Home Courses Applied Machine Learning Online Course

Applied Machine Learning Online Course

Category: AI & Machine Learning

Applied Machine Learning Online Course

Category: AI & Machine Learning

For our Instructor led programs in Data Science & ML

Register Here

Module 1: Fundamentals of Programming
Chapters : 10 Assignments : 3 Completed :

How to utilise Appliedaicourse

1.1

How to Learn from Appliedaicourse

35 mins

1.2

How the Job Guarantee program works

16 mins

Python for Data Science Introduction

2.1

Python, Anaconda and relevant packages installations

23 mins

2.2

Why learn Python?

4 mins

2.3

Keywords and identifiers

6 mins

2.4

comments, indentation and statements

9 mins

2.5

Variables and data types in Python

32 mins

2.6

Standard Input and Output

7 mins

2.7

14 mins

2.8

Control flow: if else

10 mins

2.9

Control flow: while loop

16 mins

2.10

Control flow: for loop

15 mins

2.11

Control flow: break and continue

10 mins

Python for Data Science: Data Structures

3.1

38 mins

3.2

10 mins

3.3

4 mins

3.4

16 mins

3.5

21 mins

3.6

16 mins

Python for Data Science: Functions

4.1

13 mins

4.2

Types of functions

25 mins

4.3

Function arguments

10 mins

4.4

Recursive functions

16 mins

4.5

Lambda functions

8 mins

4.6

7 mins

4.7

6 mins

4.8

23 mins

4.9

Exception Handling

15 mins

4.10

Debugging Python

15 mins

Python for Data Science: Numpy

5.1

Numpy Introduction

41 mins

5.2

Numerical operations on Numpy

41 mins

Python for Data Science: Matplotlib

6.1

Getting started with Matplotlib

20 mins

Python for Data Science: Pandas

7.1

Getting started with pandas

8 mins

7.2

Data Frame Basics

9 mins

7.3

Key Operations on Data Frames

31 mins

Python for Data Science: Computational Complexity

8.1

Space and Time Complexity: Searching for a number in a list

20 mins

8.2

17 mins

8.3

Find elements common in two lists

6 mins

8.4

Find elements common in two lists using a Hashtable/Dict

12 mins

9.1

Introduction to Databases

22 mins

9.2

15 mins

9.3

Execution of an SQL statement.

7 mins

9.4

12 mins

9.5

Installing MySQL

11 mins

9.6

Load IMDB data.

4 mins

9.7

USE, DESCRIBE, SHOW TABLES

15 mins

9.8

20 mins

9.9

10 mins

9.10

6 mins

9.11

10 mins

9.12

WHERE, Comparison operators, NULL

13 mins

9.13

Logical Operators

27 mins

9.14

Aggregate Functions: COUNT, MIN, MAX, AVG, SUM

8 mins

9.15

13 mins

9.16

12 mins

9.17

Order of keywords.

4 mins

9.18

Join and Natural Join

12 mins

9.19

Inner, Left, Right and Outer joins.

23 mins

9.20

Sub Queries/Nested Queries/Inner Queries

24 mins

9.21

7 mins

9.22

DML:UPDATE , DELETE

6 mins

9.23

DDL:CREATE TABLE

12 mins

9.24

DDL:ALTER: ADD, MODIFY, DROP

4 mins

9.25

DDL:DROP TABLE, TRUNCATE, DELETE

3 mins

9.26

Data Control Language: GRANT, REVOKE

10 mins

9.27

Learning resources

3 mins

Module 1: Live sessions

10.1

Code Walkthrough: Basic programming & bug-fixing in Python (for AI)

10.2

Code Walkthrough: Numerical algorithms using Python (for AI)

10.3

Code Walkthrough: Numerical methods in Python (for AI) -II

10.4

Code Walkthrough: Problems in Python [ Strings and Regex ]

10.5

Code Walkthrough: Problems in Python [ Strings and Regex -II]

10.6

Code Walkthrough: Dynamic Programming & Python in-built data-structures

10.7

Code Walkthrough: OOP in Python (for AI)- I

10.8

Code Walkthrough: OOP in Python for AI -II

10.9

Code Walkthrough: Pandas in Python

10.10

Code Walkthrough: Pandas& NumPy- II

10.11

How to code effectively and build a web-scraper

10.12

Using Web-APIs in Python for Machine Learning

10.13

How to use Github?

