English | 2020 | ISBN: 1838820299 | 799 pages | True PDF EPUB MOBI | 109.03 MB

Updated and revised second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems

Key Features

Updated to include new algorithms and techniques

Code updated to Python 3.8 & TensorFlow 2.x

New coverage of regression analysis, series analysis, deep learning models, and cutting-edge applications

Book Description

Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains.

You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Rag from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks.

By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios.

What you will learn

Understand the characteristics of a machine learning algorithm

Implement algorithms from supervised, semi-supervised, unsupervised, and RL domains

Learn how regression works in -series analysis and risk prediction

Create, model, and train complex probabilistic models

Cluster high-dimensional data and evaluate model accuracy

Discover how artificial neural networks work – train, optimize, and validate them

Work with autoencoders, Hebbian networks, and GANs

Who this book is for

This book is for data science professionals who want to delve into complex ML algorithms to understand how various machine learning models can be built. Knowledge of Python programming is required.

Table of Contents

Machine Learning Model Fundamentals

Loss functions and Regularization

Introduction to Semi-Supervised Learning

Advanced Semi-Supervised Classifiation

Graph-based Semi-Supervised Learning

Clustering and Unsupervised Models

Advanced Clustering and Unsupervised Models

Clustering and Unsupervised Models for Marketing

Generalized Linear Models and Regression

Introduction to -Series Analysis

Bayesian Networks and Hidden Markov Models

The EM Algorithm

Component Analysis and Dimensionality Reduction

Hebbian Learning

Fundamentals of Ensemble Learning

Advanced Boosting Algorithms

Modeling Neural Networks

Optimizing Neural Networks

Deep Convolutional Networks

Recurrent Neural Networks

Auto-Encoders

Introduction to Generative Adversarial Networks

Deep Belief Networks

Introduction to Reinforcement Learning

Advanced Policy Estimation Algorithms