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Python Machine Learning by Example

You're reading from   Python Machine Learning by Example Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn

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Product type Paperback
Published in Oct 2020
Publisher Packt
ISBN-13 9781800209718
Length 526 pages
Edition 3rd 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 (17) Chapters Close

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

Exercise

  1. As mentioned earlier, we extracted user-movie relationships only from the movie rating data where most ratings are unknown. Can you also utilize data from the files movies.dat and users.dat?
  2. Practice makes perfect—another great project to deepen your understanding could be heart disease classification. The dataset can be downloaded directly at https://www.kaggle.com/ronitf/heart-disease-uci, or from the original page at https://archive.ics.uci.edu/ml/datasets/Heart+Disease.
  3. Don't forget to fine-tune the model you obtained from Exercise 2 using the techniques you learned in this chapter. What is the best AUC it achieves?
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Python Machine Learning by Example - Third Edition
Published in: Oct 2020
Publisher: Packt
ISBN-13: 9781800209718
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