Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: concepts, tools, and techniques to build intelligent systems

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 ; Representation learning and generative learning using autoencoders and GANs ; Reinforcement learning ; Training and deploying TensorFlow models at scale ; Exercise solutions ; Machine learning project checklist ; SVM dual problem ; Autodiff ; Other popular ANN architectures ; Special data structures ; TensorFlow graphs.