The 14 Best TensorFlow Courses in 2023 [Free + Paid]

In this article, we share the 14 best tensorflow courses in 2023. Whether you’re just starting out or an experienced data scientist that wants to enhance your skills, we’ve included tensorflow courses for all skill levels, including free and paid options.

As a top 10 framework and one of the most popular choices for machine learning and deep learning, learning to use this framework by taking tensorflow courses is  the ideal to advance your career in data science.

It’s also nice to know that the Bureau of Labor and Statistics reports an average salary of more than $110,000 for data scientists, which means that taking the time to enhance your data science skill set with one of the best tensorflow courses can be highly rewarding.

So if you’re ready, let’s dive into the best tensorflow courses to help you learn the skills you need to explore the ever-growing data science job market.

How to Find the Best TensorFlow Course?

Before you begin, it's really helpful to assess your objectives and learning style when trying to pick the best tensorflow course. 

Are you a novice, eager to learn TensorFlow? Or are you a seasoned professional seeking advanced concepts or trying to choose between PyTorch and TensorFlow? 

Do you prefer self-paced online courses or thrive in a structured classroom environment? Clear answers to these questions will help streamline your options. 

That said, we used the following criteria when compiling our list of the best tensorflow courses, and we’d recommend you use these as well:

• Course Content and Curriculum: We favored courses that offer a logical progression - starting from basics and gradually moving towards advanced topics.
• Instructor Expertise and Reputation: We looked for courses that are led by highly experienced professionals with solid reputations in the field.
• Hands-on Exercises and Projects: We chose courses that provide a rich array of projects, exercises, and real-world applications.
• Student Reviews and Ratings: We've factored in student feedback and course ratings to understand how well the courses have been received by the learners.
• Certification or Accreditation: Our list comprises courses that provide recognized certifications, enhancing your credibility in the TensorFlow realm.

14 Best TensorFlow Courses of 2023

1. [Coursera] DeepLearning.AI TensorFlow Developer Professional Certificate 

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Why we chose this course

Our team chose this course as it’s offered by DeepLearning.AI, an organization recognized for its expertise and contributions to AI and machine learning. This means that you’ll be guided by seasoned experts as you prepare for the Google TensorFlow Certificate exam.

The program encompasses four courses, providing you with a comprehensive knowledge base and hands-on experience through 16 Python programming assignments to help you learn TensorFlow.

Expect to learn how to build and train neural networks and how to create original poetry through natural language processing systems. You’ll also cover neural networks, computer vision, augmentation, transfer learning, gated recurrent units, long short-term memory, time series learning, and prediction models.

And while this course assumes you have foundational knowledge in various areas, it can be helpful to consider taking one of the best deep learning courses to reinforce your understanding.

Pros

• Covers a wide range of TensorFlow applications, including computer vision, natural language processing, and time series prediction
• Includes 16 practical Python programming assignments 
• User feedback indicates a high level of satisfaction with the quality of instruction 
• Aligns with the requirements of the Google TensorFlow Certificate exam
• Offered by DeepLearning.AI, an organization recognized for its AI expertise 

Cons

• None

2. [Udacity] Intro to Machine Learning with TensorFlow

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Why we chose this course

Our findings show that this three-month program is designed for Python-savvy students who want to delve into machine learning models with TensorFlow. 

The course walks you through foundational ML algorithms, beginning with data cleaning and supervised models, before progressing to deep and unsupervised learning.

You get hands-on experience with practical code exercises and projects, like building an image classifier or segmenting customers using unsupervised learning techniques. Each project encourages you to implement your skills in real-world scenarios, whether that’s predicting donation yield for a charity or assessing customer segments for a business.

The program is enriched with content co-created with Kaggle and offers supportive features like real-time assistance, project feedback from experienced reviewers, and career services.

Pros

• Comprehensive understanding of ML algorithms, including supervised and unsupervised learning
• Real-world projects to apply what you’ve learned and gain practical experience
• Includes career services like GitHub review and LinkedIn profile optimization
• Real-time support ensures you can progress smoothly through the material

Cons

• None

3. [Coursera] TensorFlow: Advanced Techniques Specialization

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Why we chose this course

Our findings show that this four-course specialization takes an in-depth look into the advanced features and applications of TensorFlow, which is ideal if you want to take the next step in data science or if you want to pursue deep learning.

If you’re an early or mid-career software and machine learning engineer with a foundational knowledge of TensorFlow, this is ideal for you as it aims to expand your understanding of complex model architectures and training techniques. 

The curriculum includes topics such as the Functional API, optimization with GradientTape & Autograph, advanced computer vision scenarios such as object detection and image segmentation, as well as generative deep learning.

You will get hands-on experience by building exotic non-sequential model types, customizing loss functions and layers, optimizing training across various environments, implementing object detection and image segmentation, and even creating new content with Style Transfer, Auto Encoding, VAEs, and GANs.

