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

Linear regression with scikit-learn and higher dimensionality


scikit-learn offers the class LinearRegression, which works with n-dimensional spaces. For this purpose, we're going to use the Boston dataset:

from sklearn.datasets import load_boston

>>> boston = load_boston()

>>> boston.data.shape
(506L, 13L)
>>> boston.target.shape
(506L,)

It has 506 samples with 13 input features and one output. In the following figure, there' a collection of the plots of the first 12 features:

Note

When working with datasets, it's useful to have a tabular view to manipulate data. pandas is a perfect framework for this task, and even though it's beyond the scope of this book, I suggest you create a data frame with the command pandas.DataFrame(boston.data, columns=boston.feature_names) and use Jupyter to visualize it. For further information, refer to Heydt M., Learning pandas - Python Data Discovery and Analysis Made Easy, Packt.

There are different scales and outliers (which can be...