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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Predicting stock prices with neural networks

We will build the stock predictor with TensorFlow in this section. We will start with feature generation and data preparation, followed by network building and training. After that, we will fine-tune the network and incorporate early stopping to boost the stock predictor.

Training a simple neural network

We prepare data and train a simple neural work with the following steps:

  1. We load the stock data, generate features, and label the generate_features function we developed in Chapter 7, Predicting Stock Prices with Regression Algorithms:
    >>> data_raw = pd.read_csv('19880101_20191231.csv', index_col='Date')
    >>> data = generate_features(data_raw)
  2. We construct the training set using data from 1988 to 2018 and the testing set using data from 2019:
    >>> start_train = '1988-01-01'
    >>> end_train = '2018-12-31'