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Python Machine Learning By Example

You're reading from   Python Machine Learning By Example Unlock machine learning best practices with real-world use cases

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Product type Paperback
Published in Jul 2024
Publisher Packt
ISBN-13 9781835085622
Length 518 pages
Edition 4th Edition
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Author (1):
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Yuxi (Hayden) Liu Yuxi (Hayden) Liu
Author Profile Icon Yuxi (Hayden) Liu
Yuxi (Hayden) Liu
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Table of Contents (18) Chapters Close

Preface 1. Getting Started with Machine Learning and Python 2. Building a Movie Recommendation Engine with Naïve Bayes FREE CHAPTER 3. Predicting Online Ad Click-Through with Tree-Based Algorithms 4. Predicting Online Ad Click-Through with Logistic Regression 5. Predicting Stock Prices with Regression Algorithms 6. Predicting Stock Prices with Artificial Neural Networks 7. Mining the 20 Newsgroups Dataset with Text Analysis Techniques 8. Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling 9. Recognizing Faces with Support Vector Machine 10. Machine Learning Best Practices 11. Categorizing Images of Clothing with Convolutional Neural Networks 12. Making Predictions with Sequences Using Recurrent Neural Networks 13. Advancing Language Understanding and Generation with the Transformer Models 14. Building an Image Search Engine Using CLIP: a Multimodal Approach 15. Making Decisions in Complex Environments with Reinforcement Learning 16. Other Books You May Enjoy
17. Index

Summary

In this chapter, we worked on the project of predicting stock (specifically stock index) prices using machine learning regression techniques. Regression estimates a continuous target variable, as opposed to discrete output in classification

We started with a short introduction to the stock market and the factors that influence trading prices. We followed this with an in-depth discussion of three popular regression algorithms, linear regression, regression trees, and regression forests. We covered their definitions, mechanics, and implementations from scratch with several popular frameworks, including scikit-learn and TensorFlow, along with applications on toy datasets. You also learned the metrics used to evaluate a regression model. Finally, we applied what was covered in this chapter to solve our stock price prediction problem.

In the next chapter, we will continue working on the stock price prediction project, but with powerful neural networks. We will see whether...

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