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Machine Learning

Category: Gate DA

Machine Learning

Category: Gate DA

₹10,000.00 ₹12,000.00

Instructor

Chekuri Srikanth Varma

Dimensionality Reduction and PCA
Chapters : 2 Assignments : 0 Completed : 0%

Dimensionality reduction and Visualization:

1.1

Introduction to IRIS dataset and 2D scatter plot

1 mins

1.2

What is Dimensionality reduction?

3 mins

1.3

Row Vector and Column Vector

5 mins

1.4

How to represent a data set?

4 mins

1.5

How to represent a dataset as a Matrix.

7 mins

1.6

Data Preprocessing: Feature Normalisation

20 mins

1.7

Mean of a data matrix

6 mins

1.8

Data Preprocessing: Column Standardization

16 mins

1.9

Co-variance of a Data Matrix

24 mins

1.10

MNIST dataset (784 dimensional)

20 mins

1.11

Code to Load MNIST Data Set

12 mins

PCA(principal component analysis)

2.1

Why learn PCA?

4 mins

2.2

Geometric intuition of PCA

14 mins

2.3

Mathematical objective function of PCA

13 mins

2.4

Alternative formulation of PCA: Distance minimization

10 mins

2.5

Eigen values and Eigen vectors (PCA): Dimensionality reduction

23 mins

2.6

PCA for Dimensionality Reduction and Visualization

10 mins

2.7

Visualize MNIST dataset

5 mins

2.8

Limitations of PCA

5 mins

2.9

PCA Code example

19 mins

2.10

PCA for dimensionality reduction (not-visualization)

15 mins

Foundations of Machine Learning
Chapters : 11 Assignments : 0 Completed : 0%

Real world problem: Predict rating given product reviews on Amazon

3.1

Dataset overview: Amazon Fine Food reviews(EDA)

23 mins

3.2

Data Cleaning: Deduplication

15 mins

3.3

Why convert text to a vector?

14 mins

3.4

Bag of Words (BoW)

18 mins

3.5

Text Preprocessing: Stemming, Stop-word removal, Tokenization, Lemmatization.

15 mins

3.6

uni-gram, bi-gram, n-grams.

9 mins

3.7

tf-idf (term frequency- inverse document frequency)

22 mins

3.8

Why use log in IDF?

14 mins

3.9

Word2Vec.

16 mins

3.10

Avg-Word2Vec, tf-idf weighted Word2Vec

9 mins

3.11

Bag of Words( Code Sample)

19 mins

3.12

Text Preprocessing( Code Sample)

11 mins

3.13

Bi-Grams and n-grams (Code Sample)

5 mins

3.14

TF-IDF (Code Sample)

6 mins

3.15

Word2Vec (Code Sample)

12 mins

3.16

Avg-Word2Vec and TFIDF-Word2Vec (Code Sample)

2 mins

Classification And Regression Models: K-Nearest Neighbors

4.1

How “Classification” works?

10 mins

4.2

Data matrix notation

7 mins

4.3

Classification vs Regression (examples)

6 mins

4.4

K-Nearest Neighbours Geometric intuition with a toy example

12 mins

4.5

Failure cases of KNN

7 mins

4.6

Distance measures: Euclidean(L2) , Manhattan(L1), Minkowski, Hamming

20 mins

4.7

Cosine Distance & Cosine Similarity

19 mins

4.8

How to measure the effectiveness of k-NN?

16 mins

4.9

Test/Evaluation time and space complexity

12 mins

4.10

KNN Limitations

9 mins

4.11

Decision surface for K-NN as K changes

23 mins

4.12

Overfitting and Underfitting

12 mins

4.13

Need for Cross validation

22 mins

4.14

K-fold cross validation

18 mins

4.15

Visualizing train, validation and test datasets

13 mins

4.16

How to determine overfitting and underfitting?

19 mins

4.17

Time based splitting

19 mins

4.18

k-NN for regression

5 mins

4.19

Weighted k-NN

8 mins

4.20

Voronoi diagram

4 mins

4.21

Binary search tree

16 mins

4.22

How to build a kd-tree

17 mins

4.23

Find nearest neighbours using kd-tree

13 mins

4.24

Limitations of Kd tree

9 mins

4.25

Extensions

3 mins

4.26

Hashing vs LSH

10 mins

4.27

LSH for cosine similarity

40 mins

4.28

LSH for euclidean distance

13 mins

4.29

Probabilistic class label

8 mins

4.30

Code Sample:Decision boundary .

