Book Image

Python Machine Learning By Example - Second Edition

By : Yuxi (Hayden) Liu
Book Image

Python Machine Learning By Example - Second Edition

By: Yuxi (Hayden) Liu

Overview of this book

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.
Table of Contents (15 chapters)
Free Chapter
1
Section 1: Fundamentals of Machine Learning
3
Section 2: Practical Python Machine Learning By Example
12
Section 3: Python Machine Learning Best Practices

Exercises

  • Do you think all of the top 500 word tokens contain valuable information? If not, can you impose another list of stop words?
  • Can you use stemming instead of lemmatization to process the newsgroups data?
  • Can you increase max_features in CountVectorizer from 500 to 5000 and see how the t-SNE visualization will be affected?
  • Try visualizing documents from six topics (similar or dissimilar) and tweak parameters so that the formed clusters look reasonable.