Online LearningData Science9 Best TensorFlow Courses to Learn Online in 2021

9 Best TensorFlow Courses to Learn Online in 2021

TensorFlow is a software library for building and training sophisticated deep neural networks and other machine learning applications.

Artificial intelligence has shaken our world and become an integral part of many systems in the present digital era. Therefore, unsurprisingly, there is a huge demand for professionals with expertise in TensorFlow programming.

As a result, TensorFlow has become a highly lucrative skill. According to Payscale, TensorFlow professionals in the U.S. earn $108,907 on average, surpassing numerous other skills in the IT industry.

In addition, TensorFlow is a prerequisite for more advanced courses in deep learning and applied AI, such as AI in Healthcare or AI in Finance, which could also provide even more bankable career opportunities for learners.

You can master TensorFlow by simply taking online courses. Unfortunately, not all online courses are worth taking. You need to cherry-pick the course that is worth your time and tuition.

Thus, I decided to do the heavy lifting for you. I have researched and selected the best TensorFlow courses that provide excellent value for money. You can then handily choose the one that suits your learning style and start learning right away.

Things You Should Know

As a software library, TensorFlow is not for absolute beginners. You should have experience in Python Programming, its libraries (NumPy and Pandas), data structures, and finally, probability & statistics.

In addition, some courses may require you to understand basic concepts of machine learning and deep learning. I will inform you explicitly if they do.

Affiliate Disclosure: This post from Victory Tale contains affiliate links. If you purchase TensorFlow courses through our links, we will receive a small commission from the providers.

Still, we always value integrity and prioritize our audience’s interests, so you can rest assured that we present every course truthfully.

1. Intro to Machine Learning with TensorFlow

If you are looking for a marvelous TensorFlow course with excellent student support, you don’t need to look anywhere else besides Udacity.

You will learn everything about TensorFlow from the beginning and manage to craft real-world machine learning models of your own when you finish the course.

one of the best TensorFlow courses from Udacity

Course Content

The course comprises three sections as follows:

1. Supervised Learning – The first section will cover the essence of supervised learning and the basics of machine learning models.

2. Deep Learning – The second section will introduce you to neural networks and how to design and train them on Tensorflow.

3. Unsupervised Learning – The final section will discuss advanced unsupervised learning methods and their applications.

All sections will have numerous quizzes, assignments, and a capstone project to complete. For example, you will build the image classifier by training a neural network and evaluating its performance.

Thus, you will receive tons of hands-on experience that you can apply to your projects or use to showcase your skills in your resume or Github portfolio.

As usual, Udacity’s course always comes with full support, which includes the following:

  • Mentor Support – You can ask any technical questions to your mentors 24/7. The average response time is less than an hour, which is much swifter than other platforms.
  • Project Reviews – You can send unlimited requests to experts to review your projects and send feedback.
  • Career Services – The support team will review your resume, Github portfolio, and LinkedIn profile to ensure you are ready for job applications.

This support is more or less similar to bootcamps, which would enrich your learning experience and ensure that there are experts out there to help you if you have problems.

The workload for this course is 10 hours per week for 3 months. The course is self-paced so that you can adjust your pace according to your schedule. However, since Udacity uses a subscription model, you will need to pay for the extra time you spend.

The tuition for this course is $399 per month. However, if you choose to pay in full for all 3 months, you will receive a 30% discount and pay $279 on average per month.

If you have an unpredictable schedule, it would be best to select the month-to-month option, as you can pause the entire program and keep your progress without paying course fees to no avail.

Tips: Udacity frequently offers discounts and promotions, which can be up to 75% off. Alternatively, Udacity sometimes grants a financial support after a free account registration, which equals to a 75% discount. However, only new students can receive such support.

With these offers, you can take Udacity’s excellent course by paying no more than $100 per month.

Hence, it would be best to check for such discounts on their site before making decisions.

