This course was created by Madecraft. We are pleased to host this content in our library.
- Why use scikit-learn?
- Supervised vs. unsupervised learning
- Linear and logistic regression
- Decision trees and random forests
- K-means clustering
- Principal component analysis (PCA)
Skill Level Advanced
- Machine learning is transforming industries and it's an exciting time to be in the field. A large amount of machine learning programs are written using open source Python library, Scikit-learn. Scikit-learn provides an easy to use streamlined API that provides efficient versions of a large number of common algorithms. And it makes it easy to train models. My name is Michael Galarnyk. I'm a data scientist, a machine learning instructor, and a blogger about all things data science. I'm also a big fan of Scikit-learn. In this course, I'll show you how to use several machine learning algorithms and when they're appropriate. I'll share with you how you can tune your models to better predict unseen data. So not only make your models better, but also help you understand the strengths and weaknesses of each algorithm. By the end of the course, you'll feel confident and ready to build your own powerful machine learning models using Scikit-learn. So if you're ready to dive in, then let's go.