TensorFlow is a very popular free and open-source machine learning library created by Google.
In this Best Courses Guide (BCG), I’ve used Class Central’s catalog of 250K courses to find the best online courses for learning TensorFlow.
If you’d like to know how I chose these courses, you can find my methodology below.
Click on the shortcuts for more details:
Which TensorFlow Course Is Right for You?
What is TensorFlow?
TensorFlow is a comprehensive open source library for machine learning (ML). Originally created at Google as an internal tool before being open sourced in 2015, it has become incredibly popular over the years as artificial intelligence gained prominence.
The reason why TensorFlow is one of the best libraries to implement machine learning applications, including deep learning, is its comprehensive, flexible ecosystem of tools and community resources. It allows researchers to push the state-of-the-art in ML and developers to easily build and deploy ML-powered applications.
According to PayScale, TensorFlow skills command an average base salary of $109K in the US.
Why You Should Trust Us
Class Central, a Tripadvisor for online education, has helped 100 million learners find their next course. We’ve been combing through online education for more than a decade to aggregate a catalog of 250,000 online courses and 250,000 reviews written by our users. And we’re online learners ourselves: combined, the Class Central team has completed over 400 online courses, including online degrees.
Best Hands-On TensorFlow Course for AI Beginners (DeepLearning.AI)
For anyone completely new to machine learning (ML) or deep learning (DL) who wants to learn TensorFlow for AI, Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning is a great starting point.
In this course, you’ll start with the basics of machine learning before diving into practical applications. Using TensorFlow, you’ll build neural networks to predict housing prices and classify clothing items from images. The course covers computer vision, convolutional neural networks, and handling complex real-world datasets. You’ll gain hands-on experience with Keras and develop skills in binary classification.
Designed for beginners with Python experience and high school-level math, this course provides a foundation in AI and deep learning techniques.
What you’ll learn:
- Fundamentals of machine learning and deep learning
- Building and training neural networks using TensorFlow
- Computer vision techniques and image classification
- Implementing convolutional neural networks for improved efficiency
- Handling complex datasets and performing binary classification
- Practical applications of AI in housing price prediction and image recognition.
| Organization | DeepLearning.AI |
| Provider | Coursera |
| Part of | DeepLearning.AI TensorFlow Developer Professional Certificate |
| Instructor | Laurence Moroney |
| Workload | 22 hours |
| Enrollments | 406.4K |
| Rating | 4.8 / 5.0 (19.7K) |
| Exercises | Quizzes and coding projects |
| Certificate | Paid |
Best Comprehensive Course for Beginners (Zero To Mastery)
Offered by Zero To Mastery (ZTM) Academy, TensorFlow for Deep Learning Bootcamp aims to provide you with all the skills necessary to truly stand out from the deep learning crowd!
This paid course is heavily project-based. You’ll do lots of coding exercises, where you’ll build machine learning models and projects that mimic real-life scenarios. More importantly, the course will guide you through TensorFlow, preparing you to use TensorFlow out in the world. Upon course completion, you’ll have the skill sets needed to develop modern deep learning solutions.
A basic understanding of machine learning is helpful but not strictly necessary. No previous TensorFlow knowledge is required!
A great feature of ZTM is the global Discord community with over 500,000 members so you can connect online at any time of the day or night. Ask questions, share projects, and connect with the community so you won’t feel lonely while studying.
What you’ll learn:
- Fundamental TensorFlow concepts and tensor operations
- Overview of NumPy and Pandas
- Resources included: Jupyter notebooks, Google Colab notebooks, follow-along code, notes, and slides
- Building and training neural networks for regression and classification tasks
- Implementing convolutional neural networks for computer vision applications
- Utilizing transfer learning techniques with pre-trained models from TensorFlow Hub
- Developing natural language processing models using recurrent neural networks
- Tackling time series forecasting problems
- Practical skills through three major projects: Food101 image classification, SkimLit medical abstract classification, and time series prediction.
| Provider | Zero To Mastery |
| Instructor | Daniel Bourke |
| Level | Beginner to advanced |
| Workload | 64 hours |
| Rating | 4.9 / 5.0 |
| Material | Videos, readings, projects |
| Certificate | Paid |
Best Advanced Tensorflow and Machine Learning Course (Udacity)
In the Introduction to Machine Learning with TensorFlow nanodegree you will build powerful machine learning models to predict outcomes and uncover insights using data. You should have knowledge of basic statistics, probability, calculus, python for data science, and supervised machine learning prior to tackling this nanodegree. Most of these topics are covered in optional courses listed in the nanodegree. For more depth, you can also take three more courses covering additional material.
