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Code Sample:Cross Validation.

Instructor: Applied AI Course Duration: 13 mins

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Code Sample:Decision boundary .
Exercise: Apply k-NN on Amazon reviews dataset
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
Introduction
5 min
3.2
Imbalanced vs balanced dataset
23 min
3.3
Multi-class classification
12 min
3.4
k-NN, given a distance or similarity matrix
9 min
3.5
Train and test set differences
22 min
3.6
Impact of outliers
7 min
3.7
Local outlier Factor (Simple solution :Mean distance to Knn)
13 min
3.8
K-Distance(A),N(A)
4 min
3.9
Reachability-Distance(A,B)
8 min
3.10
Local reachability-density(A)
9 min
3.11
Local outlier Factor(A)
21 min
3.12
Impact of Scale & Column standardization
12 min
3.13
Interpretability
12 min
3.14
Feature Importance and Forward Feature selection
22 min
3.15
Handling categorical and numerical features
24 min
3.16
Handling missing values by imputation
21 min
3.17
Curse of dimensionality
27 min
3.18
Bias-Variance tradeoff
24 min
3.19
Best and worst cases for an algorithm
6 min
4.1
Accuracy
15 min
4.2
Confusion matrix, TPR, FPR, FNR, TNR
25 min
4.3
Precision and recall, F1-score
10 min
4.4
Receiver Operating Characteristic Curve (ROC) curve and AUC
19 min
4.5
Log-loss
12 min
4.6
R-Squared/Coefficient of determination
14 min
4.7
Median absolute deviation (MAD)
5 min
4.8
Distribution of errors
7 min
5.1
Conditional probability
13 min
5.2
Independent vs Mutually exclusive events
6 min
5.3
Bayes Theorem with examples
18 min
5.4
Exercise problems on Bayes Theorem
30 min
5.5
Naive Bayes algorithm
26 min
5.6
Toy example: Train and test stages
26 min
5.7
Naive Bayes on Text data
16 min
5.8
Laplace/Additive Smoothing
24 min
5.9
Log-probabilities for numerical stability
11 min
5.10
Bias and Variance tradeoff
14 min
5.11
Feature importance and interpretability
10 min
5.12
Imbalanced data
14 min
5.13
Outliers
6 min
5.14
Missing values
3 min
5.15
Handling Numerical features (Gaussian NB)
13 min
5.16
Multiclass classification
2 min
5.17
Similarity or Distance matrix
3 min
5.18
Large dimensionality
2 min
5.19
Best and worst cases
8 min
5.20
Code example
7 min
5.21
Exercise: Apply Naive Bayes to Amazon reviews
6 min
6.1
Geometric intuition
31 min
6.2
Sigmoid function: Squashing
37 min
6.3
Mathematical formulation of Objective function
24 min
6.4
Weight vector
11 min
6.5
Regularization: Overfitting and Underfitting
26 min
6.6
L1 regularization and sparsity
11 min
6.7
Probabilistic Interpretation: Gaussian Naive Bayes
19 min
6.8
Loss minimization interpretation
24 min
6.9
Feature importance and interpretability
14 min
6.10
Collinearity of features
14 min
6.11
Test/Run time space and time complexity
10 min
6.12
Real World cases
11 min
6.13
Non-linearly separable data & feature engineering
28 min
6.14
Code sample: Logistic regression, GridSearchCV, RandomSearchCV
23 min
6.15
Exercise: Apply Logistic regression to Amazon reviews dataset
6 min
6.16
Extensions to Logistic Regression: Generalized linear models
9 min
6.17
Hyperparameter search: Grid Search and Random Search
16 min
6.18
Column Standardization
5 min
7.1
Geometric intuition
13 min
7.2
Mathematical formulation
14 min
7.3
Real world Cases
9 min
7.4
Code sample for Linear Regression
13 min
8.1
Differentiation
29 min
8.2
Online differentiation tools
8 min
8.3
Maxima and Minima
12 min
8.4
Vector calculus: Grad
10 min
8.5
Gradient descent: geometric intuition
19 min
8.6
Learning rate
8 min
8.7
Gradient descent for linear regression
8 min
8.8
SGD algorithm
9 min
8.9
Constrained Optimization & PCA
14 min
8.10
Logistic regression formulation revisited
6 min
8.11
Why L1 regularization creates sparsity?
17 min
8.12
Exercise: Implement SGD for linear regression
6 min