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Train and run time complexities

Instructor: Applied AI Course Duration: 8 mins

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