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AdaBoost: geometric intuition

Instructor: Applied AI Course Duration: 7 mins

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XGBoost: Boosting + Randomization
Stacking models
1.1
Geometric Intution
20 min
1.2
Mathematical derivation
17 min
1.3
Loss function (Hinge Loss) based interpretation
18 min
1.4
Dual form of SVM formulation
16 min
1.5
Kernel trick
10 min
1.6
Polynomial kernel
11 min
1.7
RBF-Kernel
21 min
1.8
Domain specific Kernels
6 min
1.9
Train and run time complexities
8 min
1.10
nu-SVM: control errors and support vectors
6 min
1.11
SVM Regression
8 min
1.12
Cases
9 min
1.13
Code Sample
14 min
1.14
Exercise: Apply SVM to Amazon reviews dataset
4 min
2.1
Geometric Intuition: Axis parallel hyperplanes
17 min
2.2
Sample Decision tree
8 min
2.3
Building a decision Tree:Entropy
19 min
2.4
Building a decision Tree:Information Gain
10 min
2.5
Building a decision Tree: Gini Impurity
7 min
2.6
Building a decision Tree: Constructing a DT
21 min
2.7
Building a decision Tree: Splitting numerical features
8 min
2.8
Feature standardization
4 min
2.9
Building a decision Tree:Categorical features with many possible values
7 min
2.10
Overfitting and Underfitting
8 min
2.11
Train and Run time complexity
7 min
2.12
Regression using Decision Trees
9 min
2.13
Cases
12 min
2.14
Code Samples
9 min
2.15
Exercise: Decision Trees on Amazon reviews dataset
3 min