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TF-IDF (Code Sample)

Instructor: Applied AI Course Duration: 6 mins

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Bi-Grams and n-grams (Code Sample)
Word2Vec (Code Sample)
2.1
How “Classification” works?
10 min
2.2
Data matrix notation
7 min
2.3
Classification vs Regression (examples)
6 min
2.4
K-Nearest Neighbours Geometric intuition with a toy example
11 min
2.5
Failure cases of KNN
7 min
2.6
Distance measures: Euclidean(L2) , Manhattan(L1), Minkowski, Hamming
20 min
2.7
Cosine Distance & Cosine Similarity
19 min
2.8
How to measure the effectiveness of k-NN?
16 min
2.9
Test/Evaluation time and space complexity
12 min
2.10
KNN Limitations
2.11
Decision surface for K-NN as K changes
23 min
2.12
Overfitting and Underfitting
12 min
2.13
Need for Cross validation
22 min
2.14
K-fold cross validation
17 min
2.15
Visualizing train, validation and test datasets
13 min
2.16
How to determine overfitting and underfitting?
19 min
2.17
Time based splitting
19 min
2.18
k-NN for regression
5 min
2.19
Weighted k-NN
8 min
2.20
Voronoi diagram
4 min
2.21
Binary search tree
16 min
2.22
How to build a kd-tree
17 min
2.23
Find nearest neighbours using kd-tree
13 min
2.24
Limitations of Kd tree
9 min
2.25
Extensions
3 min
2.26
Hashing vs LSH
10 min
2.27
LSH for cosine similarity
40 min
2.28
LSH for euclidean distance
13 min
2.29
Probabilistic class label
8 min
2.30
Code Sample:Decision boundary .
23 min
2.31
Code Sample:Cross Validation
13 min
2.32
Exercise: Apply k-NN on Amazon reviews dataset
5 min
2.33
Revision Questions
30 min
3.1
Accuracy
15 min
3.2
Confusion matrix, TPR, FPR, FNR, TNR
25 min
3.3
Distribution of errors
7 min
3.4
Receiver Operating Characteristic Curve (ROC) curve and AUC
19 min
3.5
Log-loss
12 min
3.6
R-Squared/Coefficient of determination
14 min
3.7
Median absolute deviation (MAD)
5 min
3.8
Revision Questions
30 min
3.9
Precision and recall, F1-score
10 min
4.1
Geometric intuition of Logistic Regression
31 min
4.2
Sigmoid function: Squashing
37 min
4.3
Mathematical formulation of Objective function
24 min
4.4
Weight vector
11 min
4.5
L2 Regularization: Overfitting and Underfitting
26 min
4.6
L1 regularization and sparsity
11 min
4.7
Probabilistic Interpretation: Gaussian Naive Bayes
19 min
4.8
Loss minimization interpretation
24 min
4.9
hyperparameters and random search
16 min
4.10
Column Standardization
5 min
4.11
Feature importance and Model interpretability
14 min
4.12
Collinearity of features
14 min
4.13
Test/Run time space and time complexity
10 min
4.14
Real world cases
11 min
4.15
Non-linearly separable data & feature engineering
28 min
4.16
Code sample: Logistic regression, GridSearchCV, RandomSearchCV
23 min
4.17
Exercise: Apply Logistic regression to Amazon reviews dataset.
6 min
4.18
Extensions to Generalized linear models
9 min
5.1
Geometric intuition of Linear Regression
13 min
5.2
Mathematical formulation
14 min
5.3
Real world Cases
8 min
5.4
Code sample for Linear Regression
13 min
6.1
Differentiation
29 min
6.2
Revision Questions
30 min
6.3
Online differentiation tools
8 min
6.4
Maxima and Minima
12 min
6.5
Vector calculus: Grad
10 min
6.6
Gradient descent: geometric intuition
19 min
6.7
Learning rate
8 min
6.8
Gradient descent for linear regression
8 min
6.9
SGD algorithm
9 min
6.10
Constrained Optimization & PCA
14 min
6.11
Logistic regression formulation revisited
6 min
6.12
Why L1 regularization creates sparsity?
17 min
6.13
Exercise: Implement SGD for linear regression
6 min
6.14
Revision questions
30 min