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 worked on classifying clothing images using CNNs. We started with a detailed explanation of individual components of a CNN model and learned how CNNs are inspired by the way our visual cells work. We then developed a CNN model to categorize fashion-MNIST clothing images from Zalando. We also talked about data augmentation and several popular image augmentation methods. We practiced implementing deep learning models again with the Keras module in TensorFlow.

In the next chapter, we will focus on another type of deep learning networks: Recurrent Neural Networks (RNNs). CNNs and RNNs are the two most powerful deep neural networks that make deep learning so popular nowadays.