Pros and Cons

Pros

  • Definitely one of the best online courses to learn basic TensorFlow
  • Well-structured curriculum
  • Detailed lessons
  • Excellent course materials
  • Several real-world projects (building deep learning applications)
  • Top-notch support from experts + Unlimited Reviews
  • Udacity frequently provides discounts.

Cons

  • High monthly subscription fee
  • The workload of 10 hours per week far exceeds that of other courses. If you don’t have that much time to spend, you will need to pay more tuition for extra months.
  • The program does not cover advanced TensorFlow concepts.

2. TensorFlow Developer Certificate in 2021: Zero to Mastery

This course is a new Udemy course from Andrei Neagoie, one of my favorite programming instructors. Though this course aims to prepare students for the Google TensorFlow developer certification exam, anyone can take it to improve TensorFlow skills.

Basic machine learning knowledge could be helpful but not necessary.

Best online Tensorflow courses on Udemy

Course Content

Below is a summary of what you will learn from the course.

  • Deep Learning and TensorFlow Fundamentals (Tensors, Matrix Multiplication, Aggregation, Troubleshooting, etc.)
  • Neural Network Regression and Classification in TensorFlow
  • Computer Vision and Convoluted Neural Networks
  • Transfer Learning in TensorFlow (Feature Extraction, Fine Tuning)
  • Scaling up your models
  • NLP Fundamentals
  • Time Series Fundamentals in TensorFlow (Under construction)
  • Guides for test-takers (Under construction)
  • Machine Learning and NumPy/Pandas review

Two parts of this course are under construction. Andrei plans to upload new videos soon, so the course is not fully completed.

Apart from 50.5 hours of video lectures (expect more when those two parts are completed), Andrei has provided excellent learning resources, including three real-world projects, case examples, and assignments to enrich your learning experience and help you better understand the concepts.

As a project-based course, you will build your own machine learning algorithm that can perform various tasks, including but not limited to image recognition, object detection, and text recognition. Upon project completion, you will have a skill set highly demanded by tech giants.

The course receives overwhelmingly positive reviews, scoring 4.7/5.0 out of 644 ratings.

Pros and Cons

Pros

  • One of the best technical courses for learning TensorFlow online
  • Learn with a knowledgeable instructor who has strong teaching skills
  • In-depth and informative
  • Three large projects to complete, thus best for those who prefer project-based learning
  • Excellent course materials (especially slides)
  • Lifetime access (all future updates included)
  • Inexpensive (you can get it less than $20 when on sale)

Cons

  • Two final parts of the course are under construction, including the part related to the certification.
  • Few students complained that some explanations were too vague.

3. Tensorflow 2.0: Deep Learning and Artificial Intelligence

This TensorFlow course was created by Lazy Programmer, an AI and machine learning engineer who specializes in deep learning. I have taken some of his courses myself and really appreciated his teaching style, so I am confident in recommending him.

You will need background knowledge in college-level calculus to understand the theoretical part of the course.

Course Content

Below is a summary of what you will learn from the course.

  • Introduction to Google Colab
  • Review machine learning and neurons
  • Artificial Neural Networks (ANNs)
  • Convolutional Neural Networks (CNNs)
  • RNNs, Time Series, and Sequence Data
  • NLP and Recommender Systems
  • Transfer Learning
  • Generative Adversarial Networks (GANs)
  • Deep Reinforcement Learning
  • Advanced TensorFlow (Deploy models, distribution strategies, write a custom model, etc.)
  • and many more

This course has 21.5 hours of video lectures in total. It may not have as many details as Andrei’s course, but Lazy Programmer explains the content more concisely. His teachings can help you understand the core TensorFlow concepts in no time.

You will also have a chance to apply what you learn to use by completing assignments and projects. For example, you will learn how to create a model that can predict stock prices.

