Feature Encoding in ML: Beyond the Basics

Welcome to the sixteenth video of the series "Build your First Machine Learning Project". This is video is all about the Feature Encoding in Machine Learning.
Feature encoding is a process of converting categorical or non-numeric data into a numerical format that can be used as input for machine learning algorithms.
Many machine learning algorithms require input data to be in numerical form, and feature encoding is a crucial step in preparing data for these algorithms to make accurate predictions or classifications.
Let's understand it in deep.
Chapters
0:00 Intro to feature encoding
1:25 Various Approaches
1:40 First approach
3:16 Second approach
5:08 Third approach
7:09 Fourth approach
7:49 Conclusion
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Previous Lesson:
Isolation Forest: A Tree based approach for Outlier Detection : • Isolation Forest: A Tr...
Earlier Lessons:
1. Build your first ML Project: • Build Your FIRST Machi...
2. How to Formulate ML Problem: • Build Your First ML Pr...
3. Setup Python Environment: • Setup Python Environme...
4. Jupyter Notebook Tutorial: • Jupyter Notebook Tutor...
5. What is ML Modeling: • What is ML Modeling? (...
6. Reduce the size of Pandas Dataframe: • Reduce the memory size...
7. What is EDA: • Exploratory Data Analy...
8. How to impute missing Data: • How to handle missing ...
9. Mice Imputation Algorithm: • Multiple Imputation by...
10. How to impute missing data in categorical Variables: • How to impute missing ...
11. How to Detect Outliers with Z Score: • How to Detect Outliers...
12. Mahalanobis distance: • Why mahalanobis distan...
13. Cook's Distance: • Understanding Cooks Di...
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[@anbarasanethiran7182]

Sir, Your teaching way is awesome

[@machinelearningplus]

Thanks Anbarasan :-)

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