Day 12 - Introduction, Missing Values & Categorical Variables (Intermediate ML Lessons 1, 2 & 3)

Welcome to Day 12 of Kaggle 30 Days of Machine Learning.

In this video, I will walk through Lessons 1, 2 and 3 of the Kaggle Intermediate Machine Learning course.

In Lesson 1, we are given a brief overview of the topics that will be covered in this course which includes missing values, categorical variables, pipelines, cross-validation, XGBoost and leakage. It is highly recommended that you first complete the Introduction to Machine Learning course before proceeding in this one - check out my past couple of videos!

In Lesson 2, we learn how to deal with missing values. Specifically, we look at two approaches: dropping columns with missing values as well as imputation, that is filling in the missing vales with some number.

In Lesson 3, we explore what a categorical variable is and the difference between ordinal variables and nominal variables. Furthermore, we also learn three approaches when working with categorical variables: dropping categorical variables, ordinal encoding and one-hot encoding.

Like and subscribe for more future videos!

Kaggle 30 Days of ML

Kaggle Intermediate Machine Learning micro-course

My article on imputing missing values

My article on encoding categorical variables using ordinal encoding and one-hot encoding

Follow me

#Kaggle #30DaysOfML