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Exercise: Decision Trees on Amazon reviews dataset

Instructor: Applied AI Course Duration: 3 mins

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Code Samples
Questions & Answers
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
4.1
Questions & Answers

30 min
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