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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

Getting started with classification

Movie recommendation can be framed as a machine learning classification problem. If it is predicted that you like a movie, for example, then it will be on your recommended list, otherwise, it won't. Let's get started by learning the important concepts of machine learning classification.

Classification is one of the main instances of supervised learning. Given a training set of data containing observations and their associated categorical outputs, the goal of classification is to learn a general rule that correctly maps the observations (also called features or predictive variables) to the target categories (also called labels or classes). Putting it another way, a trained classification model will be generated after the model learns from the features and targets of training samples, as shown in the first half of Figure 2.1. When new or unseen data comes in, the trained model will be able to determine their...

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Python Machine Learning by Example - Third Edition
Published in: Oct 2020
Publisher: Packt
ISBN-13: 9781800209718
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