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Regularization by Shrinkage

Instructor: Applied AI Course Duration: 8 mins

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Gradient Boosting
Train and Run time complexity
1.1
Geometric Intution
20 min
1.2
Mathematical derivation
32 min
1.3
Loss function (Hinge Loss) based interpretation
18 min
1.4
kernel trick
10 min
1.5
nu-SVM: control errors and support vectors
6 min
1.6
Cases
9 min
1.7
Dual form of SVM formulation
16 min
1.8
RBF-Kernel
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
SVM Regression
8 min
1.14
Code Samples
14 min
1.15
Exercise: Apply SVM to Amazon reviews dataset
4 min
2.1
Questions & Answers

30 min
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
Cases
12 min
3.14
Code Samples
9 min
3.15
Exercise: Decision Trees on Amazon reviews dataset
3 min
4.1
Questions & Answers

30 min