Complete Data Preprocessing in Python: Hands-on Data Science

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Common Approach to EDA and Data Pre-processing

Lecture 3: Practical Approach to EDA and Data Pre-processing

Lecture 4: Practice Exercises

Lecture 5: Getting started with Google Colaboratory

Lecture 6: Reading your data in Google Colab

Chapter 2: Data Preprocessing: Feature Scaling

Lecture 1: Section Intro

Lecture 2: What is Feature Scaling?

Lecture 3: Why Feature Scaling is needed?

Lecture 4: Scaling Treatment Options

Lecture 5: Advantages and Disadvantages of Scaling

Lecture 6: Getting started with the Data

Lecture 7: The Normal Distribution

Lecture 8: The StandardScaler

Lecture 9: The MinMaxScaler

Lecture 10: The RobustScaler

Lecture 11: Important note related to next tutorial

Lecture 12: Data Leakage

Lecture 13: Practice Exercise Part 1

Lecture 14: Solution: Practice Exercise Part 1

Chapter 3: Data Preprocessing: Missing Value Treatment

Lecture 1: Section Intro

Lecture 2: What are missing values?

Lecture 3: Common mistakes to be avoided

Lecture 4: Getting started with the Data

Lecture 5: Dropping Missing Values

Lecture 6: Filling Missing Values

Lecture 7: Forward Fill and Backward Fill

Lecture 8: The Simple Imputer

Lecture 9: KNN Intuition

Lecture 10: The KNN Imputer

Lecture 11: The Iterative Imputer

Lecture 12: A closer look at Missing Values

Lecture 13: Recognizing Missing Values Hands-on

Chapter 4: Data Preprocessing: Outlier Treatment

Lecture 1: Section Intro

Lecture 2: What are Outliers?

Lecture 3: Why to treat Outliers?

Lecture 4: Choosing the Outlier Treatment

Lecture 5: Getting started with the Data

Lecture 6: Detecting Outliers Using Tukey's Approach

Lecture 7: Remove Outlier Rows

Lecture 8: Replace Outliers with the Median

Lecture 9: Feature Transformation for Outliers

Lecture 10: Winsorization

Lecture 11: Algorithmic Treatment

Lecture 12: Practice Exercise Part 2

Lecture 13: Solution: Practice Exercise Part 2

Chapter 5: Data Preprocessing: Multicollinearity Treatment

Lecture 1: Section Intro

Lecture 2: What is Multicollinearity?

Lecture 3: Why to treat Multicollinearity?

Lecture 4: Choices for Multicollinearity Treatment

Lecture 5: Common mistakes to be avoided

Lecture 6: What is Variance Inflation Factor(VIF)?

Lecture 7: Getting started with the Data

Lecture 8: Dropping Correlated Variables

Lecture 9: Eliminating Highly Correlated Features using VIF

Lecture 10: Practice Exercise Part 3

Lecture 11: Solution: Practice Exercise Part 3

Chapter 6: Data Preprocessing: Feature Selection

Lecture 1: What is Feature Selection?

Lecture 2: Variance Threshold

Lecture 3: Select K Best

Lecture 4: Recursive Feature Elimination

Lecture 5: Select From Model

Lecture 6: Sequential Feature Selector

Lecture 7: Getting started with the data

Lecture 8: Supervised Feature Selection

Lecture 9: Hands-on Variance Threshold

Lecture 10: Hands-on Select K Best

Lecture 11: Hands-on Recursive Feature Elimination

Lecture 12: Hands-on Select From Model

Lecture 13: Hands-on Sequential Feature Selector

Chapter 7: Data Preprocessing: Feature Encoding

Lecture 1: Section Intro

Lecture 2: What is Feature Encoding?

Lecture 3: Why to perform Feature Encoding?

Lecture 4: Encoding Choices

Lecture 5: Label Encoding

Lecture 6: One Hot Encoding

Lecture 7: Ordinal and Custom Encoding

Lecture 8: Target Encoding

Lecture 9: Common mistakes to be avoided

Lecture 10: Getting started with the Data

Lecture 11: Label Encoder Hands-on

Lecture 12: One Hot Encoder Hands-on (sklearn)

Lecture 13: One Hot Encoder Hands-on (pandas)

Lecture 14: Encoding Features with High Cardinality

Lecture 15: Ordinal Encoder Hands-on

Lecture 16: Custom Encoder Hands-on

Lecture 17: Target Encoder Hands-on