logoIntroduction toMachine Learnin

Module 1: Machine Learning Terminology

In this module, we will explain the different branches of machine learning and introduce the steps needed to build a model by constructing baseline models.

0Module Learning Outcomes

1What is Supervised Machine Learning?

2Is it Machine Learning?

3Types of Machine Learning

4 Supervised vs. Unsupervised Learning

5Classification vs. Regression

6Classification vs. Regression

7 Classification vs. Regression Target

8Tabular Data and Terminology

9Terminology: Target

10Terminology: Features

11Describing a Dataset

12Separating Our Data

13Baselines: Training a Model using Scikit-learn

14Fit or Predict

15First Step in Building a Model

16Building a Model

17Baselines: Dummy Regression

18Dummy Regressors

19Dummy Regressor Scores

20Building a Dummy Regressor

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.