Hands-On Machine Learning

Hands-On Machine Learning

by Aurélien Géron

Chapter	Topic
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
10	Introduction to Artificial Neural Networks with Keras
11	Training Deep Neural Networks
12	Custom Models and Training with TensorFlow
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	Training and Deploying TensorFlow Models at Scale

Chapter 1: The Machine Learning Landscape

Whats Is Machine Learning?

• Machine Learning is the field of study that gives computers the ability to learn without being explicitly programmed
• A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E

Types of Machine Learning Systems

• Supervised learning
• Unsupervised learning
• Semisupervised learning

Chapter 2: End-to-End Machine Learning Project

Steps Taken in the Example Project

• Look at the big picture.
• Get the data.
• Discover and visualize the data to gain insights.
• Prepare the data for Machine Learning algorithms.
• Select a model and train it.
• Fine-tune your model.
• Present your solution.
• Launch, monitor, and maintain your system

Chapter 3: Classification

Confusion Matrix

Markdown	Less	Pretty
Still	renders	nicely
1	2	3