Machine Learning with Scikit-Learn

Learn to use scikit-learn, the popular open-source Python library, to build efficient machine learning models.

Introduction

• Effective machine learning with scikit-learn
• What you should know before you start
• Using the exercise files

1. Input and Loading Data

• What is machine learning?
• Why use scikit-learn for machine learning?

2. Supervised Learning

• What is supervised learning?
• How to format data for scikit-learn
• Linear regression using scikit-learn
• Train test split
• Logistic regression using scikit-learn
• Logistic regression for multiclass classification
• Decision trees using scikit-learn
• How to visualize decision trees using Matplotlib
• Bagged trees using scikit-learn
• Random forests using scikit-learn
• Which machine learning model should you use?

3. Unsupervised Learning

• What is unsupervised learning?
• K-means clustering
• Principal component analysis (PCA) for data visualization
• PCA to speed up machine learning algorithms
• scikit-learn pipelines

Conclusion

• Get started with scikit-learn