Introduction to Machine Learning with Python
by Sarah Guido, Andreas C. Müller
Introduction to Machine Learning with Python
- Preface
- 1. Introduction
- 2. Supervised Learning
- 3. Unsupervised Learning and Preprocessing
- 4. Representing Data and Engineering Features
- 4.1. Categorical Variables
- 4.2. OneHotEncoder and ColumnTransformer: Categorical Variables with scikit-learn
- 4.3. Convenient ColumnTransformer creation with make_columntransformer
- 4.4. Binning, Discretization, Linear Models, and Trees
- 4.5. Interactions and Polynomials
- 4.6. Univariate Nonlinear Transformations
- 4.7. Automatic Feature Selection
- 4.8. Utilizing Expert Knowledge
- 4.9. Summary and Outlook
- 5. Model Evaluation and Improvement
- 6. Algorithm Chains and Pipelines
- 7. Working with Text Data
- 7.1. Types of Data Represented as Strings
- 7.2. Example Application: Sentiment Analysis of Movie Reviews
- 7.3. Representing Text Data as a Bag of Words
- 7.4. Stopwords
- 7.5. Rescaling the Data with tf–idf
- 7.6. Investigating Model Coefficients
- 7.7. Bag-of-Words with More Than One Word (n-Grams)
- 7.8. Advanced Tokenization, Stemming, and Lemmatization
- 7.9. Topic Modeling and Document Clustering
- 7.10. Summary and Outlook
- 8. Wrapping Up
- Index
Introduction to Machine Learning with Python
A Guide for Data Scientists
Andreas C. Müller and Sarah Guido
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