TensorFlow is an open-source library for performing machine learning and deep learning tasks. TensorFlow supports multiple languages, but Python is the most commonly used programming language to implement TensorFlow. Essentially, this library was developed to perform large numerical computations with ease, but recently several advanced deep learning operations have been performed with the help of TensorFlow, such as digit classification, image recognition, natural language processing tasks, and more.

Besides, TensorFlow comprises a flexible ecosystem consisting of various tools and resources that enable developers to quickly build and deploy machine learning applications. TensorFlow was developed by Google and is currently the most well-known deep learning and machine learning library used by developers and researchers globally.

With multiple functionalities provided by TensorFlow, this open-source platform has become the core of most machine learning and deep learning applications, including Google’s products to improve search engine performance, language translation, image captioning and recommendations, and more. Therefore, aspirants aiming for a career in machine learning and deep learning should upskill themselves with TensorFlow skills.

Although selecting the right online course may seem tricky, this article provides some of the top trending courses for students and professionals to develop their practical and theoretical knowledge about TensorFlow from some of the most well-recognized institutions worldwide.


Related reading: Top 9 Machine Learning Courses



1. Deeplearning.AI TensorFlow Developer Professional Certificate – Coursera

Deeplearning.AI TensorFlow Developer Professional Certificate – Coursera

Deeplearing.AI is a global brand created by the pioneer in artificial intelligence, Andrew Ng. This course is provided on Coursera by Deep learning. AI. In addition, it is one of the most-in-demand courses to learn TensorFlow with top reviews on the platform. 

The certificate program provides all the essential applied machine learning skills with TensorFlow to build and train robust machine learning models. However, there is a focus on building the hands-on experiences of the learners with various tools to build scalable AI-enabled applications using TensorFlow. 

Essentially, one can be assured to gain a lot from the program as learners will understand the best practices for TensorFlow, learn to build NLP and computer vision applications with real-world data, explore various strategies to prevent overfitting, and more. 

There are a total of 16 Python programming assignments using TensorFlow that targets areas like building and training a neural network from scratch, improving the network performance with the help of convolutions, building a learning model to respond to human speech and teaching machines to understand, and building a model to process texts and represent sentences. 

Moreover, the course curriculum is well-developed for exposing learners to different AI concepts with TensorFlow, which is essential to prepare for the official Google TensorFlow Certification exam. 

The course contents are:

  • Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
  • Convolutional Neural Networks in TensorFlow
  • Natural Language Processing in TensorFlow
  • Sequences, Time Series, and Prediction


Instructor: Laurence Moroney

Level: Intermediate

Duration: 4 months

User Review: 4.7/5

No. of Reviews: 18,028

Price: Free Enrollment (Charges applicable for certification)

2. Machine Learning with TensorFlow on Google Cloud Specialization – Coursera

Machine Learning with TensorFlow on Google Cloud Specialization – Coursera

Perhaps, this certification course needs no introduction. This program is offered by Google, an industry leader in machine learning and creators of the TensorFlow library. One can find this program listed under TensorFlow courses on Coursera

Learning TensorFlow becomes more manageable with this course, as the concepts are covered from the essential part of understanding which problems machine learning can solve and different phases involved in the development. Furthermore, the critical concepts on neural networks supervised learning, and the use of gradient descent is covered in-depth. Besides, the learners will have exposure to different software development environments in artificial intelligence, where they will learn to write distributed machine learning models that scale using TensorFlow.

Additionally, scaling out the training of machine learning models and achieving higher performance on prediction tasks are covered with appropriate hands-on examples. Finally, the learners will learn to incorporate appropriate parameters for the models, convert raw data to insights and experiment with end-to-end ML model building with the right strategy for model training optimization using the Google Cloud platform.

The final components of the course are to infuse learners with the knowledge of best practices for building Responsible AI, machine learning on the cloud, leveraging Google Cloud Platform tools and environments, and understanding implementation of AutoML for machine learning model building without writing a single line of code.

