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Building a decision Tree:Entropy

Instructor: Applied AI Course Duration: 19 mins

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Sample Decision tree
Building a decision Tree:Information Gain
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
3.1
What are ensembles?
6 min
3.2
Bootstrapped Aggregation (Bagging) Intuition
17 min
3.3
Random Forest and their construction
15 min
3.4
Bias-Variance tradeoff
7 min
3.5
Bagging :Train and Run-time Complexity.
9 min
3.6
Bagging:Code Sample
4 min
3.7
Extremely randomized trees
8 min
3.8
Random Tree :Cases
6 min
3.9
Boosting Intuition
17 min
3.10
Residuals, Loss functions and gradients
13 min
3.11
Gradient Boosting
10 min
3.12
Regularization by Shrinkage
8 min
3.13
Train and Run time complexity
6 min
3.14
XGBoost: Boosting + Randomization
14 min
3.15
AdaBoost: geometric intuition
7 min
3.16
Stacking models
22 min
3.17
Cascading classifiers
15 min
3.18
Kaggle competitions vs Real world
9 min
3.19
Assignment: Apply Random Forests & GBDT ?
4 min