Scikit, No Tears
Preface
This book is to teach students how to use scikit-learn. To follow along and execute the code samples, you will need Docker installed. The Docker container is located on Docker Hub. After you have installed Docker, you may run the container as follows.
docker run -it \
-p 8888:8888 \
oneoffcoder/book-scikit-intro
Note that this Docker container has Jupyter Lab running on port 8888
. You may access Jupyter Lab at http://localhost:8888 when the Docker container is running.
- 1. Generating Data
- 2. Visualizing data
- 3. Regression
- 4. Logistic Regression
- 5. Poisson Regression
- 6. Classification
- 7. Clustering
- 8. Nearest Neighbors
- 9. Dimensionality Reduction
- 10. Pairwise Metrics
- 11. Data Preprocessing
- 11.1. Binarizer
- 11.2. K-bins discretizer
- 11.3. Label binarizer
- 11.4. Label encoder
- 11.5. Multi-label binarizer
- 11.6. Maximum absolute scaler
- 11.7. Min-max scaler
- 11.8. Normalizer
- 11.9. One-hot encoder
- 11.10. Ordinal encoder
- 11.11. Polynomial features
- 11.12. Power transformer
- 11.13. Robust scaler
- 11.14. Standard scaler
- 11.15. Column transformation
- 12. Imputation
- 13. Pipelines
- 14. Validation
- 15. Hyperparameter Tuning
- 16. Feature Extraction
- 17. Feature Selection
- 18. Customized Estimators
About
One-Off Coder is an educational, service and product company. Please visit us online to discover how we may help you achieve life-long success in your personal coding career or with your company’s business goals and objectives.
Copyright
Cite this book as follows.:
@misc{oneoffcoder_scikit_intro_2019,
title={Scikit, No Tears},
url={https://learn-scikit.oneoffcoder.com},
author={One-Off Coder},
year={2019},
month={Oct}}