Explore Sklearn Description
Explore Sklearn by Tracy Renee is divided into 7 different sections of greatly extensive guidance on how to be effective with Sklearn. Students will learn everything essential, ranging from theoretical principles of component analysis to how to deal with codes of semi-supervised classification and regression problems.
Here are what you will learn in this course:
- Introduction
- Introduction to Sklearn
- Supervised learning
- Sklearn classification models
- Supervised classification
- Sklearn regression models
- Supervised regression
- Semi-supervised learning
- Sklearn semi-supervised models
- Sklearn semi-supervised functions
- Semi-supervised classification
- Semi-supervised regression
- Unsupervised learning
- Sklearn unsupervised models
- Unsupervised breast cancer dataset
- Unsupervised wine dataset
- Principal component analysis
- Sklearn PCA models
- Principal component analysis
- Future selection
- Sklearn feature selection models
- SelectKBes
- SelectPercentile
- Other machine-learning topics
- Logistic Regression versus Decision Tree
- Machine learning life cycle