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, we continued working on the online advertising click-through prediction project. This time, we overcame the categorical feature challenge by means of the one-hot encoding technique. We then resorted to a new classification algorithm, logistic regression, for its high scalability to large datasets. The in-depth discussion of the logistic regression algorithm started with the introduction of the logistic function, which led to the mechanics of the algorithm itself. This was followed by how to train a logistic regression model using gradient descent.

After implementing a logistic regression classifier by hand and testing it on our click-through dataset, you learned how to train the logistic regression model in a more advanced manner, using SGD, and we adjusted our algorithm accordingly. We also practiced how to use the SGD-based logistic regression classifier from scikit-learn and applied it to our project.

We then continued to tackle problems we might...