The course receives excellent reviews, scoring 4.6/5.0 from 5,400+ ratings

Pros and Cons

Pros

  • Highly knowledgeable instructor
  • Include extensive explanations of the theoretical (mathematical) part of machine learning
  • Tons of exercises and projects to practice and put your knowledge into use
  • Excellent course materials
  • Lifetime Access
  • Inexpensive (you can get it on sale at less than $20)

Cons

  • As true to all courses by Lazy Programmer, the course structure could be slightly problematic. You might need to take some time to connect different concepts.

4. TensorFlow 2 for Deep Learning

This Coursera specialization by Imperial College London is unarguably one of the best options for those who want to learn TensorFlow. You will first learn fundamental concepts and later attempt to build AI-powered scalable models of your own.

This program requires you to know about both machine learning and deep learning.

Course Content

The specialization comprises three following courses:

1. Getting Started with TensorFlow 2 – The first course will introduce you to TensorFlow. You will then start building and training your models using the sequential API.

You will also learn about vital techniques that prevent recurring problems and improve efficiencies, such as validation, regularization, and callbacks.

2. Customizing Your Needs with TensorFlow 2 – You will use TensorFlow APIs to create customized deep learning models. During the process, you will learn about functional API, data pipeline, sequence modeling, and other concepts.

3. Probabilistic Deep Learning with TensorFlow 2 – This final course will build upon the knowledge you obtained from previous courses. You will learn to develop probabilistic deep learning models with TensorFlow and understand how probabilistic distributions can be incorporated into the models.

Like other courses, this program has numerous assignments and projects to complete, so you will have hands-on experience in building models that can perform several real-world tasks.

According to the university, you should spend 7 hours per week on the courses. With this pace, you will finish the entire specialization in 4 months.

You can audit the entire specialization for free. However, you may want to subscribe to the full course ($49 per month) to obtain access to graded assignments and a certificate.

Pros and Cons

Pros

  • Excellent TensorFlow tutorial from a leading university
  • Well-structured curriculum
  • Informative and clear explanations
  • Content applicable to many real-world scenarios
  • Thoughtful projects
  • Free to audit

Cons

  • Some reviewers noted that the third course of the specialization has some minor technical issues.

5. DeepLearning.AI TensorFlow Developer

This Coursera program by Deeplearning.ai will train you to build robust machine learning models to solve real-world problems. Upon course completion, you can handily use the knowledge you obtain from the course to apply to your projects.

Course Content

The program consists of four minor courses as follows:

1. Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning – This first course will teach theoretical knowledge, including machine learning and deep learning concepts. Then, you will learn to use TensorFlow to implement these principles.

In the second section of this course, you will learn about computer vision, methods to enhance it with convolutional neural networks (CNN). Finally, you will train the model to identify real-world images.

2. Convolutional Neural Network in TensorFlow – The second course will focus on developing the computer vision model you started in the first course.

You will learn advanced techniques and concepts, including augmentation, transfer learning, and multiclass classifications.

3. Natural Language Processing in TensorFlow – The third course will drill deep into natural language processing. You will learn how to process texts to input data into a neural network and apply several neural networks in TensorFlow.

4. Sequences, Time Series, and Prediction – The final course will explain how to build a time-series model in TensorFlow. You will also learn how we use deep neural networks and recurrent neural networks (RNNs) with time series.

With this program, you will have an opportunity to build your own neural network and prediction models using real-world data.

The recommended pace for this course is 5 hours per week for 4 months. I think the pace is manageable for full-time employees.

You can audit the entire program for free. However, the full course, which includes graded assignments and a certificate, costs $49 per month.

According to several reviews, the entire specialization appears too basic, especially if you have basic knowledge in machine learning and deep learning. Therefore, it would be best to stick with the audit option.

Pros and Cons

Pros

  • Learn Tensorflow from a leading AI researcher
  • Well structured curriculum
  • Interesting real-world projects
  • Free to audit

Cons

  • Some reviewers complained that the specialization lacks depth.
  • Quizzes and assignments are too easy.