The course contents are:

  • How does Google do Machine Learning?
  • Launching into Machine Learning
  • Introduction to TensorFlow
  • Feature Engineering
  • Art and Science of Machine Learning


Instructor: Google Cloud Training

Level: Intermediate

Duration: 5 months

User Review: 4.6/5

No. of Reviews: 8209

Price: Free Enrollment (Charges applicable for certification)

3. Deep Learning with TensorFlow 2.0 (2022) – Udemy

Deep Learning with TensorFlow 2.0 (2022) – Udemy

Udemy offers this program on TensorFlow that covers concepts from the basics of Python programming to the most advanced concepts on model building. The program is designed to get acquainted with TensorFlow and NumPy, which are essential for deep learning applications.

Besides, the learners will explore the concepts on layers, building blocks, and activations, sigmoid, tanh, ReLu, and softmax. Furthermore, the program offers concepts on backpropagation and spotting and preventing overfitting, including initialization methods. 

Although most of the basic concepts of Python are covered, it is more beneficial to have some practical experience working with Python programming basics to gain more out of the program. Therefore, the course emphasizes building the learners’ practical skills on deep learning and machine learning application building and understanding the necessary terminologies like underfitting, overfitting, training, validation, testing, early stopping, and more. 

Finally, the learners will work on deep learning algorithms from scratch using Python and TensorFlow library and its tools. 

The course contents are:

  • Introduction to Neural Networks
  • Setting up the Working Environment
  • Building The First Machine Learning Algorithm
  • TensorFlow Introduction
  • Introduction to Deep Neural Networks
  • Backpropagation
  • Overfitting and Initialization
  • Gradient Descent and Learning Rates
  • Preprocessing
  • The MNIST Example
  • Business Case
  • Linear Algebra Fundamentals
  • Conclusion
  • Bonus Lecture


Instructor: 365 Careers Team

Level: Beginner/Intermediate

Duration: 5 hours 55 minutes

User Review: 4.7/5

No. of Reviews: 2170

Price: $45.6

4. TensorFlow Developer Certificate in 2022: Zero to Mastery – Udemy

TensorFlow Developer Certificate in 2022- Zero to Mastery – Udemy

This program is among the highly rated online courses on TensorFlow on Udemy. The TensorFlow tutorials follow a hands-on approach with multiple exercises and learning to build machine learning models with real-life scenarios. Besides, the concepts offered in the course are ideal for preparing for the industry-based professional certification offered by Google. 

By the end of the program, one can expect mastery in building machine learning solutions for tools and applications, building models for image recognition, object detection, text recognition tasks, and working with deep learning models for time series forecasting. In addition, the learners will gain proficiency with programming and implementing the latest TensorFlow 2 for advanced models.

The course curriculum includes:

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

Neural Network Regression with TensorFlow

  • Building TensorFlow sequential models with multiple layers
  • Preparing the data using a machine learning model
  • Learning various components that make up a deep learning model (loss function, architecture, optimization function)
  • Learning how to diagnose a regression problem (predicting a number) and build a neural network for it

Neural Network Classification with TensorFlow

  • Learning 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 modeling performance metrics against each other
  • Matching input (training data shape) and output shapes (prediction data target)

Computer Vision and Convolutional Neural Networks with TensorFlow

  • Build convolutional neural networks with Conv2D and pooling layers
  • Learning how to diagnose different kinds of computer vision problems
  • Learning how to build computer vision neural networks
  • Learning how to use real-world images with your computer vision models

Transfer Learning with TensorFlow Part 1: Feature Extraction

  • Learning to use pre-trained models to extract features from your data
  • Learning to use TensorFlow Hub for pre-trained models
  • Learning to use TensorBoard to compare the performance of several different models

Transfer Learning with TensorFlow Part 2: Fine-tuning

  • Learn how to set up 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 custom problem
  • Learn how to use Callbacks to add functionality to your model during training

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

  • Learn how to scale up an existing model
  • Learn how to evaluate your machine learning models by finding the most wrong predictions
  • Beat the original Food101 paper using only 10% of the data

Milestone Project 1: Food Vision

  • Combining the concepts learned in the previous 6 notebooks to build Food Vision: a computer vision model can classify 101 different kinds of foods.

