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
In order to make the best out of this, please watch this series in the order in playlist: Build Your First ML Model Playlist: • Build Your FIRST Machine Learning Project ...
Previous Lesson:
Isolation Forest: A Tree based approach for Outlier Detection : • Isolation Forest: A Tree based approach fo...
Earlier Lessons:
1. Build your first ML Project: • Build Your FIRST Machine Learning Project ...
2. How to Formulate ML Problem: • Build Your First ML Project part 2: How t...
3. Setup Python Environment: • Setup Python Environment using ANACONDA
4. Jupyter Notebook Tutorial: • Jupyter Notebook Tutorial - How to Install...
5. What is ML Modeling: • What is ML Modeling? (Problem statement an...
6. Reduce the size of Pandas Dataframe: • Reduce the memory size of Pandas Dataframe...
7. What is EDA: • Exploratory Data Analysis (EDA) - Use thes...
8. How to impute missing Data: • How to handle missing data for machine lea...
9. Mice Imputation Algorithm: • Multiple Imputation by Chained Equations (...
10. How to impute missing data in categorical Variables: • How to impute missing data in categorical ...
11. How to Detect Outliers with Z Score: • How to Detect Outliers with Z Score | Clea...
12. Mahalanobis distance: • Why mahalanobis distance is incredibly pow...
13. Cook's Distance: • Understanding Cooks Distance to detect inf...
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