5 1 Data Preprocessing Introduction Applied Machine Learning Varada Kolhatkar Ubc

June 5, 2026
Media Summary: What is the fundamental goal of supervised Relevant arguments for kNNs, pros and cons of kNNs, parametric and non-parametric Corresponding notebook: ... Train, validation, test splits, "deployment"

5 1 Data Preprocessing Introduction Applied Machine Learning Varada Kolhatkar Ubc - Detailed Analysis & Overview

What is the fundamental goal of supervised Relevant arguments for kNNs, pros and cons of kNNs, parametric and non-parametric Corresponding notebook: ... Train, validation, test splits, "deployment" Limitations of K-Means, DBSCAN motivation Related course Github page: Parameters and hyperparameters, Decision boundaries Corresponding notebook: ... Motivation for Ensembles Corresponding notebook: TBD Course Github page:

Motivation for hyperparameter optimization Corresponding notebook: TBD Course Github page: ... Motivation for model interpretation Corresponding notebook: TBD Course Github page:

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5.1 Data Preprocessing Introduction [Applied Machine Learning || Varada Kolhatkar || UBC]
1.0 Machine Learning Introduction [Applied Machine Learning || Varada Kolhatkar || UBC]
10.1 Preprocessing Housing Price Dataset [Applied Machine Learning || Varada Kolhatkar || UBC]
5.3 Introduction to Scikit-Learn Pipelines [Applied Machine Learning || Varada Kolhatkar || UBC]
6.1 Scikit-Learn ColumnTransformer [Applied Machine Learning || Varada Kolhatkar || UBC]
3.1 Generalization [Applied Machine Learning || Varada Kolhatkar || UBC]
4.3 kNNs continued [Applied Machine Learning || Varada Kolhatkar || UBC]
3.2 Data Splitting [Applied Machine Learning || Varada Kolhatkar || UBC]
15.1 DBSCAN Motivation [Applied Machine Learning || Varada Kolhatkar || UBC]
9.4 Addressing Class Imbalance [Applied Machine Learning || Varada Kolhatkar || UBC]
2.1 Machine Learning Terminology [Applied Machine Learning || Varada Kolhatkar || UBC]
2.4 More Terminology [Applied Machine Learning || Varada Kolhatkar || UBC]

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5.1 Data Preprocessing Introduction [Applied Machine Learning || Varada Kolhatkar || UBC]

5.1 Data Preprocessing Introduction [Applied Machine Learning || Varada Kolhatkar || UBC]

A quick

1.0 Machine Learning Introduction [Applied Machine Learning || Varada Kolhatkar || UBC]

1.0 Machine Learning Introduction [Applied Machine Learning || Varada Kolhatkar || UBC]

What is

10.1 Preprocessing Housing Price Dataset [Applied Machine Learning || Varada Kolhatkar || UBC]

10.1 Preprocessing Housing Price Dataset [Applied Machine Learning || Varada Kolhatkar || UBC]

Preprocessing

5.3 Introduction to Scikit-Learn Pipelines [Applied Machine Learning || Varada Kolhatkar || UBC]

5.3 Introduction to Scikit-Learn Pipelines [Applied Machine Learning || Varada Kolhatkar || UBC]

A quick

6.1 Scikit-Learn ColumnTransformer [Applied Machine Learning || Varada Kolhatkar || UBC]

6.1 Scikit-Learn ColumnTransformer [Applied Machine Learning || Varada Kolhatkar || UBC]

An

3.1 Generalization [Applied Machine Learning || Varada Kolhatkar || UBC]

3.1 Generalization [Applied Machine Learning || Varada Kolhatkar || UBC]

What is the fundamental goal of supervised

4.3 kNNs continued [Applied Machine Learning || Varada Kolhatkar || UBC]

4.3 kNNs continued [Applied Machine Learning || Varada Kolhatkar || UBC]

Relevant arguments for kNNs, pros and cons of kNNs, parametric and non-parametric Corresponding notebook: ...

3.2 Data Splitting [Applied Machine Learning || Varada Kolhatkar || UBC]

3.2 Data Splitting [Applied Machine Learning || Varada Kolhatkar || UBC]

Train, validation, test splits, "deployment"

15.1 DBSCAN Motivation [Applied Machine Learning || Varada Kolhatkar || UBC]

15.1 DBSCAN Motivation [Applied Machine Learning || Varada Kolhatkar || UBC]

Limitations of K-Means, DBSCAN motivation Related course Github page: https://github.com/

9.4 Addressing Class Imbalance [Applied Machine Learning || Varada Kolhatkar || UBC]

9.4 Addressing Class Imbalance [Applied Machine Learning || Varada Kolhatkar || UBC]

A quick

2.1 Machine Learning Terminology [Applied Machine Learning || Varada Kolhatkar || UBC]

2.1 Machine Learning Terminology [Applied Machine Learning || Varada Kolhatkar || UBC]

Basic terminology used in

2.4 More Terminology [Applied Machine Learning || Varada Kolhatkar || UBC]

2.4 More Terminology [Applied Machine Learning || Varada Kolhatkar || UBC]

Parameters and hyperparameters, Decision boundaries Corresponding notebook: ...

11.1 Ensembles: Motivation [Applied Machine Learning || Varada Kolhatkar || UBC]

11.1 Ensembles: Motivation [Applied Machine Learning || Varada Kolhatkar || UBC]

Motivation for Ensembles Corresponding notebook: TBD Course Github page: https://github.com/

5.2 Imputation and Scaling [Applied Machine Learning || Varada Kolhatkar || UBC]

5.2 Imputation and Scaling [Applied Machine Learning || Varada Kolhatkar || UBC]

A quick

14.1 Clustering Motivation [Applied Machine Learning || Varada Kolhatkar || UBC]

14.1 Clustering Motivation [Applied Machine Learning || Varada Kolhatkar || UBC]

Unsupervised

8.1 Hyperparameter Optimization Motivation [Applied Machine Learning || Varada Kolhatkar || UBC]

8.1 Hyperparameter Optimization Motivation [Applied Machine Learning || Varada Kolhatkar || UBC]

Motivation for hyperparameter optimization Corresponding notebook: TBD Course Github page: ...

15.2 DBSCAN [Applied Machine Learning || Varada Kolhatkar || UBC]

15.2 DBSCAN [Applied Machine Learning || Varada Kolhatkar || UBC]

Introduction

12.1 Model Interpretation Motivation [Applied Machine Learning || Varada Kolhatkar || UBC]

12.1 Model Interpretation Motivation [Applied Machine Learning || Varada Kolhatkar || UBC]

Motivation for model interpretation Corresponding notebook: TBD Course Github page: https://github.com/