The course covers the learning outcomes and goals of feature engineering, including performing one hot encoding for multi-categorical variables, different types of feature engineering encoding techniques, the importance of feature scaling, handling missing values in categorical features, and techniques for handling ordinal and many categories in categorical features. The course teaches various feature engineering techniques such as count/frequency encoding, ordinal encoding, probability ratio encoding, standardization, and transformation techniques. The teaching method includes live sessions on handling missing values, categorical features, imbalanced datasets, outliers, and feature transformation, along with a step-by-step process in exploratory data analysis (EDA) and feature engineering in data science projects. The intended audience for this course includes data scientists, machine learning engineers, and anyone interested in enhancing their skills in feature engineering for machine learning projects.
Overview
Syllabus
Feature Engineering-How to Perform One Hot Encoding for Multi Categorical Variables.
Different Types of Feature Engineering Encoding Techniques.
Why Do We Need to Perform Feature Scaling?.
How To Handle Missing Values in Categorical Features.
Featuring Engineering- Handle Categorical Features Many Categories(Count/Frequency Encoding).
Featuring Engineering- How To Handle Ordinal Categories(Ordinal Encoding).
Live-Feature Engineering-All Techniques To Handle Missing Values- Day 1.
Live-Feature Engineering-All Techniques To Handle Missing Values- Day 2.
Live-Feature Engineering-All Techniques To Handle Missing Values- Day 3.
Live-Feature Engineering-All Techniques To Handle Categorical Features - Day 4.
Summary Live Streaming-Feature Engineering- Probability Ratio Encoding- Handling Categorical Feature.
Live-Feature Engineering-All Standardization And Transformation Techniques- Day 6.
Live Discussion On Handling Imbalanced Dataset- Machine Learning.
Live Discussion On Outlier And Its Impacts On Machine Learning UseCases.
Discussing All The Types Of Feature Transformation In Machine Learning.
Step By Step Process In EDA And Feature Engineering In Data Science Projects.
Taught by
Krish Naik
