PartⅠ. The Fundamentals of Machine Learning 1.The Machine Learning Landscape 2.End-to-End Machine Learning Project 3.Classification 4.Training Models 5.Support Vector Machines 6.Decision Trees 7.Ensemble Learning and Random Forests 8.Dimensionality Reduction 9.Unsupervised Learning Techniques
Part Ⅱ. Neural Networks and Deep Learning 10.Introduction to Artificial Neural Network with Keras 11.Training Deep Neural Networks 12.Custom Models and Traning with TensoFlow 13.Loading and Preprocessing Data with TensorFlow 14.Deep Computer Vision Using Convolutional Neural Networks 15.Processing Sequences Using RNNs and CNNs 16.Natural Language Processing with RNNs and Attention 17.Representation Learning and Generative Learning Using Autoencoders and GANs 18.Reinforcement Learning 19.Traning and Deploying TensorFlow Models at Scale
A. Exercise Solutions B. Machine Learning Project Checklist C. SVM Dual Problem D. Autodiff E. Other Popular ANN Architectures F. Special Data Structures G.TensorFlow Graphs