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)

Getting started with classification

Spam email detection is basically a machine learning classification problem. We herein get started with learning important concepts of machine learning classification. Classification is one of the main instances of supervised learning in machine learning. Given a training set of data containing observations and their associated categorical outputs, the goal of classification is to learn a general rule that correctly maps the observations (also called features) to the targeted categories. In another word, a trained classification model will be generated by learning from the features and targets of training samples, as shown in the diagram below. When new or unseen data comes in, it will be able to determine their desired memberships. Class information will be predicted based on the known input features using the trained classification model.

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