Faculty: Dr. Sujata Chakravarty

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Domain Track Title :Data Science and Machine Learning

Total Credits ( T-P-P): 26(2-9-15)

Courses Division:

For Batch - 2017 to 2021 & 2018 to 2022

1. Data Analysis and Visualisation Using Python -CUML2000- 4(0+1+3)

2. Machine Learning using Python -CUML2001- 4(1+2+1)

3. ML for Predictive Analysis -CUML2002-4(1+2+1)

4. ML for Image Analytics -CUML2003- 6(0+4+2)

5. ML for Hyperspectral Imaging -CUML2004- 6(0+4+2)

6. Internship -CUML2005- 4(0+0+4)

7. Project -CUML2006-4(0+0+4)

Note: Only one course has to be opted from course-4 & course-5.
For Batch - 2019 to 2023 

1. ML for Predictive Analysis -CUML2002-4(1+2+1)

2. ML for Image Analytics -CUML2003- 6(0+4+2)

3. ML for Hyperspectral Imaging -CUML2004- 6(0+4+2)

4. Digital Video Processing -CUML2007- 4(0+2+2)

5. IoT Analytics-CUML2008- 4(0+2+2)

6. Internship -CUML2005- 4(0+0+4)

7. Project -CUML2006-4(0+0+4)

Note: Only one course has to be opted from course-2 & course-3.
For Batch - 2023 to 2027 

1. Machine Learning for Predictive Analytics -CUML1021-4(0+2+2)

2. Deep Learning for Image Analytics -CUML1022-4(0+2+2)

3. Data Analytics using Tableau -CUML1023-4(0+2+2)

4. 
   ML for Spectral Imaging -CUML1024- 4(0+2+2)
   

5. Project -CUML1025-6(0+0+6)

Note: Only one course has to be opted from course-2 & course-4.

Domain Track Objectives:

• Understand the scope, stages, applications, effects and challenges of ML.

• Understand the mathematical relationships within and across ML algorithms and the paradigms of supervised and unsupervised learning.

Domain Track Learning Outcomes:

• Ability to Create and incorporate ML solutions in their respective fields of study.

• Ability to design and implement various machine learning algorithms in a range of real-world applications.

• Ability to design product/ publish article/ file patent

Domain Syllabus:

Course 1: Data Analysis and Visualisation Using Python (0+1+3)

1.1 Story Board Development:-

• The objective and flow of the story to be understood through cases.

1.2 Data Reading using Python Functions;-

• Python libraries: Pandas, NumPy, Plotly, Matplotlib, Seaborn, Dash.

• Data collection from online data sources

• Web scrap, data formats such as HTML, CSV, MS Excel.

• Data compilation, arranging and reading data, data munging

1.3 Data Visualisation using Python Libraries:-

• Using graphs- Scatterplot, Line chart, Histogram, Bar chart, Bubble chart, Heatmaps .

• Dashboard Basics- Layout, Reporting, Infographics, Interactive components, live updating.

Projects

• COVID 19

• World Development Indicators

• ERP dashboarding

• Details of Social/ Empowerment schemes of Govt.

References:

• https://courseware.cutm.ac.in/files/2019/04/Python-for-Data-Analysis-2nd-Edition.pdf
• https://towardsdatascience.com/data-visualization/home

Course 2: Machine Learning using Python (1+2+1)

2.1 Application and Environmental-setup:- 

• Applications of Machine Learning In different fields (Medical science, Agriculture, Automobile, mining and many more).

• Supervised vs Unsupervised Learning based on problem Definition.

• Understanding the problem and its possible solutions using IRIS datasets.

• Python libraries suitable for Machine Learning(numpy, scipy, scikit-learn, opencv)

• Environmental setup and Installation of important libraries.

2.2 Regression:- 

• Linear Regression

• Non-linear Regression

• Model Evaluation in Regression

• Evaluation Metrics in Regression Models

• Multiple Linear Regression

• Feature Reduction using PCA

• Implementation of regression model on IRIS datasets.

2.3 Classification:- 

• Defining Classification Problem with IRIS datasets.

• Mathematical formulation of K-Nearest Neighbour Algorithm for binary classification.

• Implementation of K-Nearest Neighbour Algorithm using sci-kit learn.

