Learn to use scikit-learn, the popular open-source Python library, to build efficient machine learning models.
Overview
Syllabus
Introduction
- Effective machine learning with scikit-learn
- What you should know before you start
- Using the exercise files
- What is machine learning?
- Why use scikit-learn for machine 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?
- What is unsupervised learning?
- K-means clustering
- Principal component analysis (PCA) for data visualization
- PCA to speed up machine learning algorithms
- scikit-learn pipelines
- Get started with scikit-learn
Taught by
Michael Galarnyk and Madecraft
