16:24

Curve Fitting and Regression Analysis | Newton's Forward Difference Interpolation | Unit- 04

16:25

Curve Fitting and Regression Analysis | Lagrange's Interpolation | Unit- 04

17:55

Curve Fitting and Regression Analysis | Fitting of Exponential Equation | Unit- 04

11:41

Curve Fitting and Regression Analysis | Fitting of Power Equation | Unit- 04

15:56

Curve Fitting and Regression Analysis | Fitting of Power Equation | Unit- 04

16:00

Curve Fitting and Regression Analysis | Fitting of Power Equation | Unit- 04

22:00

Curve Fitting and Regression Analysis | Fitting of Power Equation | Unit- 04

18:00

Curve Fitting and Regression Analysis | Fitting of Quadratic Equation | Unit- 04

18:57

Curve Fitting and Regression Analysis | Fitting of Quadratic Equation | Unit- 04

15:06

Curve Fitting and Regression Analysis | Fitting of Straight Line | Unit- 04

22:50

Curve Fitting and Regression Analysis | Fitting of Straight Line | Unit- 04

15:59

Numerical Integration | Simpson's (1/3)rd Rule | Unit- 03

11:44

Numerical Integration | Simpson's (1/3)rd Rule | Unit- 03

26:36

Numerical Integration | Simpson's (1/3)rd Rule | Double Integration | Unit- 03

09:48

Numerical Integration | Trapezoidal Rule | Double Integration | Unit- 03

35:17

Numerical Integration | Trapezoidal Rule | Double Integration | Unit- 03

13:35

Numerical Integration | Gauss Quadrature 3-Point Method | Unit- 03

13:15

Numerical Integration | Gauss Quadrature 2-Point Method | Unit- 03

20:13

Numerical Integration | Simpson's (3/8)th Rule | Unit- 03

15:29

Numerical Integration | Simpson's (1/3)rd Rule | Unit- 03

18:28

Numerical Integration | Trapezoidal Rule | Unit- 03

31:08

Partial Differential Equation (PDE) | ELLIPTIC Explicit Method Poisson's Equations| Unit- 02

19:35

Partial Differential Equation (PDE) | ELLIPTIC Explicit Method Laplace Equations| Unit- 02

30:58

Partial Differential Equation (PDE) | Parabolic Explicit Method Parabolic Equations| Unit- 02

19:55

Ordinary Differential Equation (ODE) | Runge Kutta 4th Order| Unit- 02

19:11

Ordinary Differential Equation (ODE) | Runge Kutta 2nd Order| Unit- 02

22:51

Ordinary Differential Equation (ODE) | Eulers Method | Unit- 02

19:49

Simultaneous Equation | Thomas Algorithm | Unit- 01

25:42

Simultaneous Equation | Gauss Seidel Method (Accuracy) | Unit- 01

17:52

Simultaneous Equation | Gauss Seidel Method (Iteration) | Unit- 01

27:35

Simultaneous Equation | Gauss Elimination Method (Pivoting)| Unit- 01

20:50

Roots of Equation | Newton-Raphson Method (Accuracy Criteria) | Unit- 01

24:24

Roots of Equation | Newton-Raphson Method (Iteration Criteria) | Unit- 01

31:14

Roots of Equation | Bi-section Method (Accuracy Criteria) | Unit- 01

23:12

Roots of Equation | Bi-section Method (Iteration Criteria) | Unit- 01

08:30

Unit- V Lecture 66 - Numerical on Neural Network

10:47

Unit- V Lecture 65 - Numerical on Neural Network

16:45

Unit- V Lecture 64 - How Neural Network Works

19:18

Unit- V Lecture 63 - Artificial Neural Network (ANN)

08:42

Unit- V Lecture 62 - Difference between DL & ML.

16:28

Unit- V Lecture 61 - Deep Learning

18:55

Unit- V Lecture 60 - Q- Learning

15:20

Unit- V Lecture 59 - Markov Decision Process

14:48

Unit- V Lecture 58 - Markov Property.

12:03

Unit- V Lecture 57 - Positive & Negative Reinforced Learning

09:43

Unit- V Lecture 56 - Approaches to Reinforced Learning

20:07

Unit- V Lecture 55 - Characteristics of Reinforced Learning

07:16

Unit- V Lecture 54 - Introduction to Reinforced Learning.

20:24

Unit- IV Lecture 53 - Hyperparameter Tunning

18:27

Unit- IV Lecture 52 - Confusion Matrix | Numerical.

14:47

Unit- IV Lecture 51 - Confusion Matrix.

18:06

Unit- IV Lecture 50 - Model Evaluation Technique in Machine Learning.

14:12

Unit- IV Lecture 49 - K- Fold Cross Validation.

18:19

Unit- IV Lecture 48 - Model Training, Testing and Validation.

09:51

Unit- IV Lecture 47 - Model Selection in Machine Learning.

13:19

Unit- IV Lecture 46 - Data Preprocessing In Machine Learning.

15:05

Unit- IV Lecture 45 - Data Collection In Machine Learning.

17:01

Unit- IV Lecture 44- Steps in ML Model Building.

21:52

Unit- IV Lecture 43- Problem Identification in ML

12:35

Unit-III Lecture 42- K Nearest Neighbor Algorithm | KNN