10.14

Multi-Processing & Multithreading in Python for AI/ML

10.15

Parallel programming for training and productionization of ML/AI systems [Flask & Gunicorn]

10.16

SQL: Importance and Sample Problems

10.17

Interactive Interview Session on Python programming for ML/AI

10.18

Programming problems for AI/ML/DataScience

10.19

Smart data acquisition for ML and AI

10.20

MongoDB for Documents (NoSQL)- Part 1

10.21

MongoDB for Documents (NoSQL)- Part 2

Module 2: Data Science: Exploratory Data Analysis and Data Visualization
Chapters : 10 Assignments : 1 Completed :

Plotting for exploratory data analysis (EDA)

11.1

Introduction to IRIS dataset and 2D scatter plot

26 mins

11.2

3D scatter plot

6 mins

11.3

14 mins

11.4

Limitations of Pair Plots

2 mins

11.5

Histogram and Introduction to PDF(Probability Density Function)

17 mins

11.6

Univariate Analysis using PDF

6 mins

11.7

CDF(Cumulative Distribution Function)

15 mins

11.8

Mean, Variance and Standard Deviation

17 mins

11.9

10 mins

11.10

Percentiles and Quantiles

9 mins

11.11

IQR(Inter Quartile Range) and MAD(Median Absolute Deviation)

6 mins

11.12

Box-plot with Whiskers

9 mins

11.13

4 mins

11.14

Summarizing Plots, Univariate, Bivariate and Multivariate analysis

6 mins

11.15

Multivariate Probability Density, Contour Plot

9 mins

11.16

Assignment-1: Data Visualization with Haberman Dataset

4 mins

12.1

4 mins

12.2

Introduction to Vectors(2-D, 3-D, n-D) , Row Vector and Column Vector

14 mins

12.3

Dot Product and Angle between 2 Vectors

14 mins

12.4

Projection and Unit Vector

5 mins

12.5

Equation of a line (2-D), Plane(3-D) and Hyperplane (n-D), Plane Passing through origin, Normal to a Plane

23 mins

12.6

Distance of a point from a Plane/Hyperplane, Half-Spaces

10 mins

12.7

Equation of a Circle (2-D), Sphere (3-D) and Hypersphere (n-D)

7 mins

12.8

Equation of an Ellipse (2-D), Ellipsoid (3-D) and Hyperellipsoid (n-D)

6 mins

12.9

Square ,Rectangle

6 mins

12.10

Hyper Cube,Hyper Cuboid

3 mins

12.11

Revision Questions

30 mins

Probability and Statistics

13.1

Introduction to Probability and Statistics

17 mins

13.2

Population and Sample

7 mins

13.3

Gaussian/Normal Distribution and its PDF(Probability Density Function)

27 mins

13.4

CDF(Cumulative Distribution function) of Gaussian/Normal distribution

11 mins

13.5

Symmetric distribution, Skewness and Kurtosis

25 mins

13.6

Standard normal variate (Z) and standardization

6 mins

13.7

Kernel density estimation

7 mins

13.8

Sampling distribution & Central Limit theorem

19 mins

13.9

Q-Q plot:How to test if a random variable is normally distributed or not?

23 mins

13.10

How distributions are used?

17 mins

13.11

Chebyshev’s inequality

20 mins

13.12

Discrete and Continuous Uniform distributions

13 mins

13.13

How to randomly sample data points (Uniform Distribution)

10 mins

13.14

Bernoulli and Binomial Distribution

11 mins

13.15

Log Normal Distribution

12 mins

13.16

Power law distribution

12 mins

13.17

Box cox transform

12 mins

13.18

Applications of non-gaussian distributions?

