Book Image

Python Machine Learning by Example, - Third Edition

By : Yuxi (Hayden) Liu
Book Image

Python Machine Learning by Example, - Third Edition

By: Yuxi (Hayden) Liu

Overview of this book

Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML). With six new chapters, on topics including movie recommendation engine development with Naïve Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements. At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries. Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP. By the end of this ML Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed in the best practices of applying ML techniques to solve problems.
Table of Contents (17 chapters)
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In this chapter, you learned the fundamental concepts of NLP as an important subfield in machine learning, including tokenization, stemming and lemmatization, and PoS tagging. We also explored three powerful NLP packages and worked on some common tasks using NLTK and spaCy. Then, we continued with the main project exploring newsgroups data. We began by extracting features with tokenization techniques and went through text preprocessing, stop word removal, and stemming and lemmatization. We then performed dimensionality reduction and visualization with t-SNE and proved that count vectorization is a good representation for text data.

We had some fun mining the newsgroups data using dimensionality reduction as an unsupervised approach. Moving forward, in the next chapter, we'll be continuing our unsupervised learning journey, specifically looking at topic modeling and clustering.