Key Features

• Learn the fundamentals of machine learning and build your own intelligent applications
• Master the art of building your own machine learning systems with this example-based practical guide
• Work with important classification and regression algorithms and other machine learning techniques

Book Description

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 ȓ 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.

What you will learn

• Exploit the power of Python to handle data extraction, manipulation, and exploration techniques
• Use Python to visualize data spread across multiple dimensions and extract useful features
• Dive deep into the world of analytics to predict situations correctly
• Implement machine learning classification and regression algorithms from scratch in Python
• Be amazed to see the algorithms in action
• Evaluate the performance of a machine learning model and optimize it
• Solve interesting real-world problems using machine learning and Python as the journey unfolds

About the Author

Yuxi (Hayden) Liu is currently a data scientist working on messaging app optimization at a multinational online media corporation in Toronto, Canada. He is focusing on social graph mining, social personalization, user demographics and interests prediction, spam detection, and recommendation systems. He has worked for a few years as a data scientist at several programmatic advertising companies, where he applied his machine learning expertise in ad optimization, click-through rate and conversion rate prediction, and click fraud detection. Yuxi earned his degree from the University of Toronto, and published five IEEE transactions and conference papers during his masters research. He finds it enjoyable to crawl data from websites and derive valuable insights. He is also an investment enthusiast.

Table of Contents

1. Getting Started with Python and Machine Learning
2. Exploring the 2 newsgroups data set
3. Spam email detection with Naive Bayes
4. News topic classification with Support Vector Machine
5. Click-through prediction with tree-based algorithms
6. Click-through rate prediction with logistic regression
7. Stock prices prediction with regression algorithms
8. Best practices