6. TensorFlow Data and Deployment

This Coursera specialization will focus on handling various types of data on TensorFlow and strategies for deploying machine learning models effectively. Similar to the previous course, the deeplearning.ai team will guide you through the entire process.

It would be best if you have taken the above program from deeplearning.ai before enrolling in the specialization. Furthermore, a basic understanding of JavaScript may be crucial, as you will use TensorFlow.js in one of the courses.

Course Content

The specialization comprises four minor courses as follows.

1. Browser-based models with TensorFlow.js – You will use TensorFlow.js (machine learning library for JavaScript) to run machine learning models on the browser. You will also learn about multiple beneficial techniques that help handle browser-based data.

2. Device-based models with TensorFlow Lite – The second course will drill deep into running machine learning models in mobile applications (both Android and iOS included.)

3. Data Pipeline with TensorFlow Data Services – The third essentially deals with data. You will create and optimize data pipelines and understand strategies to avoid bottlenecks.

4. Advanced Deployment Scenarios with TensorFlow – The final course will discuss four deployment scenarios you might face when deploying models. You will also use several tools that will facilitate the deployment process while retaining data privacy.

This specialization offers an excellent overview of how developers deploy successful machine learning projects so that you can implement them on your own as well.

Deeplearning.ai suggests spending 3 hours each week on the courses, and you will finish them in 4 months. Thus, I believe the workload is manageable for all students.

The pricing for the full course is $49 per month. With several projects in the course, I think enrolling in the full course is also a good idea.

Pros and Cons

Pros

  • Insightful content that synthesizes data science and web development
  • Numerous exercises and several real-world projects to complete to enhance your TensorFlow skills
  • Informative and Practical lessons with clear explanations
  • Well-structured curriculum
  • Manageable workload
  • Free to audit

Cons

  • Some reviewers pointed out that some quizzes are not challenging enough.

7. Tensorflow: Advanced Techniques

This Coursera specialization from deeplearning.ai is the last and the most advanced among the series of three programs. In essence, you will learn about advanced computer vision in TensorFlow, explore generative deep learning concepts and perceive how to optimize training in different environments.

As an advanced specialization, you should have taken TensorFlow, machine learning, and deep learning courses before enrolling in the program.

Course Content

The specialization consists of four minor courses as follows.

1. Custom Models, Layers, and Loss Functions with TensorFlow – This first course will cover various concepts. You will first learn about the functional API, which allows developers more flexibility. You will then use it to build a Siamese network that produces multiple outputs.

Subsequently, you will learn about custom loss functions and custom layers, and finally, add custom functionality to existing models.

2. Custom and Distributed Training with TensorFlow – The second course will drill deep into Tensor Objects, which are the fundamental building blocks of TensorFlow. You will also learn about custom and distributed training, providing more flexibility and an ability to train larger models with more data involved.

3. Advanced Computer Vision with TensorFlow – The third course will cover advanced computer vision techniques with Tensorflow. These include those that apply to image classification, image segmentation, object detection, and many more.

4. Generative Deep Learning with TensorFlow – The final course will cover advanced concepts, including neural style transfer, AutoEncoders, Variational Encoders, Generative Adversarial Networks (GANs), and how to build them on TensorFlow.

You should spend 6 hours per week on the course, and you will complete the entire specialization in 5 months. Though the workload is higher than most Coursera courses, I think it is still manageable.

Similar to other Coursera programs, you can audit the entire specialization for free. However, the full course costs $49 per month.

Pros and Cons

Pros

  • Cover complex content that is rarely included in online courses
  • Clear and informative explanations
  • Good code examples
  • Free to audit

Cons

  • Assignments are not very challenging.
  • Some reviewers that some parts of the course are too concise.

8. Machine Learning with TensorFlow on Google Cloud Platform Specialization

This Coursera specialization from Google Cloud Training will introduce you to both TensorFlow and machine learning. In addition, you will work on hands-on labs that utilize the power of the Google Cloud Platform.