26 mins

13.19

14 mins

13.20

Pearson Correlation Coefficient

13 mins

13.21

Spearman Rank Correlation Coefficient

7 mins

13.22

Correlation vs Causation

5 mins

13.23

How to use correlations?

13 mins

13.24

Confidence interval (C.I) Introduction

8 mins

13.25

Computing confidence interval given the underlying distribution

11 mins

13.26

C.I for mean of a random variable

14 mins

13.27

Confidence interval using bootstrapping

18 mins

13.28

Hypothesis testing methodology, Null-hypothesis, p-value

16 mins

13.29

Hypothesis Testing Intution with coin toss example

27 mins

13.30

Resampling and permutation test

15 mins

13.31

K-S Test for similarity of two distributions

15 mins

13.32

Code Snippet K-S Test

6 mins

13.33

Hypothesis testing: another example

18 mins

13.34

Resampling and Permutation test: another example

19 mins

13.35

How to use hypothesis testing?

23 mins

13.36

Proportional Sampling

18 mins

13.37

Revision Questions

30 mins

Interview Questions on Probability and statistics

14.1

Questions & Answers

30 mins

Dimensionality reduction and Visualization:

15.1

What is Dimensionality reduction?

3 mins

15.2

Row Vector and Column Vector

5 mins

15.3

How to represent a data set?

4 mins

15.4

How to represent a dataset as a Matrix.

7 mins

15.5

Data Preprocessing: Feature Normalisation

20 mins

15.6

Mean of a data matrix

6 mins

15.7

Data Preprocessing: Column Standardization

16 mins

15.8

Co-variance of a Data Matrix

24 mins

15.9

MNIST dataset (784 dimensional)

20 mins

15.10

Code to Load MNIST Data Set

12 mins

PCA(principal component analysis)

16.1

4 mins

16.2

Geometric intuition of PCA

14 mins

16.3

Mathematical objective function of PCA

13 mins

16.4

Alternative formulation of PCA: Distance minimization

10 mins

16.5

Eigen values and Eigen vectors (PCA): Dimensionality reduction

23 mins

16.6

PCA for Dimensionality Reduction and Visualization

10 mins

16.7

Visualize MNIST dataset

5 mins

16.8

Limitations of PCA

5 mins

16.9

PCA Code example

19 mins

16.10

PCA for dimensionality reduction (not-visualization)

15 mins

(t-SNE)T-distributed Stochastic Neighbourhood Embedding

17.1

7 mins

17.2

Neighborhood of a point, Embedding

7 mins

17.3

Geometric intuition of t-SNE

9 mins

17.4

Crowding Problem

8 mins

17.5

How to apply t-SNE and interpret its output

38 mins

17.6

7 mins

17.7

Code example of t-SNE

9 mins

17.8

Revision Questions

30 mins

Interview Questions on Dimensionality Reduction

18.1

Questions & Answers

30 mins

Statistical Testing and Experiments(Recorded LIVE Sessions)

19.1

Introduction to A/B Tests

19.2

How do we randomly split users ?

19.3

Metrics to compare Control and Treatment

19.4

Permutation-Resampling test

19.5

Q&A: Why permute/shuffle?

19.6

Q&A: How would shuffle programatically?

19.7

Quick Recap [till now]

19.8

Q&A + Polls with participants

19.9

Hypothesis testing: p-values, significance level & errors

19.10

Estimating the power of Permutation Tests

19.11

Code Walkthrough: Permutation Test from scratch.

