Book Image

Machine Learning Algorithms

Book Image

Machine Learning Algorithms

Overview of this book

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
Table of Contents (22 chapters)
Title Page
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

Summary


In this chapter, we have introduced some main concepts about machine learning. We started with some basic mathematical definitions, to have a clear view about data formats, standards, and kind of functions. This notation will be adopted in all the other chapters and it's also the most diffused in technical publications. We discussed how scikit-learn seamlessly works with multi-class problems, and when a strategy is preferable to another.

The next step was the introduction of some fundamental theoretical concepts about learnability. The main questions we tried to answer were: how can we decide if a problem can be learned by an algorithm and what is the maximum precision we can achieve. PAC learning is a generic but powerful definition that can be adopted when defining the boundaries of an algorithm. A PAC learnable problem, in fact, is not only manageable by a suitable algorithm but is also fast enough to be computed in polynomial time. Then we introduced some common statistical learning...