NLP Fundamentals in TensorFlow

  • Preprocess natural language text to be used with a neural network
  • Creating word embedding (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
  • Learning to evaluate your NLP models

Milestone Project 2: SkimLit

  • Replicating 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)

Time Series fundamentals in TensorFlow

  • Learning 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)
  • Milestone Project


Instructor: Andrei Neagoie, Daniel Bourke

Level: Beginner/Intermediate

Duration: 63 hours 27 minutes

User Review: 4.7/5

No. of Reviews: 3125

Price: $45.6

5. Deep Learning with TensorFlow – edX

Deep Learning with TensorFlow – edX

edX is another popular platform that offers tremendous value-for-money courses from top-tier universities and organizations worldwide. IBM offers this certification program on the platform that covers the fundamentals of TensorFlow concepts, including the main functions, operations, and the execution pipeline. 

In addition, the students can explore concepts on how TensorFlow is used for curve fitting and regression problems, classification, and minimization of error functions. Advanced concepts range from understanding the deep architectures such as CNN, recurrent networks, and Autoencoders. Besides, the learners will learn to use TensorFlow for backpropagation to fine-tune the weights and biases when training a neural network. 

The course contents are:

Module 1 – Introduction to TensorFlow

  • HelloWorld with TensorFlow
  • Linear Regression
  • Nonlinear Regression
  • Logistic Regression

Module 2 – Convolutional Neural Networks (CNN)

  • CNN Application
  • Understanding CNNs

Module 3 – Recurrent Neural Networks (RNN)

  • Intro to RNN Model
  • Long Short-Term Memory (LSTM)

Module 4 – Restricted Boltzmann Machine

  • Restricted Boltzmann Machine
  • Collaborative Filtering with RBM

Module 5 – Autoencoders

  • Introduction to Autoencoders and Applications
  • Autoencoders
  • Deep Belief Network


Instructor: Saeed Aghabozorgi, Romeo Kienzler, Samaya Madhavan

Level: Beginner/Intermediate

Duration: 5 weeks

User Review: NA

No. of Reviews: NA

Price: Free Enrollment (Charges applicable for certification)

6. Deep Learning Course with TensorFlow Certification – Edureka

A popular platform, Edureka, offers this certification on deep learning with TensorFlow. With the help of expert industry professionals, this program covers some of the essential topics that are the most in-demand skills in the marketplace. 

The concepts on popular algorithms such as CNN, RCNN, RNN, LSTM, and RBM using TensorFlow 2 packages in Python are covered with a wide range of hands-on sessions. In addition, the learners will work on different real-time projects such as Auto Image Captioning, Emotion detection, and much more. However, there are some prerequisites of the program that includes basic knowledge of Python programming and fundamentals of machine learning.

Additionally, to help learners brush up on the fundamentals skills, additional modules on Python for AI/ML and statistics and machine learning are included in the contents. 

The course contents are:

  • Introduction to Deep Learning
  • What is Deep Learning?
  • Curse of Dimensionality
  • Machine Learning Vs. Deep Learning
  • Uses cases of Deep Learning
  • Human Brain and Neural Network
  • What is Perceptron?
  • Learning Rate
  • Epoch and Batch Size
  • Activation Function
  • Single Layer Perceptron

Getting Started with TensorFlow 2.0

  • Introduction to TensorFlow
  • Installation
  • Defining Sequence Model Layers
  • Activation Function
  • Layer Types
  • Model Compilation
  • Model Optimizer
  • Model Loss Function
  • Model Training
  • Digit Classification using Neural Network in TensorFlow 2.0
  • Improving the model
  • Adding Hidden Layer
  • Adding Dropout Layer
  • Using Adam Optimizer

Convolutional Neural Network

  • Image Classification
  • What is Convolution?
  • Convolutional Layer
  • Filtering and ReLU Layer
  • Pooling
  • Data Flattening
  • Fully Connected Layer
  • Predicting a cat or dog
  • Saving and Loading Model
  • Face Detection using OpenCV