If any of this sounds intimidating, you should also consider picking up some deep learning books to use as a supplementary learning resource.

Pros

• In-depth exploration of TensorFlow functional API
• Practical assignments using GradientTape and Autograph for fine-tuning models
• Hands-on experience with complex computer vision tasks 
• Covers generative techniques, including Style Transfer, Auto Encoding, VAEs, and GANs

Cons

• None

4. [Udemy] Tensorflow 2.0: Deep Learning and Artificial Intelligence

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Why we chose this course

Our research indicates that this is an extensive TensorFlow course that’s been designed to introduce both beginners and advanced students to the exciting world of deep learning and AI. 

The course instructor, leveraging Google's TensorFlow 2.0, ensures an engaging and effective learning experience by meticulously explaining every line of code and incorporating advanced mathematical algorithms in the course content.

The curriculum covers a variety of essential topics ranging from the basics of Artificial Neural Networks (ANNs) and Deep Neural Networks (DNNs) to advanced applications such as Recommender Systems, Convolutional Neural Networks (CNNs), and Generative Adversarial Networks (GANs). 

We also like that the course offers a practical experience with projects like a Deep Reinforcement Learning Stock Trading Bot and Transfer Learning for Computer Vision.

If you’re not 100% confident that you have the prerequisites to take this course, it’s worth checking out some of the best AI courses to beef up your skills.

Pros

• Covers a wide array of deep learning and AI topics, from basic to advanced levels
• Provides real-world applications of theoretical knowledge through real-world projects
• Detailed lessons on using TensorFlow's Distribution Strategies
• Thorough explanations for every line of code 

Cons

• Reports that the instructor is not very open to learner queries 

5. [Google] Machine Learning Crash Course with TensorFlow APIs

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Why we chose this course

Google is perhaps one of the best places to learn TensorFlow and machine learning. This fast-paced introduction to TensorFlow blends academic rigor with real-world applicability, featuring 25 lessons and over 30 practice exercises from Google's experienced researchers.

Based on our observations, this course tackles critical machine learning concepts such as distinguishing machine learning from traditional programming, understanding loss and gradient descent, evaluating model effectiveness, data representation for machine learning, and the construction of deep neural networks. 

Beyond the robust course content, you also get the chance to apply your skills through a companion Kaggle competition, providing an excellent blend of theory and practice.

If some of these areas sound intimidating, it might be worth considering machine learning courses to take in tandem and to strengthen your foundational skills.

Pros

• Taught by experienced Google researchers
• Incorporates real-world case studies that add practical context to the learning
• Plethora of hands-on exercises, enhancing practical learning and concept reinforcement
• Kaggle competition for real-world experience and skill application
• Makes use of interactive visualizations, promoting easier and more engaging learning

Cons

• None

6. [edX] Deep Learning with TensorFlow 

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Why we chose this course

Our team found this IBM-endorsed course that explores the potentials of TensorFlow in implementing deep learning solutions. We also like that it’s structured in a progressive way, starting with a simple “Hello World” example and advancing to more complex concepts. 

The course explains how to use TensorFlow in various machine learning applications such as curve fitting, regression, classification, and minimization of error functions. You could even supplement this with machine learning books if you want a 360-degree view of these topics.

It also covers foundational TensorFlow concepts, operations, and the execution pipeline, alongside a deep dive into various deep learning architectures like Convolutional Networks, Recurrent Networks, and Autoencoders.

Pros

• Goes in-depth into TensorFlow and its applications in deep learning
• Covers a broad spectrum of deep learning architectures
• Professional digital badge upon successful completion
• Progressive learning path, making it accessible to beginners 

Cons

• None

7. [Udacity] Intro to TensorFlow for Deep Learning 

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Why we chose this course

This intermediate-level TensorFlow course is developed by industry experts in collaboration with TensorFlow themselves, providing a practical, hands-on experience in building state-of-the-art deep learning models.

Our findings show that the course covers a range of topics, from a high-level overview of AI and machine learning to more specific areas like Convolutional Neural Networks, Time Series Forecasting, and Natural Language Processing. 

It even delves into TensorFlow Lite for building ML applications on mobile and IoT devices. By the end of this course, you will be equipped with the necessary skills to create your own AI applications.

Pros

• Direct collaboration with TensorFlow ensures accurate and up-to-date content
• Practical, hands-on experience with TensorFlow
• Covers a broad array of deep learning topics
• Opportunity to work on state-of-the-art image classifiers and other deep learning models

Cons

• None

8. [Udemy] A Complete Guide on TensorFlow 2.0 using Keras API

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Why we chose this course

Our analysis of this course found it provides an in-depth overview of TensorFlow applications while integrating hands-on projects into the learning process. This means you’ll be building a Fashion API with Flask and serving a TensorFlow model with a RESTful API.

