Contents:Part I, The fundamentals of machine learning. The machine learning landscape ; End-to-end machine learning project ; Classification ; Training models ; Support vector machines ; Decision trees ; Ensemble learning and random forests ; Dimensionality reduction ; Unsupervised learning techniques -- Part II, Neural networks and deep learning. Introduction to artificial neural networks with Keras ; Training deep neural networks ; Custom models and training with TensorFlow ; Loading and preprocessing data with TensorFlow ; Deep computer vision using convolutional neural networks ; Processing sequences using RNNs and CNNs ; Natural language processing with RNNs and attention ; Autoencoders, GAN, and diffusion models ; Reinforcement learning ; Training and deploying TensorFlow models at scale ; Machine learning project checklist ; Autodiff ; Special data structures ; TensorFlow graphs.