Code-along sessions move you from introductory machine learning concepts to concrete code.
Machine learning is moving from futuristic AI projects to data analysis on your desk. You need to go beyond nodding along in discussion to coding machine learning tasks. These videos skew away from heavy mathematics and focus on showing you how to turn introductory machine learning concepts into concrete code using Python, scikit-learn, and friends. Our focus is on stories, graphics and code that build your understanding of machine learning; we minimize pure mathematics.
You learn how to load and explore simple datasets; build, train, and perform basic learning evaluation for a few models; compare the resource usage of different models in code snippets and scripts; and briefly explore some of the software and mathematics behind these techniques.
Learn How To
- Build and apply simple classification and regression models
- Evaluate learning performance with train-test splits
- Evaluate learning performance with metrics tailored to classification and regression
- Evaluate the resource usage of your learning models
Who Should Take This Course
If you are becoming familiar with the basic concepts of machine learning and you want an experienced hand to help you turn those concepts into running code, this course is for you. If you have some coding knowledge but want to see how Python can drive basic machine learning models and practice, this course is for you.
- A basic understanding of programming in Python (variables, basic control flow, simple scripts)