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Data matrix notation

Instructor: Applied AI Course Duration: 7 mins

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How “Classification” works?
Classification vs Regression (examples)
1.1
Dataset overview: Amazon Fine Food reviews(EDA)
23 min
1.2
Data Cleaning: Deduplication
15 min
1.3
Why convert text to a vector?
14 min
1.4
Bag of Words (BoW)
18 min
1.5
Text Preprocessing: Stemming, Stop-word removal, Tokenization, Lemmatization.
15 min
1.6
uni-gram, bi-gram, n-grams.
9 min
1.7
tf-idf (term frequency- inverse document frequency)
22 min
1.8
Why use log in IDF?
14 min
1.9
Word2Vec.
16 min
1.10
Avg-Word2Vec, tf-idf weighted Word2Vec
9 min
1.11
Bag of Words( Code Sample)
19 min
1.12
Text Preprocessing( Code Sample)
11 min
1.13
Bi-Grams and n-grams (Code Sample)
5 min
1.14
TF-IDF (Code Sample)
6 min
1.15
Word2Vec (Code Sample)
12 min
1.16
Avg-Word2Vec and TFIDF-Word2Vec (Code Sample)
2 min
1.17
Exercise: t-SNE visualization of Amazon reviews with polarity based color-coding
6 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