4. Regression in Machine Learning

ML Lectures Playlist: https://youtube.com/playlist?list=PLGWXNgjLi7BTp_T4HU-KkbHBerAE8gRp4&si=Jc00z8S92vhNuzlN TL;DR: 🎓 Dive into the world of Regression with Dr. Andrey Holz and explores fundamental to advanced regression concepts. This lecture introduces the regression problem and explores different model types, starting with basic linear and decision tree models and expanding to advanced approaches such as Ridge, Lasso, and ElasticNet. You’ll learn to set up, validate, and perfect your models, so they’re ready to take on real-world data challenges. Here’s what’s on the agenda: 🔍 What is Regression? Why do we care? (Think house prices, sales forecasts, and stock market trends!) 💡 Basic Models: Get comfy with Linear and Decision Tree regressions. 🧩 Multicollinearity Got You Down? Learn tools like VIF to tackle this common issue. 📊 Model Metrics: We’ll talk MSE, RMSE, R², and all the magic numbers that tell you how your model is doing. 🎛 Advanced Regularization: Check out Ridge, Lasso, and ElasticNet to keep your models lean and mean! 🛠 Hands-On Demos! Real coding examples show you how to go from data to predictions like a pro. 💻‍💻 Tools: Python, Scikit-Learn, Statsmodels, Google Colab ✏️ Timestamps: 00:00 Intro 00:22 Agenda 01:08 Regression Problem Definition 02:18 Basic Regression Models 03:15 Decision Tree Regressor 04:10 Linear Regression 05:02 Ordinary Least Squares (OLS) - Historical Background 06:22 Ordinary Least Squares (OLS) Definition 07:05 Ordinary Least Squares (OLS) Exact Solution 07:52 Gauss-Markov Assumptions 08:20 Maximum Likelihood Estimation (MLE) Definition 08:54 Log-Likelihood Function 09:44 MLE Assumptions 10:41 OLS vs MLE for Linear Regression 11:03 Multicollinearity Problem 12:30 Regularization 12:36 Regularization - Ridge (L2) 13:30 Regularization - LASSO (L1) 14:00 ElasticNet : Ridge (L2) + LASSO (L1) 14:26 Comparison of Ridge, LASSO, ElasticNet 14:37 Handling Data 15:10 Metrics 16:20 Practical Considerations 17:14 Interpretability of Linear Regression 17:39 Python Libs for Regression Problems 18:05 Advanced Regression Models Overview 19:28 Coding + Examples: sklearn Linear Regression, Ridge, Lasso; Statmodels OLS; LASSO feature Selection. 27:02 Summary and Wrap-Up 28:00 Final thoughts This video is ideal for anyone looking to deepen their understanding of regression, from data preparation to interpreting complex models. 👍 Don’t forget to like, subscribe, and hit the 🔔 notification bell for more data science tutorials! #MachineLearning #Regression #DataScience #Python #ModelEvaluation #Regularization #CodingTutorial