Regional CNN

  • Regional-CNN
  • Selective Search Algorithm
  • Bounding Box Regression
  • SVM in RCNN
  • Pre-trained Model
  • Model Accuracy 
  • Model Inference Time 
  • Model Size Comparison
  • Transfer Learning
  • Object Detection – Evaluation
  • mAP
  • IoU
  • RCNN – Speed Bottleneck
  • Fast R-CNN
  • RoI Pooling
  • Fast R-CNN – Speed Bottleneck
  • Faster R-CNN
  • Feature Pyramid Network (FPN)
  • Regional Proposal Network (RPN)
  • Mask R-CNN

Boltzmann Machine and Autoencoder

  • What is the Boltzmann Machine (BM)?
  • Identify the issues with BM
  • Why did RBM come into the picture?
  • Step by step implementation of RBM
  • Distribution of Boltzmann Machine
  • Understanding Autoencoders
  • Architecture of Autoencoders
  • Brief on types of Autoencoders 
  • Applications of Autoencoders

Generative Adversarial Network (GAN)

  • Identifying which face is fake
  • Understanding GAN
  • How does GAN work?
  • Step by Step implementation of GAN
  • Types of GAN
  • Recent Advances in GAN

Emotion and Gender Detection

  • Where do we use Emotion and Gender Detection?
  • How does it work?
  • Emotion Detection architecture
  • Face/Emotion detection using Haar Cascades
  • Implementation on Colab

Introduction to RNN and GRU

  • Issues with Feed Forward Network
  • Recurrent Neural Network (RNN)
  • Architecture of RNN
  • Calculation in RNN
  • Backpropagation and Loss calculation
  • Applications of RNN
  • Vanishing Gradient
  • Exploding Gradient
  • What is GRU?
  • Components of GRU
  • Update gate
  • Reset gate
  • Current memory content
  • Final memory at current time step


  • What is LSTM?
  • Structure of LSTM
  • Forget Gate
  • Input Gate
  • Output Gate
  • LSTM architecture
  • Types of Sequence-Based Model
  • Sequence Prediction
  • Sequence Classification
  • Sequence Generation
  • Types of LSTM
  • Vanilla LSTM
  • Stacked LSTM
  • Bidirectional LSTM
  • How to increase the efficiency of the model?
  • Backpropagation through time
  • Workflow of BPTT

Auto Image Captioning using CNN LSTM

  • Auto Image Captioning
  • COCO dataset
  • Pre-trained model
  • Inception V3 model
  • The architecture of Inception V3
  • Modify the last layer of a pre-trained model
  • Freeze model
  • CNN for image processing
  • LSTM or text processing


Instructor: Industry Professionals

Level: Intermediate

Duration: 5 weeks

User Review: 5/5

No. of Reviews: 20,000

Price: $261

7. TensorFlow: Advanced Techniques Specialization – Coursera

TensorFlow- Advanced Techniques Specialization – Coursera

This course is for advanced learners to gain experience with the advanced techniques of TensorFlow. The course is available on Coursera.

In this certification, the learners will be able to expand their knowledge about Functional APIs and build non-sequential models. Furthermore, the learners will learn to optimize training for various environments using multiprocessors and chip types. 

Moreover, experienced software engineers can gain a lot from the program, with advanced computer vision concepts related to object detection and image segmentation and the implementation of convolutions.

Besides, the learners will deep dive into generative deep learning concepts and learn how AI can create new content using Style Transfer, Auto Encoders, VAEs, and GANs. 

In addition, the course has some prerequisites that include the understanding of essential calculus and algebra, basics of AI and deep learning, and experience of working with Python, Keras, Pytorch, framework, decorator, and context manager. 

Four primary projects are mandatory for students to complete, and these projects are:

Course 1:

  • Understand the underlying basis of the Functional API and build exotic non-sequential model types, custom loss functions, and layers.

