Follow the ENGINEERING IN KARNATAKA ✪ channel on WhatsApp: https://whatsapp.com/channel/0029Vb27q0JKwqSbZewkPh1r 🚀 VTU Machine Learning Module 2 Explained in Simple & Easy Way! In this video of VTU Machine Learning Module 2, we cover important concepts like Covariance, Correlation, Gaussian Elimination Method, LU Decomposition, PCA, LDA, SVD, Find-S Algorithm, Candidate Elimination Algorithm, General-to-Specific Ordering, and Specific-to-General Ordering with easy explanations and examples. This video is useful for VTU CSE, ISE, AI & ML students preparing for exams, internals, viva, and placements. 📚 Perfect for: VTU Machine Learning Notes VTU ML Module 2 Machine Learning for Beginners Engineering Exam Preparation AI & ML Students 🔍 Searchable Keywords: VTU machine learning module 2 Machine learning module 2 VTU Covariance and correlation in machine learning Gaussian elimination method explained LU decomposition in machine learning PCA explained simply Principal Component Analysis VTU LDA machine learning explained Linear Discriminant Analysis tutorial SVD explained in simple words Singular Value Decomposition VTU Find S algorithm in machine learning Candidate elimination algorithm explained General to specific ordering Specific to general ordering Machine learning algorithms VTU VTU AIML notes ML module 2 explained Machine learning easy explanation Engineering machine learning tutorial ML for beginners VTU exam preparation machine learning ML important questions VTU Machine learning Kannada Machine learning English AI and ML concepts explained Supervised learning algorithms Machine learning full syllabus VTU ML module 2 important topics #MachineLearning #VTU #VTUML #MachineLearningVTU #Covariance #Correlation #GaussianElimination #LUDecomposition #PCA #LDA #SVD #FindSAlgorithm #CandidateElimination #ArtificialIntelligence #AIML #EngineeringStudents #VTUNotes #MLModule2 #MachineLearningAlgorithms #PrincipalComponentAnalysis #LinearDiscriminantAnalysis #SingularValueDecomposition #DataScience #AI #MLTutorial #VTUExam #Engineering #CSE #ISE #AIMLStudents #MachineLearningForBeginners #SupervisedLearning #VTUSyllabus #MLNotes #CollegeExams #Education #EDUYODHA
Comments 15
Sign in to join the conversation
Sign in
Maam naale exm but artha agalilla
I have understood all but I did not understand the step five how to take that final hypothesis
In g4 the last one will be < ? , ? , ? , G , ? , ? >
how can you consider positive and negitive based on job offer
Why didn't u consider job offer mam
Mam 5th step artaa aaglillaa🥺
And what if we get negative instance again
U just wrote specific hypothesis in step 5 what about general hypothesis
Ma'am what u didn't wite general hypothesis in last
In candidate elimination algorithm if 2 negative attributes are come back to back . what can do
in 10.54 y did u remove good
Pls make 1 shot numericals for all modules...Pls upload for all modules machine learning as we have bcm601 first paper is machine learning on Tuesday...pls pls pls upload all numericals plssss
Follow the ENGINEERING IN KARNATAKA ✪ channel on WhatsApp: /channel/0029Vb27q0JKwqSbZewkPh1r
Akka bega cover madi time ella full mod 5 tanka
Akka Big Data Analytics(BDA) plzzz😢😢😢