Mastering Machine Learning Algorithms:Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work, 2nd Edition


Mastering Machine Learning Algorithms:Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work, 2nd Edition
by:Giuseppe Bonaccorso
pages 页数:798 pages
Publisher Finelybook 出版社:Packt Publishing (January 31, 2020)
Language 语言:English
ISBN-10 书号:1838820299
ISBN-13 书号:9781838820299

Book Description
Updated and revised second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems
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. Ranging 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 time-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

Contents
Preface
Chapter 1:Machine Learning Model Fundamentals
Chapter 2:Loss Functions and Regularization
Chapter 3:Introduction to Semi-Supervised Learning
Chapter 4:Advanced Semi-Supervised Classification
Chapter 5:Graph-Based Semi-Supervised Learning
Chapter 6:Clustering and Unsupervised Models
Chapter 7:Advanced Clustering and Unsupervised Models
Chapter 8:Clustering and Unsupervised Models for Marketing
Chapter 9:Generalized Linear Models and Regression
Chapter 10:Introduction to Time-Series Analysis
Chapter 11:Bayesian Networks and Hidden Markov Models
Chapter 12:The EM Algorithm
Chapter 13:Component Analysis and Dimensionality Reduction
Chapter 14:Hebbian Learning
Chapter 15:Fundamentals of Ensemble Learning
Chapter 16:Advanced Boosting Algorithms
Chapter 17:Modeling Neural Networks
Chapter 18:Optimizing Neural Networks
Chapter 19:Deep Convolutional Networks
Chapter 20:Recurrent Neural Networks
Chapter 21:Autoencoders
Chapter 22:Introduction to Generative Adversarial Networks
Chapter 23:Deep Belief Networks
Introduction to Reinforcement Chapter 24:Learning
Chapter 25:Advanced Policy Estimation Algorithms
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Index

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