22. ColumnTransformer in Practice | 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. #DataCleaning #FeatureEngineering #DataPreprocessing #MachineLearning #DataScience #Python #Pandas #NumPy #ScikitLearn #EDA #FeatureSelection #FeatureScaling #DataWrangling #ArtificialIntelligence #AIEngineering #MLEngineering #ChurnPrediction #Kaggle #PythonTutorial #machinelearningcourse