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Python Machine Learning By Example

Python Machine Learning By Example

By : Yuxi (Hayden) Liu, Ivan Idris
4.3 (30)
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Python Machine Learning By Example

Python Machine Learning By Example

4.3 (30)
By: Yuxi (Hayden) Liu, Ivan Idris

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 (15 chapters)
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Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Summary


In this chapter, we started with an introduction to a typical machine learning problem, online advertising click-through prediction and the challenges including categorical features. We then resorted to tree-based algorithms that can take in both numerical and categorical features. We then had an in-depth discussion on the decision tree algorithm: the mechanics, different types, how to construct a tree, and two metrics, Gini impurity and entropy, to measure the effectiveness of a split at a tree node. After constructing a tree in an example by hand, we implemented the algorithm from scratch. We also learned how to use the decision tree package from scikit-learn and applied it to predict click-through. We continued to improve the performance by adopting the feature-based bagging algorithm random forest. The chapter then ended with tips to tune a random forest model.

More practice is always good for honing skills. Another great project in the same area is the Display Advertising Challenge...

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