Complete ML Course for Semester Exam | Hindi 📚

💻 KnowledgeGate Website: https://www.knowledgegate.ai

For free notes on University exam’s subjects, please check out our course: https://knowledgegate.ai/courses/FREE-SEMESTER-EXAM-COURSE

📝 Please message us on WhatsApp: https://wa.me/918000121313

➡ Contact Us: 👇
📞Call on: +91-8000121313
🟦 Telegram Updates: https://t.me/kg_gate
🟩 Whatsapp Updates: https://www.whatsapp.com/channel/0029VaC5Weq2Jl85NaTls63w
📧 Email: contact@knowledgegate.in

➡ One Shot Complete Playlist for GATE CSE Exam : 👇
▶️ http://tiny.cc/GATEoneshotplaylist

➡ Our One Shot Semester Exam Videos: 👇
▶ Operating System: https://youtu.be/xw_OuOhjauw
▶ DBMS: https://youtu.be/YRnjGeQbsHQ
▶ Computer Network: https://youtu.be/q3Z3Qa1UNBA
▶ Digital Electronics: https://youtu.be/pHNbm-4reIc
▶ Computer Architecture: https://youtu.be/DsK35f8wyUw
▶ Data Structure: https://youtu.be/MdG0Vw9f1A4
▶ Algorithm: https://youtu.be/z6DY_YSdyww
▶ Software Engineering: https://youtu.be/NlLM3sVF8wY
▶ Theory of Computation: https://youtu.be/9kuynHcM3UA
▶ Compiler: https://youtu.be/OQCjakjCJu4
▶ Discrete Maths: https://youtu.be/3zOtLEeHygg
▶ Artificial Intelligence: https://youtu.be/yiXAmkimZRQ
▶ Machine Learning: https://youtu.be/2oGsCHlfBUg

#knowledgegate #sanchitsir #sanchitjain
*********************************************************
Content in this video:
00:00 Chapter-0 (About this video)
01:45 Chapter-1 (INTRODUCTION)
1:23:31 Chapter-2 (REGRESSION & BAYESIAN LEARNING)
2:31:11 Chapter-3 (DECISION TREE LEARNING)
3:42:35 Chapter-4 (ARTIFICIAL NEURAL NETWORKS)
5:41:09 Chapter-5 (REINFORCEMENT LEARNING)


(UNIT-1 : INTRODUCTION) Learning, Types of Learning, Well defined learning problems, Designing a Learning System, History of ML, Introduction of Machine Learning Approaches - (Artificial Neural Network, Clustering, Reinforcement Learning, Decision Tree Learning, Bayesian networks, Support Vector Machine, Genetic Algorithm), Issues in Machine Learning and Data Science Vs Machine Learning.

(UNIT-2: REGRESSION & BAYESIAN LEARNING) REGRESSION: Linear Regression and Logistic Regression. BAYESIAN LEARNING - Bayes theorem, Concept learning, Bayes Optimal Classifier, Naïve Bayes classifier, Bayesian belief networks, EM algorithm. SUPPORT VECTOR MACHINE: Introduction, Types of support vector kernel - (Linear kernel, polynomial kernel,and Gaussiankernel), Hyperplane - (Decision surface), Properties of SVM, and Issues in SVM.

(UNIT-3: DECISION TREE LEARNING) DECISION TREE LEARNING - Decision tree learning algorithm, Inductive bias, Inductive inference with decision trees, Entropy and information theory, Information gain, ID-3 Algorithm, Issues in Decision tree learning. INSTANCE-BASED LEARNING - k-Nearest Neighbour Learning, Locally Weighted Regression, Radial basis function networks, Case-based learning.

(UNIT-4: ARTIFICIAL NEURAL NETWORKS) ARTIFICIAL NEURAL NETWORKS - Perceptron's, Multilayer perceptron, Gradient descent & the Delta rule, Multilayer networks, Derivation of Backpropagation Algorithm, Generalization, Unsupervised Learning - SOM Algorithm and its variant; DEEP LEARNING - Introduction, concept of convolutional neural network, Types of layers - (Convolutional Layers, Activation function, pooling, fully connected), Concept of Convolution (1D and 2D) layers, Training of network, Case study of CNN for eg on Diabetic Retinopathy, Building a smart speaker, Self-deriving car etc.

(UNIT-5: REINFORCEMENT LEARNING) REINFORCEMENT LEARNING-Introduction to Reinforcement Learning, Learning Task,Example of Reinforcement Learning in Practice, Learning Models for Reinforcement - (Markov Decision process, Q Learning - Q Learning function, @ Learning Algorithm ), Application of Reinforcement Learning,Introduction to Deep Q Learning. GENETIC ALGORITHMS: Introduction, Components, GA cycle of reproduction, Crossover, Mutation, Genetic Programming, Models of Evolution and Learning, Applications.