Home Courses Applied Machine Learning Online Course Mathematical derivation

Mathematical derivation

Instructor: Applied AI Course Duration: 32 mins

This content is restricted. Please Login

Prev

Next

Geometric Intution

Why we take values +1 and and -1 for Support vector planes

Support Vector Machines (SVM)

1.1

Geometric Intution

20 min

1.2

Mathematical derivation

32 min

1.3

Why we take values +1 and and -1 for Support vector planes

9 min

1.4

Loss function (Hinge Loss) based interpretation

18 min

1.5

Dual form of SVM formulation

16 min

1.6

10 min

1.7

Polynomial Kernel

11 min

1.8

22 min

1.9

Domain specific Kernels

6 min

1.10

Train and run time complexities

8 min

1.11

nu-SVM: control errors and support vectors

6 min

1.12

8 min

1.13

9 min

1.14

14 min

1.15

Revision Questions

30 min

Interview Questions on Support Vector Machine

2.1

Questions & Answers

30 min

Decision Trees

3.1

Geometric Intuition of decision tree: Axis parallel hyperplanes

17 min

3.2

Sample Decision tree

8 min

3.3

Building a decision Tree:Entropy

19 min

3.4

14 min

3.5

Building a decision Tree:Information Gain

10 min

3.6

Building a decision Tree: Gini Impurity

7 min

3.7

Building a decision Tree: Constructing a DT

21 min

3.8

Building a decision Tree: Splitting numerical features

8 min

3.9

Feature standardization

4 min

3.10

Building a decision Tree:Categorical features with many possible values

7 min

3.11

Overfitting and Underfitting

8 min

3.12

Train and Run time complexity

7 min

3.13

Regression using Decision Trees

9 min

3.14

12 min

3.15

9 min

3.16

Revision Questions

30 min

Interview Questions on decision Trees

4.1

Questions & Answers

30 min

Ensemble Models

5.1

What are ensembles?

6 min

5.2

Bootstrapped Aggregation (Bagging) Intuition

17 min

5.3

Random Forest and their construction

15 min

5.4

Bias-Variance tradeoff

7 min

5.5

Bagging Train and run time complexity

9 min

5.6

Bagging:Code Sample

4 min

5.7

Extremely randomized trees

8 min

5.8

Random Forest :Cases

6 min

5.9

Boosting Intuition

17 min

5.10

Residuals, Loss functions and gradients

13 min

5.11

Gradient Boosting

10 min

5.12

Regularization by Shrinkage

8 min

5.13

Train and Run time complexity

6 min

5.14

XGBoost: Boosting + Randomization

14 min

5.15

AdaBoost: geometric intuition

7 min

5.16

Stacking models

22 min

5.17

Cascading classifiers

15 min

5.18

Kaggle competitions vs Real world

9 min

5.19

Revision Questions

30 min

Module 4: Live Sessions

6.1

Code Walkthrough: Models in Scikit-Learn

6.2

Interview Questions on SVMs

6.3

Interview Questions on Boosting and Bagging

6.4

Attribution Models in Marketing

6.5

Attribution Models in Marketing-Part 2

6.6

LIVE_ Monte Carlo Simulations

6.7

Which ML/DL technique to use where?