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

Bonaccorso, Giuseppe

ISBN 10: 1785889621 / ISBN 13: 9781785889622

Edité par Packt Publishing, 2017

A propos de cet article

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Détails bibliographiques

• Titre: Machine Learning Algorithms: A reference guide to popular algorithms for data science and machine learning
• Éditeur: Packt Publishing
• Date de parution: 2017
• Langue: anglais
• ISBN à 10 chiffres: 1785889621
• ISBN à 13 chiffres: 9781785889622
• Reliure: Couverture souple
• État de l'article: acceptable

A propos de ce titre

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.

À propos de l?auteur

Giuseppe Bonaccorso is Head of Data Science in a large multinational company. He received his M.Sc.Eng. in Electronics in 2005 from University of Catania, Italy, and continued his studies at University of Rome Tor Vergata, and University of Essex, UK. His main interests include machine/deep learning, reinforcement learning, big data, and bio-inspired adaptive systems. He is author of several publications including Machine Learning Algorithms and Hands-On Unsupervised Learning with Python, published by Packt.