audit mode. Overall, this is a practical course, where you will work on hands-on exercises.

Drawback-

• You can’t access the quizzes and projects in free-to-audit mode.
• And you will not receive a certificate.

Who Should Enroll?

• Those who have previous Python knowledge and basics of Machine Learning.

Interested to Enroll?

If yes, then check out all details here- Getting started with TensorFlow 2

Time to Complete-13 hours

Rating-4.5/5

Best For-Advanced

You can Audit the Course for free. This “End-to-End Machine Learning with TensorFlow on GCP” course has a 3-week study plan. In the first week, you will understand why to use TensorFlow and Cloud ML Engine for building effective Machine Learning solutions. Week 1 also has one lab assignment where you have to explore the dataset.

In the next week, you will learn how to create a dataset and build the model. In the last week, you will learn about the Cloud AI platform, BigQuery ML, and how to deploy and predict with the Cloud AI platform.

Overall, this is a good hands-on course to understand machine learning end-to-end with Tensorflow.

Drawback-

• Quizzes and Certificates are locked in free-to-audit mode.

Who Should Enroll?

• Those who have previous SQL, Python, and TensorFlow knowledge.

Interested to Enroll?

If yes, then check out all details here- End-to-End Machine Learning with TensorFlow on GCP

Time to Complete-5 weeks

Rating-NA

Best For-Intermediate

The course material of this “Deep Learning with Tensorflow” course is available freely. But for the certificate, you have to pay. In this course, there are six modules. The first module covers the Tensorflow Introduction, Linear regression, Logistic regression, and intro to deep learning.

The next module covers CNN basics and its architecture with practical exercises. In module 3, you will learn Recurrent Neural Networks (RNNs) and the Basics of LSTM.

Restricted Boltzmann Machines (RBMs) and Autoencoders are covered in modules 4 and 5. In the last module, you will learn Scaling.

Drawback-

• Doesn’t provide a certificate.

Who Should Enroll?

• Those who already know Python, Machine Learning, and Deep Learning basics.

Interested to Enroll?

If yes, then check out all details here- Deep Learning with Tensorflow