Course 2: 

  • Learn how optimization works and use GradientTape and Autograph. Optimize training in different environments with multiple processors and chip types.

Course 3: 

  • Practice object detection, image segmentation, and visual interpretation of convolutions.

Course 4:

  • Explore generative deep learning and how AIs can create new content, from Style Transfer through Auto Encoding and VAEs to Generative Adversarial Networks.
  • To find out the contents of the program, the information is given below.
  • Custom Models, Layers, and Loss Functions with TensorFlow
  • Custom and Distributed Training with TensorFlow
  • Advanced Computer Vision with TensorFlow
  • Generative Deep Learning with TensorFlow


Instructor: Laurence Moroney

Level: Intermediate/Advanced

Duration: 5 months

User Review: 4.8/5

No. of Reviews: 872

Price: Free Enrollment (Charges applicable for certification)

8. Intro to TensorFlow for Deep Learning – Udacity

Intro to TensorFlow for Deep Learning – Udacity

This is a free course available on Udacity and is offered by the TensorFlow team. The program is designed to provide a practical approach for implementing deep learning for software engineers. Additionally, there are ample hands-on experience-building sessions where learners will learn to build state-of-the-art image classification models and other deep learning models for various problem areas. 

Next, the learners will deep dive into the concepts of TensorFlow models to implement them in the real world on various platforms such as mobile devices, cloud platforms, and browsers. 

In addition, some of the advanced techniques and algorithms are covered to enable learners to work with large datasets that are essentially used for data science and big data environments. However, one must ensure that the course’s prerequisites are considered, which requires understanding the fundamentals of Python and introductory algebra. The course contents are:

Introduction to Machine Learning

Overview of artificial intelligence and machine learning

Understanding how ML and deep learning have revolutionized software

The First Model: Fashion MNSIT

Building a neural network for image recognition of articles of clothing

Introduction to CNNs

Using CNNs for efficient models for Fashion MNIST

Going Further with CNNs

Expanding image classifiers into models for predicting from multiple classes

Using a convolutional network to build a classifier for detailed color images

Transfer Learning

Using the pre-trained network to build state-of-the-art classifiers

Saving and Loading Models

Understanding the new SAVEDMODEL format in TensorFlow 2.0 and exploring TensorFlow lite and TensorFlow Serving

Time Series Forecasting

Learning from sequential data with RNNs

Natural Language Processing

Tokenizing words, creating embedding for using text data with neural network

Building RNN for improving NLP models

Generating new text for tasks such as novel song lyrics

Introduction to TensorFlow Lite

Learning how to use TensorFlow lite to build machine learning applications on Android, iOS, and IoT devices.



Instructor: Magnus Hyttsten, Juan Delgado, Paige Bailey

Level: Intermediate/Advanced

Duration: 2 months

User Review: NA

No. of Reviews: NA

Price: Free


Currently, TensorFlow is the most popular library used for machine learning and deep learning applications. TensorFlow enables faster and more efficient computations and builds advanced models for image classification, voice search, text-based applications, and even face recognition.

Top companies like Facebook and Apple implement TensorFlow for their respective applications, such as Siri and DeepFace. Even Google implements TensorFlow in various Google-based applications to improve the user experience.

Similarly, TensorFlow has gained widespread acceptance in the industry and stands out among the top skills in the field of AI. As of 2021, the average TensorFlow developer salary is $148,508, whereas senior professionals earn up to $204,000 yearly in the USA. However, the salaries vary depending on the promotions, location, and years of experience. 

Nevertheless, the developers with TensorFlow skills are offered lucrative salaries and job positions in the marketplace. Therefore, aspirants and experienced professionals aiming for a career as a machine learning or deep learning expert must have TensorFlow skills, including hands-on experience, which can be built with some of the top courses online.

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Kaushik Das

Kaushik is an artificial intelligence researcher and a data scientist with expertise in medical image processing, intelligent automation, computer vision, and big data engineering—a technical and a scientific writer by passion. He is also passionate about sports, photography, traveling, and exploring new technologies.

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