Welcome to a comprehensive journey through modern machine learning! This resource explores the core principles, powerful algorithms, and cutting-edge methods that shape today’s intelligent systems.
Beginning with foundational topics such as linear models, decision trees, and kernel methods, we gradually introduce more advanced concepts, including deep dives into neural networks and deep learning techniques like CNNs, RNNs, Transformers, and generative models such as GANs and VAEs. You’ll also explore self-supervised learning and the rise of diffusion models in generative AI.
Discover how to uncover hidden structures in data through dimensionality reduction and latent variable modeling, using techniques ranging from PCA and ICA to matrix factorization and probabilistic approaches.
Whether you’re just starting out or deepening your expertise, this guide provides a solid foundation and a forward-looking perspective on the evolving field of machine learning.