Data Science in 30 Days | Data Science Full Course Free | #datascience #fullcourse - Supervised Learning – Regression Models | Day 12/30 of Data Science in 30 Days | Full Course | #ml

Welcome back to The Data Key and Day 12 of our Data Science in 30 Days series! In today’s session, we’ll dive deep into one of the core concepts of Machine Learning — Supervised Learning, with a special focus on Regression Models. You’ll learn how machines predict continuous outcomes like house prices, salaries, or temperature — using real-life Python examples and scikit-learn demos. --------------------------------------------------------------------------------------------------------------------------------------------------------------- 🔍 In this video, you’ll learn: ✅ Introduction to Supervised Learning ✅ Difference between Supervised & Unsupervised Learning ✅ What is Regression & How It Works ✅ Types of Regression Models   • Linear Regression   • Multiple Linear Regression   • Polynomial Regression   • Ridge & Lasso Regression ✅ Implementation in Python (scikit-learn example) ✅ Evaluating Regression Models (MAE, MSE, R²) ✅ Practical Applications of Regression in Data Science --------------------------------------------------------------------------------------------------------------------------------------------------------------- 🧮 Python Code Resources: 📘 Linear Regression with Scikit-learn → https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html 📘 Regularization in Regression (Ridge & Lasso) → https://scikit-learn.org/stable/modules/linear_model.html#ridge-regression 📘 Model Evaluation Metrics → https://scikit-learn.org/stable/modules/model_evaluation.html 📚 Extra Learning Resources: 🔹 Machine Learning Crash Course by Google: https://developers.google.com/machine-learning/crash-course 🔹 Kaggle Intro to Machine Learning: https://www.kaggle.com/learn/intro-to-machine-learning 🔹 Hands-On Machine Learning Book (by Aurélien Géron): https://amzn.to/3SsyTKp 🔹 MIT OpenCourseWare – Intro to ML: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2020 -------------------------------------------------------------------------------------------------------------------------------------------------------------- 💡 About the Series: This video is part of our “Data Science in 30 Days” playlist, covering every key concept — from Python & Pandas to Machine Learning & AI Algorithms. 👉 Watch the full playlist : https://www.youtube.com/playlist?list=PL07H6FsxVmmwoYzMdi2TEcb2Q6hQ0g-EV --------------------------------------------------------------------------------------------------------------------------------------------------------------- OUTLINE: 00:00:00 : Day 12 Opening – Predicting With Regression 00:01:19 : Understanding Linear Regression 00:03:25 : Example – House Prices and Living Area 00:04:58 : Curves – Introducing Polynomial Regression (+ Engine Example) 00:06:43 : Metrics And Visual Checks – R², MSE, RMSE 00:08:37 : Code To Results – scikit‑learn + Wrap‑Up --------------------------------------------------------------------------------------------------------------------------------------------------------------- #supervised #learning #supervisedlearning #thedatakey #datascience #dataanalytics #machinelearning #bigdata #deeplearning #artificialintelligence #ai #datavisualization #thedatakey #datasciencewiththedatakey #learnwiththedatakey #coding #newvideo #subscribe #regression #regressionanalysis #regressiontesting #foryou #technology #education #technology #scikitlearn #scikit #freeclasses #freecourse #freecourses #fullcourse #course #datascienceroadmap #datasciencecourse