23 mins

4.31

Code Sample:Cross Validation

13 mins

4.32

Revision Questions

30 mins

Interview Questions on K-NN(K Nearest Neighbour)

5.1

Questions & Answers

30 mins

Classification algorithms in various situations

6.1

Introduction

5 mins

6.2

Imbalanced vs balanced dataset

23 mins

6.3

Multi-class classification

12 mins

6.4

k-NN, given a distance or similarity matrix

9 mins

6.5

Train and test set differences

22 mins

6.6

Impact of outliers

7 mins

6.7

Local outlier Factor (Simple solution :Mean distance to Knn)

13 mins

6.8

k distance

4 mins

6.9

Reachability-Distance(A,B)

8 mins

6.10

Local reachability-density(A)

9 mins

6.11

Local outlier Factor(A)

21 mins

6.12

Impact of Scale & Column standardization

13 mins

6.13

Interpretability

12 mins

6.14

Feature Importance and Forward Feature selection

22 mins

6.15

Handling categorical and numerical features

24 mins

6.16

Handling missing values by imputation

21 mins

6.17

curse of dimensionality

27 mins

6.18

Bias-Variance tradeoff

24 mins

6.19

Intuitive understanding of bias-variance.

7 mins

6.20

Revision Questions

30 mins

6.21

best and worst case of algorithm

6 mins

Performance measurement of models

7.1

Accuracy

15 mins

7.2

Confusion matrix, TPR, FPR, FNR, TNR

25 mins

7.3

Precision and recall, F1-score

10 mins

7.4

Receiver Operating Characteristic Curve (ROC) curve and AUC

19 mins

7.5

Log-loss

12 mins

7.6

R-Squared/Coefficient of determination

14 mins

7.7

Median absolute deviation (MAD)

5 mins

7.8

Distribution of errors

7 mins

7.9

Revision Questions

30 mins

Interview Questions on Performance Measurement Models

8.1

Questions & Answers

30 mins

Naive Bayes

9.1

Conditional probability

13 mins

9.2

Independent vs Mutually exclusive events

7 mins

9.3

Bayes Theorem with examples

18 mins

9.4

Exercise problems on Bayes Theorem

30 mins

9.5

Naive Bayes algorithm

26 mins

9.6

Toy example: Train and test stages

26 mins

9.7

Naive Bayes on Text data

16 mins

9.8

Laplace/Additive Smoothing

24 mins

9.9

Log-probabilities for numerical stability

11 mins

9.10

Bias and Variance tradeoff

14 mins

9.11

Feature importance and interpretability

10 mins

9.12

Imbalanced data

14 mins

9.13

Outliers

6 mins

9.14

Missing values

3 mins

9.15

Handling Numerical features (Gaussian NB)

13 mins

9.16

Multiclass classification

2 mins

9.17

Similarity or Distance matrix

3 mins

9.18

Large dimensionality

3 mins

9.19

Best and worst cases

8 mins

9.20

Code example

8 mins

9.21

Revision Questions

0 mins

Logistic Regression

10.1

Geometric intuition of Logistic Regression

31 mins

10.2

Sigmoid function: Squashing

37 mins

10.3

Mathematical formulation of Objective function

24 mins

10.4

Weight vector

11 mins

10.5

L2 Regularization: Overfitting and Underfitting

26 mins

10.6

L1 regularization and sparsity

11 mins

10.7

Probabilistic Interpretation: Gaussian Naive Bayes

20 mins

10.8

Loss minimization interpretation

19 mins

10.9

hyperparameter Search: Grid search and random search

16 mins

10.10

Column Standardization

5 mins

10.11

Feature importance and Model interpretability

14 mins

10.12

Collinearity of features

14 mins

10.13

Train & Run time space & time complexity

11 mins

10.14

Real world cases

11 mins

10.15

Non-linearly separable data & feature engineering

28 mins

10.16

Code sample: Logistic regression, GridSearchCV, RandomSearchCV

23 mins

10.17

Extensions to Logistic Regression: Generalized linear models(GLM)

8 mins

Linear Regression

11.1

Geometric intuition of Linear Regression

13 mins

11.2

Mathematical formulation

14 mins

11.3

Real world Cases

8 mins

11.4

Code sample for Linear Regression

13 mins

Solving Optimization Problems

12.1

Differentiation

29 mins

12.2

Online differentiation tools

8 mins

12.3

Maxima and Minima

12 mins

12.4

Vector calculus: Grad

10 mins

12.5

Gradient descent: geometric intuition

19 mins

12.6

Learning rate

8 mins

12.7

Gradient descent for linear regression

8 mins

12.8

SGD algorithm

9 mins

12.9

Constrained Optimization & PCA

14 mins

12.10

Logistic regression formulation revisited

6 mins

12.11

Why L1 regularization creates sparsity?

