Dive into a comprehensive 13-hour course on Feature Engineering, covering essential techniques for data preprocessing in machine learning. Learn how to perform One Hot Encoding for multi-categorical variables, explore different encoding methods, and understand the importance of feature scaling. Master handling missing values in categorical features, dealing with high-cardinality categories using Count/Frequency Encoding, and implementing Ordinal Encoding for ordinal categories. Participate in live sessions covering various techniques for missing value imputation, categorical feature handling, and Probability Ratio Encoding. Explore standardization and transformation techniques, discuss strategies for handling imbalanced datasets, and examine the impact of outliers on machine learning use cases. Gain insights into various feature transformation types and learn a step-by-step process for Exploratory Data Analysis (EDA) and Feature Engineering in data science 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
