The 10 best Deep Learning courses in 2026

Machine learning (ML) allows us to teach computers to make predictions and decisions based on data and learn from experiences. In recent years, incredible optimizations have been made to machine learning algorithms, software frameworks, and embedded hardware. Thanks to this, running deep neural networks and other complex machine learning algorithms is possible on low-power devices like microcontrollers. This course will give you a broad overview of how machine learning works, how to train neural networks, and how to deploy those networks to microcontrollers, which is known as embedded machine

The field of natural language processing (NLP) aims at getting computers to perform useful and interesting tasks with human language. This course introduces students to the 3 pillars underlying modern NLP: probabilistic language models, simple neural networks with a focus on gradient based learning, and vector-based meaning representations in the form of word embeddings. At the end of the course, students will be able to implement and analyze probabilistic language models based on N-grams, text classifiers using logistic regression and gradient-based learning, and vector-based approaches to wo

This course introduces the foundational concepts of large language models (LLMs) and deep learning techniques for text analysis, a critical skill set in today’s AI-driven landscape. As organizations increasingly rely on intelligent systems to process and interpret language data, understanding these technologies has become essential for modern professionals. Throughout the course, learners will explore how deep learning models analyze and extract meaning from textual data, gaining practical insights into real-world NLP applications. By studying the architecture and working principles of transf

The course provides a comprehensive exploration of how generative AI is reshaping software development by accelerating coding, improving debugging, and enhancing automation. It is designed for aspiring software engineers, developers, and professionals who want to integrate AI into modern development workflows to build efficient, scalable, and error-free applications. You will explore the role of large language models (LLMs) like GPT, Gemini, and LLaMA in coding tasks, software testing, and project automation. The course begins with foundational AI concepts—machine learning, deep learning, and

This course introduces learners to the core principles of artificial intelligence, including its history, definitions, and the role of data in AI. You’ll explore algorithms, specialized hardware, and delve into machine learning and deep learning fundamentals. Ethical considerations around AI, especially generative AI (GenAI), will also be discussed, ensuring you understand the broader impact AI has on society and the workforce. With clear explanations and real-world examples, the course ensures you not only learn theoretical concepts but also understand their practical implications. You’

Five courses on neural networks, CNNs, sequence models, and ML strategy from DeepLearning.AI.

Image processing, object detection, and deep learning with OpenCV.

Build and train neural networks with TensorFlow for vision, NLP, and time series.

Learn PyTorch from scratch — tensors, neural networks, computer vision, and model deployment.

Free, top-down deep learning course — build state-of-the-art models from lesson one.