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

Packt Publishing, 2020. — 740 p. — ISBN: 978-18388202992nd.ed.Updated and revised second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problemsKey FeaturesUpdated 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 applicationsBook DescriptionMachine learning is a subset of artificial intelligence that aims to make modern-day computer systems more intelligent. The real power of machine learning lies in its algorithms, which make even the most difficult things capable of being handled by machines.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 how to practically apply complex techniques like Maximum Likelihood Estimation, Hebbian Learning, 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 learnLearn the characteristics of a machine learning algorithm, its possibilities and limits
Implement algorithms from supervised, semi-supervised, unsupervised, and RL domains
Learn how regression works in practical domains like time-series analysis and risk prediction
Create, model and train complex probabilistic models
Successfully cluster high-dimensional data and evaluate model accuracy
Discover how artificial neural networks work and how to train, optimize, and validate them
Work with Autoencoders, Hebbian Networks, and Generative Adversarial NetworksWho This Book Is ForThis 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.