Home Courses Applied Machine Learning Online Course Assignment-3: Apply k-Nearest Neighbor

Assignment-3: Apply k-Nearest Neighbor

Instructor: Applied AI Course Duration: 5 mins

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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
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
12 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
9 min
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
18 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
Revision Questions
30 min
3.1
Questions & Answers
30 min
4.1
Introduction
5 min
4.2
Imbalanced vs balanced dataset
23 min
4.3
Multi-class classification
12 min
4.4
k-NN, given a distance or similarity matrix
9 min
4.5
Train and test set differences
22 min
4.6
Impact of outliers
7 min
4.7
Local outlier Factor (Simple solution :Mean distance to Knn)
13 min
4.8
k distance
4 min
4.9
Reachability-Distance(A,B)
8 min
4.10
Local reachability-density(A)
9 min
4.11
Local outlier Factor(A)
21 min
4.12
Impact of Scale & Column standardization
13 min
4.13
Interpretability
12 min
4.14
Feature Importance and Forward Feature selection
22 min
4.15
Handling categorical and numerical features
24 min
4.16
Handling missing values by imputation
21 min
4.17
curse of dimensionality
27 min
4.18
Bias-Variance tradeoff
24 min
4.19
Intuitive understanding of bias-variance.
7 min
4.20
Revision Questions
30 min
4.21
best and worst case of algorithm
6 min
6.1
Questions & Answers
30 min
7.1
Conditional probability
13 min
7.2
Independent vs Mutually exclusive events
7 min
7.3
Bayes Theorem with examples
18 min
7.4
Exercise problems on Bayes Theorem
30 min
7.5
Naive Bayes algorithm
26 min
7.6
Toy example: Train and test stages
26 min
7.7
Naive Bayes on Text data
16 min
7.8
Laplace/Additive Smoothing
24 min
7.9
Log-probabilities for numerical stability
11 min
7.10
Bias and Variance tradeoff
14 min
7.11
Feature importance and interpretability
10 min
7.12
Imbalanced data
14 min
7.13
Outliers
6 min
7.14
Missing values
3 min
7.15
Handling Numerical features (Gaussian NB)
13 min
7.16
Multiclass classification
2 min
7.17
Similarity or Distance matrix
3 min
7.18
Large dimensionality
3 min
7.19
Best and worst cases
8 min
7.20
Code example
8 min
7.21
Revision Questions
8.1
Geometric intuition of Logistic Regression
31 min
8.2
Sigmoid function: Squashing
37 min
8.3
Mathematical formulation of Objective function
24 min
8.4
Weight vector
11 min
8.5
L2 Regularization: Overfitting and Underfitting
26 min
8.6
L1 regularization and sparsity
11 min
8.7
Probabilistic Interpretation: Gaussian Naive Bayes
20 min
8.8
Loss minimization interpretation
19 min
8.9
hyperparameter Search: Grid search and random search
16 min
8.10
Column Standardization
5 min
8.11
Feature importance and Model interpretability
14 min
8.12
Collinearity of features
14 min
8.13
Train & Run time space & time complexity
11 min
8.14
Real world cases
11 min
8.15
Non-linearly separable data & feature engineering
28 min
8.16
Code sample: Logistic regression, GridSearchCV, RandomSearchCV
23 min
8.17
Extensions to Logistic Regression: Generalized linear models(GLM)
8 min
9.1
Geometric intuition of Linear Regression
13 min
9.2
Mathematical formulation
14 min
9.3
Real world Cases
8 min
9.4
Code sample for Linear Regression
13 min
10.1
Differentiation
29 min
10.2
Online differentiation tools
8 min
10.3
Maxima and Minima
12 min
10.4
Vector calculus: Grad
10 min
10.5
Gradient descent: geometric intuition
19 min
10.6
Learning rate
8 min
10.7
Gradient descent for linear regression
8 min
10.8
SGD algorithm
9 min
10.9
Constrained Optimization & PCA
14 min
10.10
Logistic regression formulation revisited
6 min
10.11
Why L1 regularization creates sparsity?
17 min
10.12
Revision questions
30 min
11.1
Questions & Answers
30 min
12.1
Code Walkthrough: Text Encodings for ML/AI
12.2
Dive deep into K-NN
12.3
Performance metrics Deep Dive
12.4
Interactive LIVE session: Logistic regression deep dive
12.5
Code Walkthrough: Optimization Methods for ML/AI
12.6
Code Walkthrough: Hyper-param Optimisation
12.7
Logistic Regression with Imbalanced data: A Geometric View
12.8
Linear Regression using Probability & Stats