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Instructor: Applied AI Course Duration: 12 mins

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Regression using Decision Trees

Code Samples

Support Vector Machines (SVM)

1.1

Geometric Intution

20 min

1.2

Mathematical derivation

32 min

1.3

Loss function (Hinge Loss) based interpretation

18 min

1.4

10 min

1.5

nu-SVM: control errors and support vectors

6 min

1.6

9 min

1.7

Dual form of SVM formulation

16 min

1.8

21 min

1.9

Polynomial kernel

11 min

1.10

Domain specific Kernels

6 min

1.11

Train and run time complexities

8 min

1.12

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

9 min

1.13

8 min

1.14

14 min

1.15

Exercise: Apply SVM to Amazon reviews dataset

4 min

Interview Questions on Support Vector Machine

2.1

Questions & Answers

30 min

Decision Trees

3.1

Sample Decision tree

8 min

3.2

Building a decision Tree:Entropy

19 min

3.3

Building a decision Tree:Information Gain

10 min

3.4

Building a decision Tree: Gini Impurity

7 min

3.5

Geometric Intuition of decision tree: Axis parallel hyperplanes

17 min

3.6

Building a decision Tree: Constructing a DT

21 min

3.7

Building a decision Tree: Splitting numerical features

8 min

3.8

Feature standardization

4 min

3.9

Building a decision Tree:Categorical features with many possible values

7 min

3.10

Overfitting and Underfitting

8 min

3.11

Train and Run time complexity

7 min

3.12

Regression using Decision Trees

9 min

3.13

12 min

3.14

9 min

3.15

Exercise: Decision Trees on Amazon reviews dataset

3 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

Train and run time complexity

9 min

5.6

Bagging:Code Sample

4 min

5.7

Extremely randomized trees

8 min

5.8

Random Tree :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

Exercise: Apply GBDT and RF to Amazon reviews dataset.

4 min