19.12

Simulations using Code

19.13

Q&A with participants

19.14

Poll + Recap [till now]

19.15

Pros and Cons of Permutation Testing

19.16

Variation: Measuring clicks

19.17

Mann-Whitney-U Test

19.18

Code for Mann-Whitney-U test

19.19

Q&A with participants

19.20

Multiple testing and solutions

19.21

Remdesivir for Covid-19: Research Paper

19.22

Q&A with participants

19.23

Quick Recap (till now)

19.24

Bootstrapping for Confidence Intervals

19.25

Introduction to Multi-Arm-Bandits

19.26

Q&A with participants

Module 2: Live Sessions

20.1

Exploratory Data Analysis

20.2

Which Plot to use when and where?

20.3

Interactive Interview Session on Data Analysis

20.4

Code Walkthrough: Seaborn module for plotting in AI/ML

20.5

Code Walkthrough: Live session on Basics of Linear Algebra for AI/ML

20.6

Hands on Probability and Stats

20.7

Code-Walkthrough: Probability and statistics- I

20.8

Code-Walkthrough: Probability and statistics-II

20.9

Q&A on Probability and Statistics

20.10

Code Walkthrough: Dimensionality Reduction for ML/AI

20.11

Tableau- Part 1

20.12

Tableau- Part 2

20.13

LIVE_ Interview Questions on Probability & Stats

20.14

Live Session (24th April 2022): Statistical Tests - Part 1

20.15

Live Session (01st May 2022): Statistical Tests - Part 2

20.16

Live Session (15th May 2022): Statistical Tests - Part 3

20.17

Live Session(07th August 2022): UMAP for visualising high dimensional data

1 mins

20.18

Live Session(02nd Oct 2022): Recap of Math for ML

20.19

Live Session(16th Oct 2022): Recap of Probability and Statistics

1 mins

20.20

Live Session (1st Jan 2023): Interview Session for Data Analyst roles

Module 3: Foundations of Natural Language Processing and Machine Learning
Chapters : 12 Assignments : 5 Completed :

Real world problem: Predict rating given product reviews on Amazon

21.1

Dataset overview: Amazon Fine Food reviews(EDA)

23 mins

21.2

Data Cleaning: Deduplication

15 mins

21.3

Why convert text to a vector?

14 mins

21.4

Bag of Words (BoW)

18 mins

21.5

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

15 mins

21.6

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

9 mins

21.7

tf-idf (term frequency- inverse document frequency)

22 mins

21.8

Why use log in IDF?

14 mins

21.9

16 mins

21.10

Avg-Word2Vec, tf-idf weighted Word2Vec

9 mins

21.11

Bag of Words( Code Sample)

19 mins

21.12

Text Preprocessing( Code Sample)

11 mins

21.13

Bi-Grams and n-grams (Code Sample)

5 mins

21.14

TF-IDF (Code Sample)

6 mins

21.15

Word2Vec (Code Sample)

12 mins

21.16

Avg-Word2Vec and TFIDF-Word2Vec (Code Sample)

2 mins

Classification And Regression Models: K-Nearest Neighbors

22.1

How “Classification” works?

10 mins

22.2

Data matrix notation

7 mins

22.3

Classification vs Regression (examples)

6 mins

22.4

K-Nearest Neighbours Geometric intuition with a toy example

12 mins

22.5

Failure cases of KNN

7 mins

22.6

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

20 mins

22.7

Cosine Distance & Cosine Similarity

19 mins

22.8

How to measure the effectiveness of k-NN?

16 mins

22.9

Test/Evaluation time and space complexity

12 mins

22.10

KNN Limitations

9 mins

22.11

Decision surface for K-NN as K changes

23 mins

22.12

Overfitting and Underfitting

12 mins

22.13

Need for Cross validation

22 mins

22.14

K-fold cross validation

18 mins

22.15

Visualizing train, validation and test datasets

13 mins

22.16

How to determine overfitting and underfitting?

19 mins

22.17

Time based splitting

19 mins

22.18

k-NN for regression

5 mins

22.19

8 mins

22.20

Voronoi diagram

4 mins

22.21

Binary search tree

16 mins

22.22

How to build a kd-tree

17 mins

22.23

Find nearest neighbours using kd-tree

13 mins

22.24

Limitations of Kd tree

9 mins

22.25

3 mins

22.26

10 mins

22.27

LSH for cosine similarity

40 mins

22.28

LSH for euclidean distance

13 mins

22.29

Probabilistic class label

8 mins

22.30

Code Sample:Decision boundary .