We also like that there are quizzes after each module, and past learners consistently praise the course's comprehensive and updated content. 

Overall, this course is structured into five parts, beginning with a foundation in TensorFlow 2.0 library basics before transitioning into practical implementation of different types of neural networks, including Fully Connected, Convolutional, and Recurrent Neural Networks. 

One particular highlight is the creation of a stock market trading bot using Reinforcement Learning, specifically a Deep-Q Network. 

You will also cover TensorFlow Extended (TFX), focusing on data pipelines for production and how to distribute Neural Network training to multiple GPUs or servers.

Depending on your career goals, we’d also recommend combining this hands-on TensorFlow course with one of the best data science courses if you’re passionate about a career in data science. 

Pros

• Covers topics from basics of TensorFlow 2.0 to Distributed Training and Reinforcement Learning
• Each module has associated projects and assignments
• Course content is based on the most recent version of TensorFlow (2.0), including its latest features and tools
• Course is structured efficiently, with a total length of 13 hours 

Cons

• Does not delve deeply into the theoretical aspects of some concepts

9. [FreeCodeCamp] Python TensorFlow for Machine Learning – Neural Network Text Classification

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Why we chose this course

Led by Kylie Ying, an MIT graduate and noted STEM educator, this free TensorFlow course from FreeCodeCamp benefits from a grant from Google's TensorFlow team. 

This means you get an enriched learning experience supported by clear explanations and an engaging teaching style.

In this course, you are introduced to key machine learning concepts and are guided through implementing neural networks with Python and TensorFlow. 

A distinguishing feature of the course is the hands-on projects, including how to predict diabetes with a feedforward neural network and the classification of wine reviews with two different neural network architectures. 

Pros

• Reputable instructor with a strong academic background and teaching experience
• Covers essential machine learning concepts and offers practical applications with neural network implementation
• Hands-on projects dealing with real-world issues like diabetes prediction and text classification of wine reviews 
• Supported by Google's TensorFlow team, ensuring high-quality, up-to-date content

Cons

• Lack of personalized support or instructor interaction due to the YouTube format

10. [edX] Google AI for JavaScript Developers with TensorFlow.js

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Why we chose this course

We came across this TensorFlow course offered in collaboration with Google which is designed to empower web developers to integrate AI and Machine Learning capabilities into their web applications. 

It’s specifically tailored for JavaScript users who want to learn how to utilize TensorFlow.js, Google's leading ML library for JavaScript. The course covers the key concepts of AI, machine learning, and deep learning, and teaches you how to implement them through real-world examples.

We also think this course stands out for its practical orientation, as it encourages you to apply your knowledge to diverse scenarios, like blocking spam in blog post comments or using webcam sensors for alert detection. 

You will also be guided through model creation, the use of pre-made models, perceptrons, linear regression, transfer learning, and converting Python models to JavaScript.

Pros

• Caters to JavaScript developers
• Helps you to apply your newly acquired skills immediately to real-world scenarios
• Covers a wide range of topics, providing a comprehensive foundation 
• Includes the ability to convert Python models to JavaScript 
• Knowledge can be applied across multiple platforms.

Cons

• None

11. [educative.io] Applied Machine Learning: Industry Case Study with TensorFlow

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Why we chose this course

This intermediate-level course was designed in collaboration with industry experts from Google, Microsoft, Amazon, and Apple - which is quite impressive!

Based on our findings, you’ll get the chance to work on a real-world, industry-level machine learning project, which involves predicting retail sales based on various factors. The course curriculum also encompasses various lessons related to data analysis, data processing, and model predictions. 

Another plus with this course is that the educative platform features live code environments within the browser and built-in assessments after each lesson, ensuring you get the most out of your learning experience. 

And like all educative courses, they’ve opted out of videos in favor of text and interactive learning, which can be a huge plus if you respond to this teaching style.

Pros

• Involves an industry-level machine learning project, providing real-world application
• Built in collaboration with industry machine learning experts
• Immediately apply concepts within live code environments in the browser
• Twice as fast as video courses, ensuring an efficient learning experience 

Cons

• Entirely text-based, which may not be suitable for visual learners

12. [Codecademy] Build Deep Learning Models with TensorFlow Skill Path

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Why we chose this course

Our analysis of this course found it to be an engaging skill path for gaining valuable skills in deep learning by using both TensorFlow and Keras. 

The course curriculum includes deep learning concepts, working with TensorFlow, image classification models, real-world applications of deep learning, and an opportunity to demonstrate your new skills through a portfolio project.

You'll also complete practical projects along the way, such as designing a neural network model to predict the life expectancy of countries based on various factors, and predicting graduate admissions chances with a regression deep learning model.

