20:52

Python Code Discussion - Classification

32:31

Python Code Discussion - Regression

01:52

Explaining IQmates Gig

02:41

What exactly is IQmates?

11:02

ChatGPT describing the Zulu Nation under Shaka (and creating interview questions)

29:28

Complete Example of Lagrange's Principle

13:41

Tutorial part2

29:27

Tutorial part1

13:21

Lecture12

13:14

Lecture11 part2

29:29

Comprehensive Example : Two Swinging Connected Rods

21:44

Lecture10 part2

29:27

Lecture10 part1

12:25

Lecture9 part2

29:29

Calculus of Variations

28:32

Examples : Moment of Intertia 3 (with Center of Mass)

11:06

Examples : Moment of Intertia 2

29:29

Examples : Moment of Inertia

12:50

Moment of Inertia Tensor 2

29:24

Moment of Inertia Tensor

29:28

Detailed Recap and Moment of Inertia

29:27

Example : Double Pendulum

29:27

Conservative Forces and Potentials 4

12:45

Conservative Forces and Potentials 3

29:29

Conservative Forces and Potentials 2

29:29

Conservative Forces and Potentials

08:06

Applications of Lagrange's Principle 3

29:27

Applications of Lagrange's Principle 2

29:28

Applications of Lagrange's Equation 1

10:19

d'Alembert's Principle 2

18:26

Deriving Lagrange's Equation 2

29:29

Deriving Lagrange's Equation 1

29:28

d'Alembert Principle 1

18:48

Equations of Constraint and Generalized Coordinates 3

22:33

Equations of Constraint and Generalized Coordinates 2

13:09

Equation of Motion of A Simple Pendulum

29:28

Transforming F=ma

29:29

Forces and Equations of Constraint

05:11

Singular Value Decomposition with Python Codes

05:43

Kernel PCA with Python Codes

02:01

Incremental PCA in Python

03:24

Sparse PCA in Python

16:02

Principal Component Analysis in Python

16:39

Loading the dataset

05:40

Introduction to Principal Component Analysis

38:29

Principal Component Analysis Example

04:02

Back to MNIST dataset for Principal Component Analysis

16:46

Principal Component Analysis

11:16

Scree Plots and How PCA Actually Reduces Dimension

07:07

Introduction to Dimension Reduction

24:33

Better Evaluation Metrics Precision & Recall

14:36

Using an ensemble to improve predictions

05:41

Model 4: LightGBM

04:46

Evaluating on the Test Set

04:22

Model 2: Random Forest

03:24

Model 3: XGBoost

06:41

Better Evaluation Metrics ROC Curve and auROC

10:09

Introduction to the project

12:29

Model 1: Logistic Regression

08:00

Data Acquisition and Exploration