Data Cleaning and Validation for Machine Learning with Python

Overview

This course ensures data integrity, feature selection, anomaly detection, and validation for ML models. The goal is to remove noisy, inconsistent, or biased data before training.

Syllabus

Unit 1: Data Validation in Python Using Pandas

Data Quality Checks for Ratings
Mastering Data Validation with Pandas
Ensure Data Integrity with Validation
Validating Employee Dataset Made Simple

Unit 2: Anomaly Detection in Python Using Isolation Forest

Detect Anomalies in Product Reviews
Identify and Fix Code Bugs
Enhance Anomaly Detection Skills

Unit 3: Data Drift Detection in Python

Adjust Significance for Healthcare Analysis
Ensuring Dataset Compatibility
Enhance the KS Test Function

Unit 4: Feature Selection in Python Using Scikit-Learn

Select Powerful Movie Features
Feature Selection for Employee Promotions
Debug Lasso Feature Selection
Experimenting with k in Feature Selection
Mastering Feature Selection Techniques

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