Machine Learning | Dr Jain Classes | L4 Linear Regression | Handwritten notes tutorial | B/M TECH

Welcome to Dr Jain Classes for CSE. This is L4 Linear Regression for the Machine Learning Series. Covered topics: 1. Iterative Algorithm Rosen Blat (1958) 2. Perceptron Intuition 3. Linear Regression 4. Difference between loss and error in ML 5. Empirical Risk Minimization (ERM) 6. Logistic Regression 7. Halfspace classifier, Linear Regression, Logistic Regression 8. VC Dimension properties. Please like, share, and subscribe to download the pdf file. ML Playlist: https://www.youtube.com/playlist?list=PLyn-p9dKO9gIQf9TkwP99yHGIk7YeAc0L BTech CSE Notes, Syllabus and GATE Notes Playlist - https://www.youtube.com/playlist?list=PLyn-p9dKO9gLh4EqBkM-86-Aout3llxoz pdf file for this lecture: https://drive.google.com/file/d/16kD8e9l9vBuYUd99J0MT_mDlDFZANtXF/view?usp=sharing Channel Description: Hi Students, here I will share my lecture videos in advanced concepts in Computer Science and Engineering. Please subscribe to my channel, share my videos, and like them. You can ask me to my email ID if you want any kind of support related to concepts, tutorials, learning in CSE. I help students with: 1. Providing handwritten notes for CSE Subjects (AI, ML, DL, Graph Theory Applications, Discrete Maths, Cryptography, Operating Systems, Data Structures using C, Algorithms, DCN, Theory of Computation, Digital Logic, and Databases ) 2. Providing Counseling for GATE CSE. 3. IIT Ph.D. and MTech Interview preparation 4. SOP and SOR preparation. 5. Abroad Collabs for universities. 6. Handwritten notes, Slides and PPTs, course design, Academic consultancy 7. Btech & Mtech projects in machine learning, computer vision, and Deep Learning using google colab and many other related problems in academia. Feel free to email me. Tags: Machine learning, Artificial Intelligence, ML notes, AI notes, handwritten notes, last-minute exam guide, Machine Learning tutorial, beginner to advanced machine learning, gate cse, dr jain classes, linear regression, logistic regression, gradient descent, SGD, risk minimization, SVM, support vector machines, kernel trick, kernels, decision trees, random forest, neural networks, perception algorithm, backpropagation, model selection and validation, k fold cross-validation, training, testing, validation dataset, Unsupervised machine learning, supervised machine learning, nearest neighbor, KNN, k means clustering, clustering, PCA, dimensionality reduction, Bayes, Generative models, MLE, LDA, Maximum likelihood estimator, linear discriminant analysis, DNN, CNN, RNN, No free lunch theorem in machine learning, VC dimensions, PAC learning, APAC learning, bias-variance tradeoff