Book Image

Python Machine Learning By Example

By : Yuxi (Hayden) Liu
Book Image

Python Machine Learning By Example

By: Yuxi (Hayden) Liu

Overview of this book

Data science and machine learning are some of the top buzzwords in the technical world today. A resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This book is your entry point to machine learning. This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Moving ahead, you will learn all the important concepts such as, exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation. With the help of various projects included, you will find it intriguing to acquire the mechanics of several important machine learning algorithms – they are no more obscure as they thought. Also, you will be guided step by step to build your own models from scratch. Toward the end, you will gather a broad picture of the machine learning ecosystem and best practices of applying machine learning techniques. Through this book, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple language, Python. Interesting and easy-to-follow examples, to name some, news topic classification, spam email detection, online ad click-through prediction, stock prices forecast, will keep you glued till you reach your goal.
Table of Contents (9 chapters)

What is regression?

Regression is another main instance of supervised learning in machine learning. Given a training set of data containing observations and their associated continuous outputs, the goal of regression is to explore the relationships between the observations (also called features) and the targets, and to output a continuous value based on the input features of an unknown sample.

The major difference between regression and classification is that the outputs in regression are continuous while discrete in classification. This leads to different application areas for these two supervised learning methods. Classification is basically used in determining desired memberships or characteristics, as we have seen in previous chapters, such as email being spam or not, news topics, and ad being clicked-through or not. Regression mainly involves estimating an outcome or forecasting a response.

Examples of machine...