Mastering Machine Learning Algorithms: Expert techniques to implement popular machine learning algorithms and fine-tune your models

by Giuseppe Bonaccorso

Explore and master the most important algorithms for solving complex machine learning problems.

Key FeaturesDiscover high-performing machine learning algorithms and understand how they work in depth. One-stop solution to mastering supervised, unsupervised, and semi-supervised machine learning algorithms and their implementation. Master concepts related to algorithm tuning, parameter optimization, and moreBook DescriptionMachine 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. You will also learn how to use Keras and TensorFlow 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.

What you will learnExplore how a ML model can be trained, optimized, and evaluatedUnderstand how to create and learn static and dynamic probabilistic modelsSuccessfully cluster high-dimensional data and evaluate model accuracyDiscover how artificial neural networks work and how to train, optimize, and validate themWork with Autoencoders and Generative Adversarial NetworksApply label spreading and propagation to large datasetsExplore the most important Reinforcement Learning techniquesWho this book is forThis book is an ideal and relevant source of content for data science professionals who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. A basic knowledge of machine learning is preferred to get the best out of this guide.

Table of ContentsMachine 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 Advanced Clustering and Feature Extraction Ensemble Learning Neural Networks for Machine Learning Advanced Neural Models Auto-Encoders Generative Adversarial Networks Deep Belief NetworksIntroduction to Reinforcement Learning Policy estimation algorithms

Genres: Artificial Intelligence, Nonfiction

578 pages, Kindle Edition

Published May 25, 2018

About the author

Experienced and goal-oriented senior executive leader with wide expertise in the management of Artificial Intelligence, Machine Learning, Deep Learning, and Data Science projects for healthcare, B2C and Military industries (Fortune 500 firms).

His main interests include Machine/Deep Learning, Reinforcement Learning, Advanced Analytics, Bio-inspired adaptive systems, Business Intelligence, Neuroscience, Neural Language Processing, Econometrics, Data Science Strategy and Organization.

Professional member of IEEE, IEEE Computer Society, AAAI, ACM, IAENG, AICA, SFIA, and Agile Manifesto.