Book description
- Work with simple and complex datasets common to Scikit-Learn
- Manipulate data into vectors and matrices for algorithmic processing
- Become familiar with the Anaconda distribution used in data science
- Apply machine learning with Classifiers, Regressors, and Dimensionality Reduction
- Tune algorithms and find the best algorithms for each dataset
- Load data from and save to CSV, JSON, Numpy, and Pandas formats
Table of contents
- Cover
- Front Matter
- 1. Introduction to Scikit-Learn
- 2. Classification from Simple Training Sets
- 3. Classification from Complex Training Sets
- 4. Predictive Modeling Through Regression
- 5. Scikit-Learn Classifier Tuning from Simple Training Sets
- 6. Scikit-Learn Classifier Tuning from Complex Training Sets
- 7. Scikit-Learn Regression Tuning
- 8. Putting It All Together
- Back Matter
Product information
- Title: Hands-on Scikit-Learn for Machine Learning Applications: Data Science Fundamentals with Python
- Author(s):
- Release date: November 2019
- Publisher(s): Apress
- ISBN: 9781484253731
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