Pros

• Comprehensive coverage of deep learning fundamentals and applications
• Offers hands-on projects to reinforce learning and provide practical experience
• Features an interactive platform with in-browse coding and AI-driven recommendations

Cons

• None

13. [Microsoft] TensorFlow Fundamentals 

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Why we chose this course

Our research indicates that this is an excellent program for beginners seeking to learn the essentials of deep learning with TensorFlow.

We like that it’s structured into five detailed modules that cover an introduction to TensorFlow using Keras, computer vision, natural language processing (NLP), audio classification, and customization beyond Keras. 

Each module also provides extensive insight into how to handle different types of data and build relevant models with a practical approach.

Throughout this course, you’ll gain a firm grasp on different neural network architectures, exploring various techniques like image classification, convolutional neural networks, embeddings, recurrent neural networks, and more.

Pros

• Covers a comprehensive range of topics from computer vision to audio classification
• Modules follow a detailed structure, enabling a thorough understanding of each concept
• Hands-on approach allows learners to apply their knowledge in practical ways
• Exposes you to different neural network architectures

Cons

• None

14. [Pluralsight] TensorFlow on Google Cloud

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Why we chose this course

Taught by Google Cloud, this introductory course is an excellent way to get an insight into TensorFlow and its ecosystem, all while utilizing the powerful features of the Google Cloud platform. 

Based on our observations, it covers several key areas, including the design and building of TensorFlow input data pipelines, the creation of machine learning models using TensorFlow and Keras, and strategies for improving model accuracy.

You’ll also learn the art of writing machine learning models for large-scale use. Plus, with this course’s hands-on approach, you’ll have access to various lab exercises that allow you to implement and reinforce what you’ve learned.

Pros

• Designed by Google Cloud, ensuring high-quality content and a real-world context
• Range of topics, providing you with a well-rounded understanding of TensorFlow
• Use of lab exercises offers practical, hands-on experience that reinforces learning
• Teaches how to leverage the Google Cloud platform
• Delves into specialized topics like building large-scale models and using Keras API

Cons

• None

Why Learn TensorFlow in 2023? 

TensorFlow remains a leading library in the realm of deep learning and machine learning thanks to its exceptional functionality, scalability, and growing ecosystem. Let's explore why you should consider adding TensorFlow to your tech arsenal.

• Deep Learning and Machine Learning Expertise: Mastering TensorFlow opens the door to harnessing the potential of deep learning applications and machine learning models.
• Flexibility and Scalability: TensorFlow enables you to develop models from scratch, and its scalability across various platforms makes it a go-to tool for various organizations.
• Large Community and Ecosystem: TensorFlow boasts an active global community, which ensures a steady stream of updates, innovative solutions, and extensive support. 
• Industry Adoption and Job Opportunities: With extensive industry adoption across diverse sectors, TensorFlow expertise unlocks many job opportunities. 
• Versatility and Integration: TensorFlow seamlessly integrates with other popular libraries and tools (like Keras and NumPy), boosting its versatility. 

Final Thoughts

So there you have it, the 14 best tensorflow courses in 2023, including a range of tensorflow courses for beginners and experienced pros, including free and paid options.

Whether you’re just starting out in your data science career or want to level up your existing skills, we’ve included a range of tensorflow courses to help you achieve your goals in data science.

Happy learning!

Are you brand new to programming, and you're ready to learn Python? Check out:

Our very own Python Masterclass - Python with Dr. Johns

Frequently Asked Questions

1. What Is the Best Tensorflow Course?

The best tensorflow course really depends on your current skill level, learning goals, and teaching preferences. Most tensorflow courses also assume that you have basic Python and math. If you’re not sure where to start, we’d definitely recommend DeepLearning.AI’s offering on Coursera. 

2. Where Should I Learn Tensorflow?

There are lots of choices when it comes to choosing somewhere to learn TensorFlow. Depending on your budget, time requirements, and whether or not you want a certificate, we’d recommend checking out each of the TensorFlow course providers in our list above to find the right place to learn TensorFlow for you.  

3. Is TensorFlow Worth Learning?

Yes, TensorFlow is worth learning. As a widely adopted library in the field of machine learning and deep learning, TensorFlow expertise opens up opportunities in AI, data science, and more. Industries across the globe are leveraging TensorFlow for complex applications, from automated driving to disease detection, making TensorFlow skills highly sought-after in the job market.

4. How Do I Become an Expert in Tensorflow?

Becoming an expert in TensorFlow requires understanding the fundamental concepts, gaining hands-on experience, and continuously learning. That said, like any technical skill, you can only become an expert after spending a reasonable length of time implementing your skills in a variety of settings to grow your expertise.

5. What Is the Salary for Google TensorFlow Certification?

The salary for individuals with a Google TensorFlow certification can vary significantly based on geographical location, experience level, and specific role.

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