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

Data scaling and normalization


A generic dataset (we assume here that it is always numerical) is made up of different values which can be drawn from different distributions, having different scales and, sometimes, there are also outliers. A machine learning algorithm isn't naturally able to distinguish among these various situations, and therefore, it's always preferable to standardize datasets before processing them. A very common problem derives from having a non-zero mean and a variance greater than one. In the following figure, there's a comparison between a raw dataset and the same dataset scaled and centered:

This result can be achieved using the StandardScaler class:

from sklearn.preprocessing import StandardScaler

>>> ss = StandardScaler()
>>> scaled_data = ss.fit_transform(data)

It's possible to specify if the scaling process must include both mean and standard deviation using the parameters with_mean=True/False and with_std=True/False (by default they're both active...