The 11 instructors come from Fortune 500 and Global 2000 companies and have demonstrated leadership and expertise in their professions.
What you’ll learn:
- Introduction to Machine Learning
- Supervised Learning
- Introduction to Neural Networks with TensorFlow
- Unsupervised Learning
Optional prerequisite courses:
- Python for Data Analysis
- SQL for Data Analysis
- Command Line Essentials
- Git & Github
Optional additional courses:
- Python for Data Visualization
- Statistics for Data Analysis
- Linear Algebra.
| Provider | Udacity |
| Instructor | Josh Bernhard +10 others |
| Level | Intermediate to advanced |
| Workload | 49 hours |
| Rating | 4.6 / 5.0 |
| Material | Videos, readings, projects |
| Certificate | Paid |
Comprehensive Course Made By Tensorflow Themselves (TensorFlow)
Intro to TensorFlow for Deep Learning, created by Google’s TensorFlow team in collaboration with Udacity, offers a comprehensive introduction to deep learning with TensorFlow.
You’ll learn to build state-of-the-art image classifiers and work with large datasets. The course covers various neural network types, including Convolutional Neural Networks (CNNs) for image recognition and Recurrent Neural Networks (RNNs) for text generation. You’ll also explore transfer learning and Natural Language Processing (NLP).
Taught by Google Developer Advocates and industry experts, this hands-on course features high-quality video content and plenty of learning resources. It’s ideal for those with basic Python skills and algebra knowledge. By the end of this course, you’ll have all the necessary skills to develop your own AI applications.
What you’ll learn:
- Fundamentals of AI, machine learning, and deep learning
- Building and training neural networks for image classification
- Implementing CNNs for advanced image recognition tasks
- Applying transfer learning to improve model efficiency
- Natural Language Processing techniques and text generation with RNNs
- Optional introduction to TensorFlow Lite for mobile and IoT applications.
The course wraps up with an optional but recommended introduction to TensorFlow Lite, a lighter but still powerful version of TensorFlow for building machine learning apps on Android, iOS, and IoT devices, although it is actually a separate course on its own.
| Organization | TensorFlow |
| Provider | Udacity |
| Instructors | Magnus Hyttsten, Juan Delgado, and Paige Bailey |
| Level | Intermediate |
| Workload | 17 hours |
| Exercises | Coding assignments in Google Colab |
| Certificate | None |
Best Course for Mastering TensorFlow 2 Workflow in Deep Learning Applications (Imperial)
Getting started with TensorFlow 2 offered by Imperial College London on Coursera teaches Python programmers with existing machine learning (ML) and deep learning (DL) knowledge how to build deep learning models using TensorFlow 2.
You’ll master an end-to-end workflow for developing TensorFlow models, including building, training, and evaluating using the Sequential API. This course covers a wide range of applications, from image classification to sentiment analysis and generative language models.
With hands-on programming assignments and a capstone project focused on classifying street view house numbers, this intermediate-level course provides a comprehensive understanding of TensorFlow 2 for practical deep learning applications.
What you’ll learn:
- TensorFlow 2 installation, documentation navigation, and Google Colab usage
- Building and training models with Keras, including loss functions and optimizers
- Convolutional neural networks for image classification
- Model validation, regularization, and dropout techniques to prevent overfitting
- Saving, loading, and working with pre-trained TensorFlow models
- Developing deep learning classifiers for various applications, including satellite imagery and street view house numbers.
| Institution | Imperial College London |
| Provider | Coursera |
| Part of | TensorFlow 2 for Deep Learning Specialization |
| Instructor | Kevin Webster |
| Level | Intermediate |
| Workload | 26 hours |
| Enrollments | 40.0K |
| Rating | 4.9 / 5.0 (582) |
| Exercises | Programming assignment in Jupyter Notebooks |
| Certificate | Paid |
Best Short Intro for Tabular Data (Kaggle)
If you want the shortest path from “I know some Python” to “I have trained a neural network,” Intro to Deep Learning gets you there in about four hours.
Kaggle builds the idea up from a single neuron to a full deep network, then spends the remaining lessons on the things that actually decide whether a model works: stochastic gradient descent, spotting overfitting and underfitting, and using dropout and batch normalization to keep training stable. It closes with a binary classification exercise and a lesson on running your training on Tensor Processing Units.
Everything runs in Kaggle’s hosted notebooks, so there is no local setup to fight with. The worked examples use structured, spreadsheet-style data rather than images, which makes this a good fit if the data you actually have at work is a table. It is free, and there is a certificate at the end.
