Mastering Machine Learning Algorithms Second Edition

Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work

Giuseppe Bonaccorso 지음

Packt(GCO Science) 2020년 01월 31일 출간

작품소개

책소개

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

▶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. 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

▶Key Features
？Updated to include new algorithms and techniques
？Code updated to Python 3.8 & TensorFlow 2.x
？New coverage of regression analysis, time series analysis, deep learning models, and cutting-edge applications

▶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.

목차

▶TABLE of CONTENTS
？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
？Chapter 24: Introduction to Reinforcement Learning
？Chapter 25: Advanced Policy Estimation Algorithms

책 속으로

▶What this book covers
？ Chapter 1, Machine Learning Models Fundamentals, explains the most important theoretical concepts regarding machine learning models, including bias, variance, overfitting, underfitting, data normalization, and scaling.

？ Chapter 2, Loss Functions and Regularization, continues the exploration of fundamental concepts focusing on loss functions and discussing their properties and applications. The chapter also introduces the reader to the concept of regularization, which plays a fundamental role in the majority of supervised methods.

？ Chapter 3, Introduction to Semi-Supervised Learning, introduces the reader to the main elements of semi-supervised learning, discussing the main assumptions and focusing on generative algorithms, self-training, and cotraining.

？ Chapter 4, Advanced Semi-Supervised Classification, discusses the most important inductive and transductive semi-supervised classification methods, which overcome the limitations of simpler algorithms analyzed in Chapter 3.

？ Chapter 5, Graph-Based Semi-Supervised Learning, continues the exploration of semi-supervised learning algorithms belonging to the families of graph-based and manifold learning models. Label propagation and non-linear dimensionality reduction are analyzed in different contexts, providing some effective solutions that can be immediately exploited using scikit-learn functionalities.

？ Chapter 6, Clustering and Unsupervised Models, introduces some common and important unsupervised algorithms, such as k-Nearest Neighbors (based on K-d trees and Ball Trees), K-means (with K-means++ initialization). Moreover, the chapter discusses the most important metrics that can be employed to evaluate a clustering result.

？ Chapter 7, Advanced Clustering and Unsupervised Models, continues the discussion of more complex clustering algorithms, like spectral clustering, DBSCAN, and fuzzy clustering, which can solve problems that simpler methods fail to properly manage.

？ Chapter 8, Clustering and Unsupervised Models for Marketing, introduces the reader to the concept of biclustering, which can be employed in marketing contexts to create recommender systems. The chapter also presents the Apriori algorithm, which allows us to perform Market Basket Analysis on extremely large transaction databases.

？ Chapter 9, Generalized Linear Models and Regression, discusses the main concept of generalized linear models and how to perform different kinds of regression analysis (including regularized, isotonic, polynomial, and logistic regressions).

？ Chapter 10, Introduction to Time-Series Analysis, introduces the reader to the main concepts of time-series analysis, focusing on the properties of stochastic processes and on the fundamental models (AR, MA, ARMA, and ARIMA) that can be employed to perform effective forecasts.

？ Chapter 11, Bayesian Networks and Hidden Markov Models, introduces the concepts of probabilistic modeling using direct acyclic graphs, Markov chains, and sequential processes. The chapter focuses on tools like PyStan and algorithms like HMM, which can be employed to model temporal sequences.

？ Chapter 12, The EM Algorithm, explains the generic structure of the Expectation- Maximization (EM) algorithm. We discuss some common applications, such as generic parameter estimation, MAP and MLE approaches, and Gaussian mixture.

？ Chapter 13, Component Analysis and Dimensionality Reduction, introduces the reader to the main concepts of Principal Component Analysis, Factor Analysis, and Independent Component Analysis. These tools allow us to perform effective component analysis with different kinds of datasets and, if necessary, also a dimensionality reduction with controlled information loss.
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▶ Preface
In the last few years, machine learning has become an increasingly important field in the majority of industries. Several processes once considered impossible to automate are now completely managed by computers, allowing human beings to focus on more creative tasks. This revolution has been made possible by the dramatic improvement of standard algorithms, together with a continuous reduction in hardware prices. The complexity that was a huge obstacle only a decade ago is now a problem that even a personal computer can solve. The general availability of highlevel open source frameworks has allowed everybody to design and train extremely powerful models.

The main goal of the second edition of Mastering Machine Learning Algorithms is to introduce the reader to complex techniques (such as semi-supervised and manifold learning, probabilistic models, and neural networks), balancing mathematical theory with practical examples written in Python (using the most advanced and common frameworks). I wanted to keep a pragmatic approach, focusing on the applications but never forgetting the theoretical foundations. A solid knowledge of this field, in fact, can be acquired only by understanding the underlying logic, which is always expressed using mathematical concepts. This extra effort is rewarded with a more solid awareness of every specific choice and helps the reader understand how to apply, modify, and improve all the algorithms in specific business contexts.

Machine learning is an extremely wide field and it's impossible to cover all the topics in a book. In this case, I've done my best to cover a selection of algorithms belonging to supervised, semi-supervised, unsupervised, and reinforcement learning, providing all the references necessary to further explore each of them. The examples have been designed to be easy to understand without any deep insight into the code; in fact, I believe it's more important to show general cases and let the reader improve and adapt them to cope with particular scenarios. I apologize for mistakes: even though many revisions have been made, it's possible that some details (both in the formulas and in the code) got away.

In particular, the second edition corrects some typos and mistakes present in the first one, improves the readability of some complex topics, and is based on the most recent version of production-ready frameworks (like TensorFlow 2.0). Given the overall complexity of the work, I apologize since despite the hard work of the author and all editors, it's always possible to find imprecisions or errors.

I've finished this book in a particular period of my life and I'd like to dedicate it to my father, an artist and art professor, who has been always a guide for me, teaching me how it's always possible to join scientific rigor with an artistic approach. At the end of the day, data science needs creativity and, conversely, creativity can find in data science an extremely fertile soil!

작가정보

저자(글) Giuseppe Bonaccorso

Giuseppe Bonaccorso is Head of Data Science in a large multinational company. He received his M.Sc.Eng. in Electronics in 2005 from University of Catania, Italy, and continued his studies at University of Rome Tor Vergata, and University of Essex, UK. His main interests include machine/deep learning, reinforcement learning, big data, and bio-inspired adaptive systems. He is author of several publications including Machine Learning Algorithms and Hands-On Unsupervised Learning with Python, published by Packt.