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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  1. As mentioned, can you add more signals to our stock prediction system, such as the performance of other major indexes? Does this improve prediction?
  2. Recall that I briefly mentioned several major stock indexes besides DJIA. Is it possible to improve on the DJIA price prediction model we just developed by considering the historical prices and performances of these major indexes? It's highly likely! The idea behind this is that no stock or index is isolated and that there are weak or strong influences between stocks and different financial markets. This should be intriguing to explore.
  3. Can you try to ensemble linear regression and SVR, for example, averaging the prediction, and see if you can improve the prediction?