Machine Learning Algorithms: A reference guide to popular algorithms for data science and machine learning

Bonaccorso, Giuseppe

ISBN 10: 1785889621 / ISBN 13: 9781785889622

Published by Packt Publishing, 2017

About this Item

Seller Inventory # I-9781785889622

Bibliographic Details

• Title: Machine Learning Algorithms: A reference guide to popular algorithms for data science and machine learning
• Publisher: Packt Publishing
• Publication Date: 2017
• Language: English
• ISBN 10: 1785889621
• ISBN 13: 9781785889622
• Binding: Soft cover
• Condition: New

About this title

Synopsis

Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide

Key Features:

• Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide.

• Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation.

• Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide.

Book Description:

In this book, you will learn all the important machine learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. The algorithms that are covered in this book are linear regression, logistic regression, SVM, naïve Bayes, k-means, random forest, TensorFlow and feature engineering.

In this book, you will how to use these algorithms to resolve your problems, and how they work. This book will also introduce you to natural language processing and recommendation systems, which help you to run multiple algorithms simultaneously.

On completion of the book, you will know how to pick the right machine learning algorithm for clustering, classification, or regression for your problem

What You Will Learn:

• Acquaint yourself with the important elements of machine learning

• Understand the feature selection and feature engineering processes

• Assess performance and error trade-offs for linear regression

• Build a data model and understand how it

• Learn to tune the parameters of SVMs

• Implement clusters in a dataset

• Explore the concept of Natural Processing Language and Recommendation Systems

• Create a machine learning architecture from scratch

Who this book is for:

This book is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here.

About the Author

Giuseppe Bonaccorso is an experienced team leader/manager in AI, machine/deep learningsolution design, management, and delivery. He got his M.Sc.Eng. in Electronics in 2005from University of Catania, Italy, and continued his studies at University of Rome TorVergata and University of Essex, UK. His main interests include machine/deep learning,reinforcement learning, big data, bio-inspired adaptive systems, cryptocurrencies, andNLP.