31:13

Heap Sort-3-8-Data Structures and Algorithms-Unit-3-Searching and Sorting

18:07

Quick Sort-3-7-Data Structures and Algorithms-Unit-3-Searching and Sorting

19:35

Merge Sort-3-6-Data Structures and Algorithms-Unit-3-Searching and Sorting

12:59

Insertion Sort-3-5-Data Structures and Algorithms-Unit-3-Searching and Sorting-4CS1006

13:15

Selection Sort-3-4-Data Structures and Algorithms-Unit-3-Searching and Sorting-4CS1006

11:58

Bubble Sort-3-3-Data Structures and Algorithms-Unit-3-Searching and Sorting-4CS1006

17:58

Binary Search-3-2-Data Structures and Algorithms-Unit-3-Searching and Sorting

15:24

Sequential Search-3-1-Data Structures and Algorithms-Unit-3-Searching and Sorting

11:16

Priority Queue-2-6-Data Structures and Algorithms-Definition, operations, applications

15:26

Deque-Double-Ended-Queue-2-6-Data Structures and Algorithms-Definition, Applications, types

15:35

Circular Queue-2-5-Data Structures and Algorithms-Definition-Representation-Operations-Application

18:01

Queue Structure-2-4-Data Structures and Algorithms-Definition-representation-operations-applications

18:19

Stack Structure-Infix to Postfix Conversion-Data Structures and Algorithms-4CS1006-D Sumathi

10:37

Stack Applications-2-2-Data Structures and Algorithms-subject code-4CS1006

17:34

STACK Structure-2-1-Data Structures and Algorithms, Definition, Working Principles and Operations

23:38

Linear Array-1-3-Data Structures and Algorithms-definition, applications, operations, and etc.

21:36

Asymptotic Notations-1-2-Data Structures and Algorithms-

27:35

Data Structures and Algorithms-Introduction to Data Structures-1-1-Data Structures Taxonomies

14:08

The Apriori Algorithm-Machine Learning-5-1-7-Unsupervised Learning-Finding Pattern-Association Rule

18:54

Finding Pattern Using Association Rule-Machine Learning-5-1-6-Unsupervised Learning-CSE-JNTUA-R20-

15:27

Hierarchical Clustering-Machine Learning-5-1-5-Unsupervised Learning-UNIT V-JNTUA-CSE-R20

11:08

k-Mediod Clustering Algorithm-Machine Learning-5-1-4-Partitioning Methods-Unsupervised Learning

24:45

k-Means Clustering Algorithm-Machine Learning-5-1-3-Unsupervised Learning-Partitioning Methods

17:50

Clustering-Machine Learning-5-1-2-Unsupervised Learning -20A05602T-JNTUA-CSE-R20

17:38

Unsupervised learning Introduction-Machine Learning-5-1-1-20A05602T-JNTUA-R20-CSE

06:07

Maximum Likelihood Estimation-Machine Learning-4-1-6-Supervised Learning-CSE-JNTUA-R20-3 year

12:50

Logistic Regression-Machine Learning-4-1-5-Supervised Learning-20A05602T-CSE-JNTUA-R20-Unit-4

08:06

Polynomial Regression Model-Machine Learning–4-1-4-Supervised Learning-Example- JNTUA-R20-CSE

23:13

Multiple Linear Regression-Machine Learning-4-1-3-Supervised Learning-20A05602T-JNTUA-R20-CSE-Unit 4

11:53

Solved Problem in Simple Linear Regression-Machine Learning-Supervised Learning:Regression-20A05602T

13:09

Simple linear regression-Machine Learning-4-1-1-Supervised Learning-Regression-Slopes-JNTUA-CSE

31:13

Support Vector Machine-Machine Learning-3-2-6-Supervised Learning:Classification-20A05602T

10:42

Random Forest-Machine Learning-3-2-5-Supervised Learning:Classification-JNTUA-CSE-20A05602T

27:33

Decision Tree Learning-Solved Problem-Machine Learning-3-2-4-Supervised Learning:Classification

17:46

Decision Tree Learning-Machine Learning-3-2-3-Supervised Learning-Classification-20A05602T-CSE-JNTUA

17:04

kNN-k-Nearest Neighbour Algorithm-Machine Learning-3-2-2-Unit-3 SuSupervised Learning-Classification

18:34

Supervised learning-Introduction-3-2-1-Machine Learning-Classification-Unit-3-20A05602T-3 Year-CSE

09:46

Bayesian Belief network-complex scenarios-Applications-3-1-10-Machine Learning-JNTUA-CSE-3 Year-R20

18:23

Bayesian Belief network-Example-3-1-9-Machine Learning-20A05602T-JNTUA - CSE-R20

11:22

Bayesian Belief network-Intro.-3-1-8-Machine Learning-20A05602T-Unit-3-JNTUA-CSE-III Year

06:55

Handling Continuous Numeric Features-Naïve Bayes Classifier-3-1-7-Machine Learning-CSE-JNTUA-Unit-3

07:21

Applications of Naïve Bayes classifier-3-1-6-Machine Learning-Bayesian Concept Learning-CSE-JNTUA

22:55

Naïve Bayes classifier-Machine Learning-unit-3-1-5-Steps to solve-Example-football world cup match

08:18

Bayes optimal classifier-Machine Learning-20A05602T-Unit-3-Bayesian Concept Learning-JNTUA-CSE

15:39

Brute force Bayesian algorithm-Bayesian Concept Learning-Machine Learning-20A05602T-Unit-III-JNTUA

22:46

Bayesian Concept Learning-Bayes theorem-Machine Learning-20A05602T-Unit-3-CSE-JNTUA

12:48

Bayesian Concept Learning-3-1-1-Introduction-Machine Learning-20A05602T-Unit-III-JNTUA-III-year-CSE

11:39

Overall feature selection process-Machine Learning-FEATURE SUBSET SELECTION-Unit-2-CSE-R20-JNTUA

19:44

Measures of feature relevance and redundancy-Machine Learning-20A05602T-Unit-2-CSE-R20-JNTUA

11:00

FEATURE SUBSET SELECTION-Part-1Feature Selection-Machine Learning-20A05602T-CSE-R20-JNTUA

23:07

Feature Construction-Machine Learning-20A05602T-UNIT 2-FEATURE TRANSFORMATION-iii Year-CSE-R20-JNTUA

12:49

Singular value decomposition (SVD)-Linear Discriminant Analysis (LDA)-Machine Learning-20A05602T

33:13

Principal Component Analysis-PCA-Machine Learning-20A05602T-FEATURE EXTRACTION-Unit-2-CSE-III-Year

10:16

Feature Extraction-Machine Learning-20A05602T-Unit-2-Basics of Feature Engineering-CSE-III-Year-R20

14:11

Basics of Feature Engineering-Machine Learning-20A05602T-UNIT 2-CSE-III Year-R20-JNTUA

11:46

IMPROVING PERFORMANCE OF A MODEL-Machine Learning-20A05602T-Unit-2-CSE-R20-JNTUA

17:41

EVALUATING PERFORMANCE OF A MODEL Part 2-Machine Learning-20A05602T-Unit-2-CSE-R20-JNTUA

21:38

EVALUATING PERFORMANCE OF A MODEL-Machine Learning-20A05602T-UNIT 2-Supervised learning

14:29

MODEL REPRESENTATION AND INTERPRETABILITY-Machine Learning-20A05602T-unit-2-Modelling and Evaluation

22:18

Training a Model(for Supervised Learning)-Machine Learning-20A05602T-Unit-2-Modelling and Evaluation