Get the most out of this course. In this video, discover what background knowledge would be beneficial to have before starting this course.
- [Instructor] There are a few things I'd like you to be aware of as we get started. First, you should understand basic Python data structures, such as lists, tuples, and dictionaries. Additionally, you should have knowledge of Python libraries such as pandas and matplotlib. If you'd like to know more about these libraries, you can check out my course, Python for Data Visualization. Don't worry if you feel your background knowledge could be better. Throughout the course, I'll offer a host of resources to fill in knowledge gaps. Regardless of your background, you'll still be able to follow the course and learn how to create machine learning algorithms with scikit-learn.
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- Why use scikit-learn?
- Supervised vs. unsupervised learning
- Linear and logistic regression
- Decision trees and random forests
- K-means clustering
- Principal component analysis (PCA)