Best deep learning course: 10 compared in 2026

Best for data scientists who want to build agents with deep reinforcement learning. It covers MDPs and the Bellman equation, Q-learning, DQN with experience replay, policy gradients and A2C, using Gymnasium and Stable Baselines 3 to train agents that play Atari games.

It has 4.8 stars from more than 500 reviews and more than 4,000 students. It runs about 15 and a half hours and was updated in September 2026. We lowered it because it expects reinforcement learning fundamentals and university maths.

Best for learners who want to model sequences, time series and text. It reviews neural networks, then covers RNNs, GRUs and LSTMs in TensorFlow 2, time series forecasting, stock price and return prediction, embeddings and text classification for spam detection and sentiment analysis.

It has 4.5 stars from more than 6,000 reviews and more than 46,000 students. It runs about 13 and a half hours with five quizzes and a practice test, and was updated in September 2026.

Best for Python programmers who want hands-on PyTorch. It covers tensors and the PyTorch workflow, classification, computer vision, custom datasets, modular code, transfer learning and experiment tracking, then replicates the Vision Transformer paper and deploys the FoodVision Mini model to the web.

It has 4.6 stars from more than 6,400 reviews and more than 50,000 students. It runs about 52 hours and was updated in February 2026. Basic Python is required.

Best for learners who want a project-based TensorFlow path. It covers tensors, regression and classification, CNNs, transfer learning with TensorFlow Hub, NLP with RNNs, LSTMs and GRUs, and time series, through milestone projects such as Food Vision, which classifies 101 foods, and SkimLit, which classifies medical abstracts.

It has 4.5 stars from more than 12,000 reviews and more than 90,000 students. It runs about 62 hours, the longest course here, with two quizzes, and was updated in February 2026.

Best for learners who want to master CNNs for images and text. It covers modelling image data, building CNNs in TensorFlow 2, batch normalisation, dropout and data augmentation, VGG-style architectures, custom image datasets, embeddings and text classification CNNs for NLP.

It has 4.7 stars from more than 7,400 reviews and more than 51,000 students. It runs about 14 hours with four quizzes and a practice test, and was updated in September 2026.

Best for learners who want to build their first neural network from scratch. It extends logistic regression to multiple classes with softmax, derives backpropagation, implements networks in NumPy and then TensorFlow, and ends with a facial expression recognition project.

It has 4.7 stars from more than 10,000 reviews and more than 64,000 students. It runs about 12 hours with four quizzes and was updated in August 2026. Calculus, matrices and linear models are expected.

Best for learners who know backpropagation and want to train networks better. It covers momentum, adaptive learning rates, dropout and batch normalisation, then TensorFlow, Theano, PyTorch and Keras, training on GPUs on AWS and testing on the MNIST dataset.

It has 4.7 stars from more than 3,700 reviews and more than 44,000 students. It runs about 11 hours, the shortest course here, and was updated in September 2026. You should be able to write a neural network in NumPy first.

Best for beginners who want intuition and real business case studies. It explains each model before coding it: neural networks for customer churn, CNNs for image recognition, RNNs for Google stock prices, self-organising maps for fraud detection, and Boltzmann machines and autoencoders for recommender systems, using TensorFlow, PyTorch and AWS.

It has 4.6 stars from more than 49,000 reviews and more than 411,000 students, the most on this list. It runs about 23 hours and was updated in June 2026. High school maths and basic Python are enough.

Best for learners who want to truly understand how deep learning works. Using PyTorch and Google Colab, it combines the maths of deep learning with implementation and an experimental approach, covering CNNs, transfer learning and generative models, with many exercises, projects and code challenges and an 8-hour Python appendix.

It has 4.8 stars from more than 6,600 reviews and more than 57,000 students. It runs about 57 hours and was updated in August 2026. It is not a quick overview.