17 mins

12.12

Revision questions

30 mins

Interview Questions on Logistic Regression and Linear Regression

13.1

Questions & Answers

30 mins

Machine Learning - II
Chapters : 4 Assignments : 0 Completed : 0%

Support Vector Machines (SVM)

14.1

Geometric Intution

20 mins

14.2

Mathematical derivation

32 mins

14.3

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

9 mins

14.4

Loss function (Hinge Loss) based interpretation

18 mins

14.5

Dual form of SVM formulation

16 mins

14.6

kernel trick

10 mins

14.7

Polynomial Kernel

11 mins

14.8

RBF-Kernel

22 mins

14.9

Domain specific Kernels

6 mins

14.10

Train and run time complexities

8 mins

14.11

nu-SVM: control errors and support vectors

6 mins

14.12

SVM Regression

8 mins

14.13

Cases

9 mins

14.14

Code Sample

14 mins

14.15

Revision Questions

30 mins

Interview Questions on Support Vector Machine

15.1

Questions & Answers

30 mins

Decision Trees

16.1

Geometric Intuition of decision tree: Axis parallel hyperplanes

17 mins

16.2

Sample Decision tree

8 mins

16.3

Building a decision Tree:Entropy

19 mins

16.4

KL Divergence

14 mins

16.5

Building a decision Tree:Information Gain

10 mins

16.6

Building a decision Tree: Gini Impurity

7 mins

16.7

Building a decision Tree: Constructing a DT

21 mins

16.8

Building a decision Tree: Splitting numerical features

8 mins

16.9

Feature standardization

4 mins

16.10

Building a decision Tree:Categorical features with many possible values

7 mins

16.11

Overfitting and Underfitting

8 mins

16.12

Train and Run time complexity

7 mins

16.13

Regression using Decision Trees

9 mins

16.14

Cases

12 mins

16.15

Code Samples

9 mins

16.16

Revision Questions

30 mins

Interview Questions on decision Trees

17.1

Questions & Answers

30 mins

Unsupervised Learning
Chapters : 3 Assignments : 0 Completed : 0%

Unsupervised learning/Clustering

18.1

What is Clustering?

10 mins

18.2

Unsupervised learning

4 mins

18.3

Applications

16 mins

18.4

Metrics for Clustering

19 mins

18.5

K-Means: Geometric intuition, Centroids

8 mins

18.6

K-Means: Mathematical formulation: Objective function

11 mins

18.7

K-Means Algorithm.

11 mins

18.8

How to initialize: K-Means++

25 mins

18.9

Failure cases/Limitations

11 mins

18.10

K-Medoids

19 mins

18.11

Determining the right K

5 mins

18.12

Code Samples

7 mins

18.13

Time and space complexity

4 mins

Hierarchical clustering Technique

19.1

Agglomerative & Divisive, Dendrograms

14 mins

19.2

Agglomerative Clustering

9 mins

19.3

Proximity methods: Advantages and Limitations.

24 mins

19.4

Time and Space Complexity

4 mins

19.5

Limitations of Hierarchical Clustering

5 mins

19.6

Code sample

3 mins

Singular Value Decomposition

20.1

Singular Value Decomposition

23 mins

Neural Networks
Chapters : 2 Assignments : 0 Completed : 0%

Deep Learning:Neural Networks.

21.1

History of Neural networks and Deep Learning.

25 mins

21.2

How Biological Neurons work?

8 mins

21.3

Growth of biological neural networks

17 mins

21.4

Diagrammatic representation: Logistic Regression and Perceptron

17 mins

21.5

Multi-Layered Perceptron (MLP).

23 mins

21.6

Notation

18 mins

21.7

Training a single-neuron model.

28 mins

21.8

Training an MLP: Chain Rule

40 mins

21.9

Training an MLP:Memoization

14 mins

21.10

Backpropagation.

26 mins

21.11

Activation functions

17 mins

21.12

Vanishing Gradient problem.

23 mins

21.13

Bias-Variance tradeoff.

10 mins

21.14

Decision surfaces: Playground

15 mins

21.15

Interview Questions

30 mins

Deep Learning: Deep Multi-layer perceptrons

22.1

Deep Multi-layer perceptrons:1980s to 2010s

16 mins

22.2

Dropout layers & Regularization.

21 mins

22.3

Rectified Linear Units (ReLU).

28 mins

22.4

Weight initialization.

24 mins

22.5

Batch Normalization.

21 mins

22.6

Optimizers:Hill-descent analogy in 2D

19 mins

22.7

Optimizers:Hill descent in 3D and contours.

13 mins

22.8

SGD Recap

18 mins

22.9

Batch SGD with momentum.

25 mins

22.10

Nesterov Accelerated Gradient (NAG)

8 mins

22.11

Optimizers:AdaGrad

15 mins

22.12

Optimizers : Adadelta andRMSProp

10 mins

22.13

Adam

11 mins

22.14

Which algorithm to choose when?

5 mins

22.15

Gradient Checking and clipping

10 mins

22.16

Softmax and Cross-entropy for multi-class classification.

25 mins

22.17

How to train a Deep MLP?

8 mins

Course Features

Lectures

Duration

Skill level

Language

Assessments

Chekuri Srikanth Varma

QUALIFICATION: Masters from IISC Bangalore PROFESSIONAL EXPIERENCE: 9+ years of Experience( Yahoo Labs, Matherix Labs Co-founder and Amazon)

Machine Learning

Category: Gate DA

₹10,000.00 ₹12,000.00

Instructor

Chekuri Srikanth Varma

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Machine Learning

Machine Learning

₹10,000.00 ₹12,000.00

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Machine Learning

₹10,000.00 ₹12,000.00

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