23 mins

22.31

Code Sample:Cross Validation

13 mins

22.32

Revision Questions

30 mins

Interview Questions on K-NN(K Nearest Neighbour)

23.1

Questions & Answers

30 mins

Classification algorithms in various situations

24.1

5 mins

24.2

Imbalanced vs balanced dataset

23 mins

24.3

Multi-class classification

12 mins

24.4

k-NN, given a distance or similarity matrix

9 mins

24.5

Train and test set differences

22 mins

24.6

Impact of outliers

7 mins

24.7

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

13 mins

24.8

4 mins

24.9

Reachability-Distance(A,B)

8 mins

24.10

Local reachability-density(A)

9 mins

24.11

Local outlier Factor(A)

21 mins

24.12

Impact of Scale & Column standardization

13 mins

24.13

Interpretability

12 mins

24.14

Feature Importance and Forward Feature selection

22 mins

24.15

Handling categorical and numerical features

24 mins

24.16

Handling missing values by imputation

21 mins

24.17

curse of dimensionality

27 mins

24.18

Bias-Variance tradeoff

24 mins

24.19

Intuitive understanding of bias-variance.

7 mins

24.20

Revision Questions

30 mins

24.21

best and worst case of algorithm

6 mins

Performance measurement of models

25.1

15 mins

25.2

Confusion matrix, TPR, FPR, FNR, TNR

25 mins

25.3

Precision and recall, F1-score

10 mins

25.4

Receiver Operating Characteristic Curve (ROC) curve and AUC

19 mins

25.5

12 mins

25.6

R-Squared/Coefficient of determination

14 mins

25.7

Median absolute deviation (MAD)

5 mins

25.8

Distribution of errors

7 mins

25.9

Revision Questions

30 mins

Interview Questions on Performance Measurement Models

26.1

Questions & Answers

30 mins

27.1

Conditional probability

13 mins

27.2

Independent vs Mutually exclusive events

7 mins

27.3

Bayes Theorem with examples

18 mins

27.4

Exercise problems on Bayes Theorem

30 mins

27.5

Naive Bayes algorithm

26 mins

27.6

Toy example: Train and test stages

26 mins

27.7

Naive Bayes on Text data

16 mins

27.8

Laplace/Additive Smoothing

24 mins

27.9

Log-probabilities for numerical stability

11 mins

27.10

Bias and Variance tradeoff

14 mins

27.11

Feature importance and interpretability

10 mins

27.12

Imbalanced data

14 mins

27.13

6 mins

27.14

3 mins

27.15

Handling Numerical features (Gaussian NB)

13 mins

27.16

Multiclass classification

2 mins

27.17

Similarity or Distance matrix

3 mins

27.18

Large dimensionality

3 mins

27.19

Best and worst cases

8 mins

27.20

8 mins

27.21

Revision Questions

Logistic Regression

28.1

Geometric intuition of Logistic Regression

31 mins

28.2

Sigmoid function: Squashing

37 mins

28.3

Mathematical formulation of Objective function

24 mins

28.4

11 mins

28.5

L2 Regularization: Overfitting and Underfitting

26 mins

28.6

L1 regularization and sparsity

11 mins

28.7

Probabilistic Interpretation: Gaussian Naive Bayes

20 mins

28.8

Loss minimization interpretation

19 mins

28.9

hyperparameter Search: Grid search and random search

16 mins

28.10

Column Standardization

5 mins

28.11

Feature importance and Model interpretability

14 mins

28.12

Collinearity of features

14 mins

28.13

Train & Run time space & time complexity

11 mins

28.14

Real world cases

11 mins

28.15

Non-linearly separable data & feature engineering

28 mins

28.16

Code sample: Logistic regression, GridSearchCV, RandomSearchCV

23 mins

28.17

Extensions to Logistic Regression: Generalized linear models(GLM)

