Machine Learning | Dr Jain Classes | L1 Course Introduction & Syllabus | Handwritten notes tutorial

Welcome to Dr Jain Classes for CSE. This is L1 Course Introduction & Syllabus for the Machine Learning Series. In this video, I have discussed what topics I will be covering in the complete series of the machine learning series of lectures. Covered topics: 1. Course Content 2. What is learning? 3. Is learning always successful? 4. Examples of successful and unsuccessful learning 5. Email classifier 6. Piegon rat example. 7. Hypothesis 8. Why do we need machine learning? 9. Training/Test/Val split 10. Training Dataset, Testing Dataset, Validation Dataset 11. Definition of the best hypothesis 12. Generalization in Machine Learning Please like, share, and subscribe to download the pdf file. Full Playlist: https://www.youtube.com/playlist?list=PLyn-p9dKO9gIQf9TkwP99yHGIk7YeAc0L pdf file: https://drive.google.com/file/d/1dAoDg3bATS1mM8fafnC06AnTkrQ-vJsg/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