Python Machine Learning By Example Second Edition
Implement machine learning algorithms and techniques to build intelligent systems
This is the code repository for my book Python Machine Learning By Example Second Edition, published by Packt, in March 2019. It contains all the supporting project files necessary to work through the book from start to finish.
What is this book about?
The surge in interest in machine learning (ML) is due to the fact that it revolutionizes automation by learning patterns in data and using them to make predictions and decisions. If you’re interested in ML, this book will serve as your entry point to ML.
Python Machine Learning By Example begins with an introduction to important ML concepts and implementations using Python libraries. Each chapter of the book walks you through an industry adopted application. You’ll implement ML techniques in areas such as exploratory data analysis, feature engineering, and natural language processing (NLP) in a clear and easy-to-follow way.
With the help of this extended and updated edition, you’ll understand how to tackle data-driven problems and implement your solutions with the powerful yet simple Python language and popular Python packages and tools such as TensorFlow, scikit-learn, gensim, and Keras. To aid your understanding of popular ML algorithms, the book covers interesting and easy-to-follow examples such as news topic modeling and classification, spam email detection, stock price forecasting, and more.
By the end of the book, you’ll have put together a broad picture of the ML ecosystem and will be well-versed with the best practices of applying ML techniques to make the most out of new opportunities.
This book covers the following exciting features:
- Exploit the power of Python to explore the world of data mining and data analytics
- Discover machine learning algorithms to solve complex challenges faced by data scientists today
- Use Python libraries such as TensorFlow and Keras to create smart cognitive actions for your projects
Table of contents:
Chapter 1: Getting Started with Machine Learning and Python Chapter 2: Exploring the 20 Newsgroups Dataset with Text Analysis Techniques Chapter 3: Mining the 20 Newsgroups Dataset with Clustering and Topic Modeling Algorithms Chapter 4: Detecting Spam Email with Naive Bayes Chapter 5: Classifying News Topic with Support Vector Machine Chapter 6: Predicting Online Ads Click-through with Tree-Based Algorithms Chapter 7: Predicting Online Ads Click-through with Logistic Regression Chapter 8: Scaling Up Prediction to Terabyte Click Logs Chapter 9: Stock Price Prediction with Regression Algorithms Chapter 10: Machine Learning Best Practices
Get to Know the Author
Yuxi (Hayden) Liu is an author of a series of machine learning books and an education enthusiast. His first book, the first edition of Python Machine Learning By Example, was a #1 bestseller in Amazon India in 2017 and 2018. His other books include R Deep Learning Projects and Hands-On Deep Learning Architectures with Python published by Packt.
He is an experienced data scientist who's focused on developing machine learning and deep learning models and systems. He has worked in a variety of data-driven domains and has applied his machine learning expertise to computational advertising, recommendation, and network anomaly detection. He published five first-authored IEEE transaction and conference papers during his master's research at the University of Toronto.