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Module 8: Linear Models

This module will teach you about different types of linear models. You will learn how these models can be interpreted as well as their advantages and limitations.

0Module Learning Outcomes

1Introducing Linear Regression

2Linear Regression Questions

3True or False: Ridge

4Using Ridge

5Coefficients and coef_

6Linear Model Coefficient Questions

7True or False: Coefficients

8Interpreting Ridge

9Logistic Regression

10Logistic Regression Prediction

11True or False: Logistic Regression

12 Applying Logistic Regression

13Predicting Probabilities

14Probabilities and Logistic Regression

15True or False: predict_proba

16Applying predict_proba

17Multi-class Classification

18Multi-Class Questions

19True or False: Coefficients

20Multi Class Revisited

21What Did We Just Learn?

About this course

This course covers the data science perspective on the introductory concepts in machine learning, with a focus on making predictions. It covers how to build different models such as K-NN, decision trees and linear classifiers as well as important concepts such as data splitting and fundamental rules and laws. In addition, this course will teach you how to evaluate models properly and question their validity all while streamlining the process with pipelines.

About the program

The University of British Columbia (UBC) is a comprehensive research-intensive university, consistently ranked among the 40 best universities in the world. The Key Capabilities in Data Science program was launched in September 2020 and is developed and taught by many of the same instructors as the UBC Master of Data Science program.