Introduction to Applied Linear Models

Summary

Describe common machine learning techniques and evaluate their effectiveness.

Description

Linear modeling is a popular starting point for machine learning studies for a number of reasons. Generally, these models are relatively easy to interpret and explain, and they can be applied to a broad range of problems. In this course, you will learn how to choose, apply, and evaluate commonly used linear modeling techniques. As you work through the course, you can put your new skills to practice in 5 hands-on labs.

Learning objectives

  • Describe and evaluate linear regression for regression problems.

  • Describe how to ensure machine learning models generalize to out-of-sample data.

  • Describe and evaluate logistic regression for classification problems.

  • Practice using linear modeling techniques using the Databricks Data Science Workspace.

Prerequisites

  • Intermediate experience with machine learning (experience using machine learning and data science libraries like scikit-learn and Pandas, knowledge of linear models).

  • Intermediate experience using the Databricks Workspace to perform data analysis (using Spark DataFrames, Databricks notebooks, etc.).

  • Beginning experience with statistical concepts commonly used in data science.

Learning path

  • This course is part of the Data Scientist learning path.

Proof of completion

  • Upon 80% completion of this course, you will receive a proof of completion.