Python Machine Learning by Example

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 Naive 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.

What you will learn:

Understand the important concepts in ML and data science
Use Python to explore the world of data mining and analytics
Scale up model training using varied data complexities with Apache Spark
Delve deep into text analysis and NLP using Python libraries such NLTK and Gensim
Select and build an ML model and evaluate and optimize its performance
Implement ML algorithms from scratch in Python, TensorFlow 2, PyTorch, and scikit-learn
Table of contents:

Chapter 1: Getting Started with Machine Learning and Python
Chapter 2: Building a Movie Recommendation Engine with Naive Bayes
Chapter 3: Recognizing Faces with Support Vector Machine
Chapter 4: Predicting Online Ad Click-Through with Tree-Based Algorithms
Chapter 5: Predicting Online Ad Click-Through with Logistic Regression
Chapter 6: Scaling Up Prediction to Terabyte Click Logs
Chapter 7: Predicting Stock Prices with Regression Algorithms
Chapter 8: Predicting Stock Prices with Artificial Neural Networks
Chapter 9: Mining the 20 Newsgroups Dataset with Text Analysis Techniques
Chapter 10: Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling
Chapter 11: Machine Learning Best Practices
Chapter 12: Categorizing Images of Clothing with Convolutional Neural Networks
Chapter 13: Making Predictions with Sequences Using Recurrent Neural Networks
Chapter 14: Making Decisions in Complex Environments with Reinforcement Learning