Introduction To Machine Learning With Python: A Guide For Data Scientists Python Andreas C. Muller &%
User Manual:
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- Copyright
- Table of Contents
- Preface
- Chapter 1. Introduction
- Chapter 2. Supervised Learning
- Chapter 3. Unsupervised Learning and Preprocessing
- Chapter 4. Representing Data and Engineering Features
- Chapter 5. Model Evaluation and Improvement
- Chapter 6. Algorithm Chains and Pipelines
- Chapter 7. Working with Text Data
- Types of Data Represented as Strings
- Example Application: Sentiment Analysis of Movie Reviews
- Representing Text Data as a Bag of Words
- Stopwords
- Rescaling the Data with tf–idf
- Investigating Model Coefficients
- Bag-of-Words with More Than One Word (n-Grams)
- Advanced Tokenization, Stemming, and Lemmatization
- Topic Modeling and Document Clustering
- Summary and Outlook
- Chapter 8. Wrapping Up
- Index
- About the Authors
- Colophon