MACHINE LEARNING
5.0
(2)
41 learners
What you'll learn
This course includes
- 9.5 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 64 lessons • 9.5 hours of video
MACHINE LEARNING
64 lessons
• 9.5 hours
MACHINE LEARNING
64 lessons
• 9.5 hours
- #1 Introduction to Machine Learning - Definition & Example |ML| #machinelearning #ml #jntu #btech06:24
- #2 Well Posed Learning Problem in Machine Learning with Examples |ML|09:04
- #3 Perspectives and Issues in Machine Learning |ML|06:40
- #4 Designing A Learning System - Steps & Why we need a Design |ML|04:43
- #6 Choosing a Target Function : Step-2 In Designing A Learning System|ML|08:14
- #5 Choosing a Training Experience: Step-1 In Designing A Learning System|ML|13:10
- #7 Choosing a Representation for Target Function : Step-3 In Designing A Learning System|ML|06:49
- #8 Choosing a Learning Algorithm for Approximating the Target Function : Step-4 |ML|12:15
- #9 Final Design In Designing A Learning System|ML|06:51
- #10 Concept Learning - Introduction, Concept Learning As Task |ML|12:52
- #11 Concept Learning As Search With Example |ML|09:15
- #12 Find S Algorithm - Finding A Maximally Specific Hypothesis With Example |ML|11:49
- #13 Version Spaces - Algorithm to find Version Space With Example |ML|09:04
- #14 Candidate Elimination Algorithm With Example |ML|13:50
- #15 Inductive Bias - Remarks On Version Spaces & Candidate Elimination Algorithms With Example |ML|15:55
- #16 Decision Tree Learning - Example and Algorithm |Part-1||ML| #machinelearning #ml #jntu #btech10:59
- #17 Decision Tree Learning - Example and Algorithm |Part-2||ML|15:22
- #18 Decision Tree Learning - Example and Algorithm |Part-3||ML|07:16
- #19 Appropriate Problems For Decision Tree Learning |ML|06:10
- #20 Hypothesis Space Search in Decision Tree Learning |ML|10:17
- #22 Issues in Decision Tree Learning |ML|06:07
- #21 Inductive Bias in Decision Tree Learning |ML|07:09
- #23 Introduction to Artificial Neural Networks & their Representation of Neural Networks |ML|10:18
- #24 Appropriate Problems for Learning Neural Networks |ML|07:00
- #25 The Perceptron and The Perceptron training rule |ML|08:17
- #27 Multi Layer Neural Networks With Diagram |ML|04:26
- #26 Delta Rule & The Gradient Descent Algorithm |ML|14:00
- #28 Back Propagation Algorithm With Example Part-1 |ML| #machinelearning #ml #jntu #btech13:46
- #29 Back Propagation Algorithm With Example Part-2 |ML|08:06
- #30 Back Propagation Algorithm With Example Part-3 |ML|12:47
- #31 Remarks On Back Propagation Algorithm |ML|05:31
- #32 An Illustrative Example: Face Recognition in Neural Networks |ML|08:38
- #33 Advanced Topics in Neural Networks |ML|06:19
- #35 Sample Error and True Error In estimating accuracy of Hypothesis |ML|05:48
- #34 Evaluating The Hypothesis - Motivation, Estimating Hypothesis Accuracy |ML|12:17
- #36 Basics Of Sampling Theory |ML|13:27
- #37 Difference in Error Of Two Hypothesis - Hypothesis Testing - Type 1& Type 2 Errors |ML|07:22
- #38 Comparing Learning Algorithms in Machine Learning |ML|06:29
- #39 Bayes Theorem - With Proof & Example |ML|09:24
- #40 Bayes Theorem & Concept Learning |ML|09:12
- #41 Maximum Likelihood & Least Squared Error Hypothesis |ML|09:46
- #42 Minimum Description Length Principle |ML|06:42
- #43 Bayes Optimal Classifier with Example & Gibs Algorithm |ML|11:52
- #44 Naive Bayes Classifier With Example in Machine Learning |ML| #machinelearning #ml #jntu #btech08:22
- #45 Bayesian Belief Networks - DAG & CPT With Example |ML|14:41
- #46 EM Algorithm - Expectation Maximisation - Steps, Usage, Advantages & Disadvantages|ML|07:32
- #47 Instance Based Learning - With Example |ML|07:32
- #48 K- Nearest Neighbour Algorithm ( KNN ) - With Example |ML| #machinelearning #ml #jntu #btech10:06
- #49 Locally Weighted Regression - How to Find Weights & Drawbacks |ML|10:30
- #50 Radial Basis Functions with Example |ML|08:28
- #52 Remarks on Lazy and Eager Learning Algorithms |ML|04:22
- #51 Case Based Reasoning with Example |ML|08:24
- #53 Genetic Algorithm - Introduction |ML|05:45
- #54 An Illustrative Example Of Genetic Algorithms - Selection, Crossover, Mutation|ML|13:18
- #55 Genetic Programming with Example |ML|07:12
- #56 Models Of Evolution & Learning with Example - Lamarckian & Baldwin Effect |ML|07:21
- #57 Parallel Genetic Algorithm & its Types with Example |ML|06:54
- #58 Learning Set Of Rules & Sequential Covering Algorithm with Example |ML|08:54
- #59 First Order Learning Rules & the FOIL Algorithm |ML|10:41
- #60 Reinforcement Learning- Introduction, Markovs Decision Problem with Example |ML|07:29
- #61 Q-Learning : Q Table & Q Function, Steps Followed with Example |ML|13:04
- #63 Analytical Learning with Example |ML|05:36
- #62 Temporal Difference Learning in Machine Learning |ML|07:42
- #64 Learning With Perfect Domain Theory : PROLOG-EBG|ML|08:02