You will want some Python before starting, but no machine learning background is assumed.
What you’ll learn:
- Linear units, and how they combine into deep neural networks
- Building and training your first network with Keras and TensorFlow
- Stochastic gradient descent and reading training curves
- Diagnosing overfitting and underfitting, adding capacity, and early stopping
- Dropout and batch normalization for more stable training
- Binary classification, plus an introduction to TPUs.
| Provider | Kaggle |
| Workload | 4 hours |
| Rating | 4.8 / 5.0 (4) |
| Exercises | Hands-on notebook exercises |
| Certificate | Free |
Best for Advanced Keras Applications (IBM)
Once you can build a working model with Keras, the next question is how to build the ones that don’t fit a simple stack of layers. IBM’s Deep Learning with Keras and Tensorflow is aimed squarely at that stage.
It opens with the Keras functional and subclassing APIs, which are what you need for models that branch, merge, or produce several outputs, then moves through writing your own layers and models. From there it covers advanced convolutional networks with data augmentation and transfer learning, transformers for sequential data and time series, and a full generative block: autoencoders, diffusion models, and generative adversarial networks. The last modules bring in custom training loops, hyperparameter tuning with Keras Tuner, and reinforcement learning with deep Q-networks.
Every lesson has a hands-on lab, and the course ends with a transfer learning project that classifies waste products. Before starting you’ll want working Python, comfort with gradients and matrices, and some prior Keras experience.
What you’ll learn:
- The Keras functional and subclassing APIs for complex model architectures
- Writing custom layers and models
- Advanced CNNs with data augmentation and transfer learning
- Transformers for sequential data, time series prediction, and text generation
- Autoencoders, diffusion models, and generative adversarial networks
- Custom training loops and hyperparameter tuning with Keras Tuner
- Q-learning and deep Q-networks for reinforcement learning.
| Institution | IBM |
| Provider | Coursera |
| Instructor | Alex Aklson |
| Level | Intermediate |
| Workload | 23.5 hours |
| Enrollments | 62.0K |
| Rating | 4.4 / 5.0 (1K) |
| Exercises | Hands-on labs, quizzes, and a final project |
| Certificate | Paid |
Best Tensorflow Primer Course Using Jupyter Notebooks (Microsoft)
TensorFlow fundamentals by Microsoft is a concise introduction to the fundamentals of deep learning with TensorFlow. This beginner-friendly learning path will introduce key concepts for building machine learning models. You’ll explore computer vision, natural language processing, and audio classification projects throughout your journey.
The prerequisites for this course are basic Python knowledge and a basic understanding of machine learning.
What you’ll learn:
- TensorFlow basics using Keras API for data preparation, model building, and prediction
- Implementing and understanding Convolutional Neural Networks (CNNs) for computer vision tasks
- Utilizing pre-trained networks and transfer learning for efficient model development
- Natural language processing techniques using various neural network architectures
- Converting audio data into spectrograms for binary classification using computer vision techniques
- Practical application of deep learning concepts through Jupyter Notebook exercises.
| Institution | Microsoft |
| Level | Beginner |
| Exercises | Quizzes and Jupyter Notebooks |
| Workload | 4.5 hours |
Best Advanced TensorFlow Course for Custom Model Architecture and Optimization (DeepLearning.AI)
If you want to be ahead of the curve, you’ll need to be able to build your own custom ML or DL models. This course will help you do just that!
Custom Models, Layers, and Loss Functions with TensorFlow from DeepLearning.AI aims to help those already familiar with the foundations of TensorFlow gain more control over their model architecture.
To take this course, you’ll need knowledge of AI and deep learning as well as the math that underpins them. You’ll also need experience with Python and either TensorFlow, Keras, or the PyTorch framework.
What you’ll learn:
- Using the Functional API to create flexible and complex model architectures
- Building Siamese networks for similarity measurements in handwriting or facial recognition
- Developing custom loss functions, including the Huber loss function
- Implementing lambda layers and custom layers for specialized model components
- Creating custom model classes by inheriting from TensorFlow’s Model class
- Constructing a ResNet model from the ground up
- Designing custom callbacks to monitor and control model training.
| Institution | DeepLearning.AI |
| Provider | Coursera |
| Part of | TensorFlow: Advanced Techniques Specialization |
| Instructors | Laurence Moroney and Eddy Shyu |
| Level | Intermediate |
| Workload | 31 hours |
| Enrollments | 44.4K |
| Rating | 4.9 / 5.0 (1.1K) |
| Certificate | Paid |
Best for Deploying Models (DeepLearning.AI)
Every other pick in this guide teaches you to build a model. This one is about getting it in front of people.
