Mastering Machine Learning Algorithms (eBook)

Expert techniques to implement popular machine learning algorithms and fine-tune your models

Giuseppe Bonaccorso c/o Quandoo (Autor)

eBook Download: EPUB

2018
576 Seiten
Packt Publishing (Verlag)
978-1-78862-590-6 (ISBN)

Infos

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

Key Features

• Discover 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 more

Book Description

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. 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 learn

• Explore how a ML model can be trained, optimized, and evaluated
• Understand how to create and learn static and dynamic 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 and Generative Adversarial Networks
• Apply label spreading and propagation to large datasets
• Explore the most important Reinforcement Learning techniques

Who this book is for

This 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.

Giuseppe Bonaccorso is an experienced team leader/manager in Artificial Intelligence and Machine/Deep Learning solution design, management, and delivery. He got his M.Sc.Eng. in Electronics Engineering in 2005 from University of Catania, Italy and continued his studies at the University of Rome Tor Vergata, Italy and the University of Essex, UK. His main interests include Machine/Deep Learning, Reinforcement Learning, bio-inspired adaptive systems, and Neural Language Processing.

---

Explore and master the most important algorithms for solving complex machine learning problems.About This BookDiscover 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 moreWho 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.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 techniquesIn DetailMachine 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.Style and approachA hands-on guide filled with real-world examples of popular algorithms used for data science and machine learning

Produkt-Details

• Erscheint lt. Verlag: 25.5.2018
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Datenbanken
• Themenwelt: Mathematik / Informatik ► Informatik ► Theorie / Studium
• Themenwelt: Mathematik / Informatik ► Informatik ► Web / Internet
• Schlagworte: Decision Tree • Deep learning • HMM Model • machine learning • Machine Learning Algorithm • ML model • neural network • Semi-syupervised Learning • Unsupervised Learning
• ISBN-10: 1-78862-590-0 / 1788625900
• ISBN-13: 978-1-78862-590-6 / 9781788625906