Complete ML Machine Learning in one shot | Semester Exam | Hindi скачать в хорошем качестве

Complete ML Machine Learning in one shot | Semester Exam | Hindi

💻 KnowledgeGate Website: https://www.knowledgegate.ai For free notes on University exam’s subjects, please check out our course: https://www.knowledgegate.ai/courses/... 📝 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/0029... 📧 Email: [email protected] ➡ One Shot Complete Playlist for GATE CSE Exam : 👇 ▶️ http://tiny.cc/GATEoneshotplaylist ➡ Our One Shot Semester Exam Videos: 👇 ▶ Operating System:    • Complete Operating System in one shot...   ▶ DBMS:    • Complete DBMS Data Base Management Sy...   ▶ Computer Network:    • Complete CN Computer Networks in one ...   ▶ Digital Electronics:    • Complete DE Digital Electronics in on...   ▶ Computer Architecture:    • Complete COA Computer Organization & ...   ▶ Data Structure:    • Complete DS Data Structure in one sho...   ▶ Algorithm:    • Complete DAA Design and Analysis of A...   ▶ Software Engineering:    • Complete Software Engineering in one ...   ▶ Theory of Computation:    • Complete TOC Theory of Computation in...   ▶ Compiler:    • Complete CD Compiler Design in one sh...   ▶ Discrete Maths:    • Complete DM Discrete Maths in one sho...   ▶ Artificial Intelligence:    • Complete AI Artificial Intelligence i...   ▶ Machine Learning:    • Complete ML Machine Learning in one s...   #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.