TensorFlow: Data and Deployment is a four-course specialization covering the places a trained model actually has to run. You start with TensorFlow.js, training and running models directly in a browser, then move to TensorFlow Lite for Android, iOS, and embedded devices. The third course is about the data side: using TensorFlow Data Services to pull in built-in datasets with a few lines of code, control how they are split, and process unstructured data. The last one covers TensorFlow Serving, TensorFlow Hub, and TensorBoard, plus retraining a deployed model on user data while keeping that data private.
It assumes you can already build and train models in TensorFlow, so treat it as the step after one of the beginner picks above rather than a starting point.
What you’ll learn:
- Training and running models in the browser with TensorFlow.js
- Deploying models to Android, iOS, and embedded devices with TensorFlow Lite
- Building data pipelines with TensorFlow Data Services
- Controlling dataset splits and processing unstructured data
- Serving models with TensorFlow Serving and reusing them from TensorFlow Hub
- Retraining deployed models on user data while preserving privacy.
| Institution | DeepLearning.AI |
| Provider | Coursera |
| Instructor | Laurence Moroney |
| Level | Intermediate |
| Courses | 4 |
| Workload | — |
| Enrollments | 40.6K |
| Rating | 4.7 / 5.0 (1.5K) |
| Certificate | Paid |
Best for Transformers and LLMs (MIT)
Most TensorFlow courses were built before large language models became the centre of the field. Hands-On Deep Learning, published through MIT OpenCourseWare from the Spring 2024 run of 15.773, is the one pick here that treats the modern era as its main subject.
It moves fast. The first two lectures cover setting up and training deep networks and get you into Keras and TensorFlow on tabular data. Three more lectures go through computer vision, building convolutional networks from scratch before moving to transfer learning, fine-tuning, and Hugging Face. The back half is language and generative work: embeddings, transformers, self-supervised learning, large language models with retrieval augmented generation, parameter-efficient fine-tuning, and text-to-image models.
This is course material rather than a taught course, so there is no instructor support, no graded work, and no certificate. What you get is the full 11-lecture set from a current MIT class, for free. You’ll need Python and the standard machine learning vocabulary, meaning training and validation splits, overfitting and underfitting, and regularization, before you start.
What you’ll learn:
- Setting up and training deep neural networks
- Keras and TensorFlow applied to tabular data
- Building convolutional networks from scratch for images and video
- Transfer learning and fine-tuning, including Hugging Face
- Word embeddings and transformer architectures
- Large language models and retrieval augmented generation
- Parameter-efficient fine-tuning and text-to-image models.
| Institution | Massachusetts Institute of Technology |
| Provider | MIT OpenCourseWare |
| Lectures | 11 |
| Workload | — |
| Material | Lecture slides and notebooks |
| Certificate | None |
How We Made Our Picks and Tested Them
I built this article following the now tried-and-tested methodology used in previous guides (you can find them all here). It involves a three-step process:
First, let me introduce myself. I’m part of the Class Central team, and I (@elham) built this guide in collaboration with my friend and colleague @manoel.
We started off with a data-driven process by leveraging Class Central’s database of 250K courses to make a preliminary selection of TensorFlow courses. We took into account things like ratings, reviews, and course bookmarks to gauge what are the most popular Tensorflow courses.
But we didn’t stop there. Ratings and reviews rarely tell the whole story. So the next step was to bring our personal knowledge of online education into the mix.
Second, we used our experience as online learners to evaluate each preliminary pick.
Both of us come from computer science backgrounds and are prolific online learners, having completed about 45 MOOCs between us. Additionally, Manoel has an online bachelor’s in computer science, while I am currently completing my foundation in computer science.
After carefully analyzing each course, bouncing ideas off each other, and making iterative improvements to the rankings until we were both satisfied, we’ve come up with a selection of the top courses out there. But there’s one more step which we took.
Third, during our research, we stumbled across courses that we felt were well-made but weren’t well-known. Had we adopted a purely data-centric approach, we would be forced to leave those courses out of the ranking just because they had fewer enrollments.
So we decided instead to take a more holistic approach. The courses in this ranking range from beginner to expert, as well as theory to practice focused.
After going through this process, combining Class Central data, our experience as lifelong learners, and a lot of editing, we arrived at our final ranking. So far, we’ve spent more than 14 hours building this ranking, and we intend to continue updating it in the future.
Fabio and Pat revised later versions of this article.
The post 11 Best TensorFlow Courses for 2026: Free and Paid appeared first on The Report by Class Central.