8 mins

Linear Regression

29.1

Geometric intuition of Linear Regression

13 mins

29.2

Mathematical formulation

14 mins

29.3

Real world Cases

8 mins

29.4

Code sample for Linear Regression

13 mins

Solving Optimization Problems

30.1

Differentiation

29 mins

30.2

Online differentiation tools

8 mins

30.3

Maxima and Minima

12 mins

30.4

Vector calculus: Grad

10 mins

30.5

Gradient descent: geometric intuition

19 mins

30.6

8 mins

30.7

Gradient descent for linear regression

8 mins

30.8

9 mins

30.9

Constrained Optimization & PCA

14 mins

30.10

Logistic regression formulation revisited

6 mins

30.11

Why L1 regularization creates sparsity?

17 mins

30.12

Revision questions

30 mins

Interview Questions on Logistic Regression and Linear Regression

31.1

Questions & Answers

30 mins

Module 3: Live Sessions

32.1

Code Walkthrough: Text Encodings for ML/AI

32.2

Dive deep into K-NN

32.3

Performance metrics Deep Dive

32.4

Interactive LIVE session: Logistic regression deep dive

32.5

Code Walkthrough: Optimization Methods for ML/AI

32.6

Code Walkthrough: Hyper-param Optimisation

32.7

Logistic Regression with Imbalanced data: A Geometric View

32.8

Linear Regression using Probability & Stats

Module 4: Machine Learning - II( Supervised Learning Models)
Chapters : 6 Assignments : 3 Completed :

Support Vector Machines (SVM)

33.1

Geometric Intution

20 mins

33.2

Mathematical derivation

32 mins

33.3

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

9 mins

33.4

Loss function (Hinge Loss) based interpretation

18 mins

33.5

Dual form of SVM formulation

16 mins

33.6

10 mins

33.7

Polynomial Kernel

11 mins

33.8

22 mins

33.9

Domain specific Kernels

6 mins

33.10

Train and run time complexities

8 mins

33.11

nu-SVM: control errors and support vectors

6 mins

33.12

8 mins

33.13

9 mins

33.14

14 mins

33.15

Revision Questions

30 mins

Interview Questions on Support Vector Machine

34.1

Questions & Answers

30 mins

35.1

Geometric Intuition of decision tree: Axis parallel hyperplanes

17 mins

35.2

Sample Decision tree

8 mins

35.3

Building a decision Tree:Entropy

19 mins

35.4

14 mins

35.5

Building a decision Tree:Information Gain

10 mins

35.6

Building a decision Tree: Gini Impurity

7 mins

35.7

Building a decision Tree: Constructing a DT

21 mins

35.8

Building a decision Tree: Splitting numerical features

8 mins

35.9

Feature standardization

4 mins

35.10

Building a decision Tree:Categorical features with many possible values

7 mins

35.11

Overfitting and Underfitting

8 mins

35.12

Train and Run time complexity

7 mins

35.13

Regression using Decision Trees

9 mins

35.14

12 mins

35.15

9 mins

35.16

Revision Questions

30 mins

Interview Questions on decision Trees

36.1

Questions & Answers

30 mins

Ensemble Models

37.1

What are ensembles?

6 mins

37.2

Bootstrapped Aggregation (Bagging) Intuition

17 mins

37.3

Random Forest and their construction

15 mins

37.4

Bias-Variance tradeoff

7 mins

37.5

Bagging Train and run time complexity

9 mins

37.6

Bagging:Code Sample

4 mins

37.7

Extremely randomized trees

8 mins

37.8

Random Forest :Cases

6 mins

37.9

Boosting Intuition

17 mins

37.10

Residuals, Loss functions and gradients

13 mins

37.11

Gradient Boosting

10 mins

37.12

Regularization by Shrinkage

8 mins

37.13

Train and Run time complexity