The ideal audience for this book is computer science students and professionals who want to acquire detailed knowledge of complex machine learning algorithms and applications. The approach is always pragmatic; however, the theoretical part requires some advanced mathematical skills that all graduates (in computer science, engineering, mathematics, or science) should have acquired. The book can be also utilized by more business-oriented professionals (such as CPOs and product managers) to understand how machine learning can be employed to improve existing products and businesses.
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Mastering Machine Learning Algorithms
Mastering Machine Learning Algorithms
Overview of this book
Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour.
Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn v0.19.1. You will also learn how to use Keras and TensorFlow 1.x to train effective neural networks.
If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems and use-cases, this is the book you need.
Table of Contents (17 chapters)
Preface
Free Chapter
Machine Learning Model Fundamentals
Introduction to Semi-Supervised Learning
Graph-Based Semi-Supervised Learning
Bayesian Networks and Hidden Markov Models
EM Algorithm and Applications
Hebbian Learning and Self-Organizing Maps
Clustering Algorithms
Ensemble Learning
Neural Networks for Machine Learning
Advanced Neural Models
Autoencoders
Generative Adversarial Networks
Deep Belief Networks
Introduction to Reinforcement Learning
Advanced Policy Estimation Algorithms
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