06. Handling Missing Values | Data Cleaning & Feature Engineering
Data Cleaning & Feature Engineering
Master one of the most important skills in Machine Learning—transforming raw, messy data into clean, feature-engineered, model-ready datasets.
This complete 24-part Data Cleaning & Feature Engineering course covers the entire data preprocessing workflow using the IBM Telco Customer Churn dataset. You'll learn how professional Data Scientists and Machine Learning Engineers explore datasets, identify data quality issues, clean real-world data, engineer meaningful features, prevent data leakage, encode categorical variables, scale numerical features, build reusable preprocessing pipelines, and prepare production-ready datasets for Machine Learning.
Every lesson combines intuitive explanations, visualizations, Python coding walkthroughs, and practical implementations using pandas and scikit-learn. Throughout the course, you'll build a reusable preprocessing pipeline following industry best practices that can be applied to any Machine Learning project.
Course Notebook
https://github.com/kader-xai/ml-cours...
If you enjoy this course, these playlists are a great next step:
Machine Learning Series
• Machine Learning Series
Scikit-Learn Series
• SciKit Learn Series
Machine Learning from Scratch
• Machine Learning from Scratch
Data Science with Python
• Data Science with Python
AI Agents with LangGraph
• AI Agents with LangGraph
XGBoost for CyberDefense
• XGBoost for CyberDefense
Neural Network Optimization
• Neural Network Optimization
Hugging Face Transformers
• Hugging Face Transformers
PyTorch: Build Your Own GPT
• Pytorch : Build your own GPT
TensorFlow from Scratch
• Tensor Flow from scratch
Course Structure
FOUNDATIONS
01. Course Intro & Why Preprocessing
02. Understanding the Telco Dataset
03. EDA for Data Quality
04. Data Types — Identify & Fix
DATA CLEANING
05. Split First, Clean Second
06. Handling Missing Values
07. Duplicates & Text Cleaning
08. Outliers — IQR Capping
09. Dropping Leaky Features
FEATURE ENGINEERING
10. Feature Engineering Fundamentals
11. Ratios & Interactions
12. Binning & Discretization
13. Handling Skewed Features
14. Date & Time Feature Engineering
15. Categorical Encoding — One-Hot
16. Ordinal & Frequency Encoding
17. Target Encoding Done Right
FEATURE TRANSFORMATION
18. Feature Scaling
19. Correlation & Multicollinearity
20. Feature Selection Basics
PRODUCTION PIPELINES
21. scikit-learn Pipelines
22. ColumnTransformer
23. Saving Pipelines with joblib
24. Capstone — Full Telco Pipeline
Subscribe for more Machine Learning, Data Science, AI Engineering, Artificial Intelligence, Generative AI, LLM Engineering, Deep Learning, PyTorch, TensorFlow, CUDA, scikit-learn, and Python courses.
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