Train and understand regression models in machine learning - Episode 4

Join Jason DeBoever and Glenn Stephens live on Learn TV and explore this nine-part "Foundations of data science for machine learning" series. Each week, we will be walking through Learn modules and answering your questions live. From basic classical machine learning models to exploratory data analysis and customizing architectures, you'll be guided by easy to digest conceptual content and interactive Jupyter notebooks and will learn about the underlying concepts as well as how to get into building models with the most common machine learning tools.

Train and understand regression models in machine learning: Episode 04

Regression is arguably the most widely used machine learning technique, commonly underlying scientific discoveries, business planning, and stock market analytics. This learning material takes a dive into some common regression analyses, both simple and more complex, and provides some insight on how to assess model performance. In this episode, you will:

  • Understand how regression works.
  • Work with new algorithms: Linear regression, multiple linear regression, and polynomial regression.
  • Understand the strengths and limitations of regression models.
  • Visualize error and cost functions in linear regression.
  • Understand basic evaluation metrics for regression.