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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Analyzing movie review sentiment with RNNs

So, here comes our first RNN project: movie review sentiment. We'll use the IMDb ( movie review dataset ( as an example. It contains 25,000 highly polar movie reviews for training, and another 25,000 for testing. Each review is labeled as 1 (positive) or 0 (negative). We'll build our RNN-based movie sentiment classifier in the following three sections: Analyzing and preprocessing the movie review data, Developing a simple LSTM network, and Boosting the performance with multiple LSTM layers.

Analyzing and preprocessing the data

We'll start with data analysis and preprocessing, as follows:

  1. We import all necessary modules from TensorFlow:
    >>> import tensorflow as tf
    >>> from tensorflow.keras.datasets import imdb
    >>> from tensorflow.keras import layers, models, losses, optimizers
    >>> from tensorflow...