Course Content

This specialization comprises 5 courses as follows.

1. How Google Does Machine Learning – This first course will explain machine learning concepts and Google’s approach to the machine learning field.

2. Launching to Machine Learning – The second course overviews the main types of machine learning and various techniques, including optimization and generalization.

3. Introduction to TensorFlow – You will use TensorFlow to build, train, and deploy your machine learning models and get to know essential TensorFlow components.

In addition, you will also grasp how to work with sequential and functional APIs to create scalable deep learning models with the Google Cloud Platform.

4. Feature Engineering – This course will drill deep into feature engineering. You will understand what makes good or bad features and how to preprocess and transform them for a machine learning system.

5. Art and Science of Machine Learning – This final course aims to help you build machine learning models with improved performance. You will learn about the science behind the technology, including how neural networks work and model optimization techniques such as regularization, embedding, and hyperparameter tuning.

You will work on the hands-on components through QwikLabs, so you can apply the theories you learn from the lectures to use.

The workload for this program is 5 hours per week for 5 months, which is certainly manageable for most students.

The price of the full course is $49 per month. However, most reviewers note that the labs are too simple and do not provide much value. Hence, I suggest you stick with the audit option.

Pros and Cons

Pros

  • Good TensorFlow and machine learning courses, particularly for those who are familiar with the Google Cloud platform
  • Lively and engaging lectures with practical insights
  • Free to audit

Cons

  • Labs are just filling out the code blocks, thus not providing much value.
  • Some parts are outdated (not for TensorFlow 2.0)

Coursera Plus

Some may be interested in more than one of the TensorFlow specializations or programs on Coursera. If that’s the case, I highly suggest that you subscribe to Coursera Plus.

Coursera Plus costs $399 per year (or $33.25 per month.) It provides complete access to 94% of courses, specializations, and other programs on Coursera, including all in this list.

With Coursera Plus, you will not need to subscribe individually to each program. Instead, you can enjoy full access for one year. If you are interested, you can also take other machine learning courses on the platform as you wish.

9. Deep Learning with Tensorflow

This edX course by IBM is an excellent TensorFlow tutorial. You will learn to apply deep learning with TensorFlow to solve real-world problems.

Course Content

Below is what you will learn in the course.

  • Introduction to TensorFlow (Functions, Operations, and Execution Pipelines)
  • Linear/Nonlinear/Logistic Regression
  • Deep architectures, such as CNNs (Convolutional Neural Networks), Recurrent Neural Networks (RNNs), and Long Short-Term Memory
  • Restricted Boltzmann Machines and Autoencoders

Apparently, the course content will not go beyond the basics. However, it helps build excellent foundational knowledge that you can develop further.

The workload for this course is 2-4 hours per week for 5 weeks, which is totally manageable.

You can audit the course without charges. However, the verified track, which includes instructor’s support and a certificate, costs $99.

Pros and Cons

Pros

  • Well-organized TensorFlow tutorial
  • Cover all the basic concepts you need to know
  • Light workload
  • Audit the course for free

Cons

  • The course does not include advanced TensorFlow concepts. You will need to learn more elsewhere.

Other Alternatives

In addition to the above courses. These are other alternatives from which you can learn TensorFlow. However, all these courses may not be as in-depth as those on the list.

Datacamp – Datacamp is a platform that teaches a wide range of data science skills, including TensorFlow. However, its content on TensorFlow is not as in-depth as other courses on the list.

365 Data Science – Another platform that provides comprehensive data science training. 365 Data Science offers several well-structured courses on TensorFlow. However, I don’t think there is a deep learning project for students to complete at this point. Thus, it may be better to consider other alternatives.

Pun Anansakunwat
Founder of Victory Tale, a multipotentialite who has a particular interest in technology. He loves to spend time testing new products and learning interesting topics to broaden his insights. After graduating from Columbia University in 2014, he makes a living by being a stock market investor, a private tutor, a writer of three published books, and finally a website owner.

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