Linear Regression Explained | Full Deep Dive in ML from Scratch to GPU- Math and Model Training скачать в хорошем качестве

Linear Regression Explained | Full Deep Dive in ML from Scratch to GPU- Math and Model Training

Most people use Linear Regression… without really understanding what happens inside. In this deep dive, we break Linear Regression down from first principles — no black box thinking. We start with the simple equation y = mx + b, then build everything step-by-step: • Method of Least Squares (Normal Equation) • Gradient Descent (how models actually learn) with Demo • MSE and loss functions • Train / Validation / Test split • Feature scaling • Overfitting explained • Ridge (L2), Lasso (L1), Elastic Net with Demo • Model Accuracy R², Adjusted R² and p-values • Vector form (Xθ) • From NumPy to scikit-learn • CPU vs GPU training (PyTorch demo) By the end of this video, Linear Regression will not feel like a black box anymore. This is part of the Machine Learning Deep Dive Series. Next video: Logistic Regression. #LinearRegression #MachineLearning #DeepLearning #GradientDescent #LeastSquares #DataScience #RidgeRegression #LassoRegression #ElasticNet #Python #PyTorch #scikitlearn #Statistics #ArtificialIntelligence 00:00 Introduction – Why Most People Don’t Understand Linear Regression 02:15 What Is Linear Regression? (y = mx + b) 06:07 Method of Least Squares (Closed Form Solution) 09:04 Predictions and Error Explained 13:07 Mean Squared Error (MSE) 14:36 Gradient Descent - Why 15:20 Gradient Descent 18:06 Gradient Descent Calculation by hand 20:46 Gradient Descent Colab Notebook 21:10 Features & Multi-Variable Linear Regression 30:56 Feature Scaling & Overfitting 39:35 L2 Regularisation , L1 Regularisation & Elasticnet 48:05 Vector Notation 51:01 Coding LR in numpy and demo 01:00:40 Types of Linear Regression 01:03:00 Coding all LR model in scikit-learn 01:07:48 Model Evaluation – MSE, R², Adjusted R², p-value 01:11:22 CPU vs GPU – PyTorch Demo 01:16:16 Final Recap – No More Black Box 01:19:28 What’s Next – Logistic Regression Links: Gradient Descent : https://github.com/guptnava/youtube_d... https://colab.research.google.com/dri... Multivariable Linear Regression: https://github.com/guptnava/youtube_d... https://colab.research.google.com/dri... Training using Numpy: https://github.com/guptnava/youtube_d... https://colab.research.google.com/dri... Training Models using scikit-learn: https://github.com/guptnava/youtube_d... https://colab.research.google.com/dri... Training Models on CPU and GPU comparison: https://github.com/guptnava/youtube_d... https//colab.research.google.com/drive/1xVgI70b5hpG4iPXZ9GsCuzMYWkbc3TMD?usp=sharing