• Classification using Decision tree.

• Construction of  decision trees based on entropy.

• Implementation of Decision Trees for Iris datasets .

• Classification using Support Vector Machines.

• SVM for Binary classification

• Regulating different functional parameters of SVM using sci-kit learn.

• SVM for multi class classification.

• Implementation of SVM using Iris datasets .

• Implementation of Model Evaluation Metrics using sci-kit learn and IRIS datasets.

2.4 Unsupervised Learning:- 

• Defining clustering and its application in ML .

• Mathematical formulation of K-Means Clustering.

• Defining K value and its importance in K-Means Clustering.

• Finding appropriate K value using elbow technique for a particular problem.

• Implementation of K-Means clustering for IRIS datasets

Projects

• To be defined based on respective study area of student.

References:

Text Book:

1. Ethem Alpaydin, Introduction to Machine Learning, Second Edition, http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=12012.

Web Resource:

1.  https://towardsdatascience.com/beginners-guide-to-machine-learning-with-python-b9ff35bc9c51

Course 3: Machine Learning for Predictive Analytics (0+2+2)

Course Outcomes:

COs	Course outcomes
CO1	Students will gain comprehensive knowledge of predictive analytics, including key concepts, methodologies, and applications.
CO2	Students will develop strong analytical skills and the ability to critically assess and interpret data for predictive modeling.
CO3	Students will enhance their problem-solving skills and make informed decisions based on predictive analysis results.
CO4	Students will gain hands-on experience with various machine learning tools and techniques, designing and developing predictive models.
CO5	Students will be able to conduct research in predictive analytics, utilizing advanced techniques and methodologies to solve complex problems.

3.1 Introduction to Predictive Analytics:

• Overview of Predictive Analytics
• Applications and Case Studies

3.2 Data Preprocessing:

• Data Cleaning
• Feature Engineering
• Data Transformation

3.3 Supervised Learning Algorithms:

• Linear Regression
• Logistic Regression
• Decision Trees
• Random Forests
• Support Vector Machines

3.4 Model Evaluation and Selection:

• Train-Test Split
• Cross-Validation
• Performance Metrics

3.5 Advanced Machine Learning Techniques:

• Ensemble Methods
• Gradient Boosting Machines (GBM)
• Hyperparameter Tuning

3.6 Unsupervised Learning:

• Clustering
• Dimensionality Reduction

3.7 Model Deployment:

• Introduction to Model Deployment
• Tools and Techniques for Deploying Models

Practices:

• Text pre-processing
• Term frequency-Inverse document frequency
• Simple Linear Regression
• Multiple Linear Regression
• Classification using K-Nearest Neighbors
• Classification using Decision Tree
• Classification using Random Forest
• Classification using Support Vector Machine
• K-Means Clustering
• Hierarchical Clustering
• Data Cleaning Strategies with Python
• Advanced Data Cleaning Techniques with Pandas
• Feature Engineering Fundamentals and Advanced Methods
• Advanced Feature Engineering with Python
• Data Transformation Techniques and Applications
• Model Deployment

Projects:
 

• Plagiarism detection Tool
• Customer Support and Virtual Assistants
• Automated Resume parsing
• Text Summarization
• Discourse analysis
• Automatic question generation
• Machine Translation
• Automatic Text Evaluation
• Question Answering System
• Automatic paperless examination
• Retail and E-commerce Support
• Building AI-Powered Chatbots
• AI Career Mentor
• AI-Driven Text Generation with Large Language Models
• Healthcare Outcome Prediction
• Weather Forecasting and Climate Pattern Prediction
• News and Information Hub
• Stock Market Trend Prediction
• Energy Consumption Forecasting for Smart Grids
• Real-Time Traffic Flow Prediction for Smart Cities

Text Books:

1. Introduction to Statistical Learning" by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
2. Machine Learning Yearning" by Andrew Ng

Course 4: Deep Learning for Image Analytics (0+2+2)
Project/Task: (Choose one among six Tasks)

• Detection of optometry diseases using retinal fundus imaging.

• Diabetic Retinopathy

• Glaucoma

• Cataract

• Detection of various diseases using X-ray imaging.

• Covid19

• Leaf disease classification using RGB images.

• Tomato leaf

• Potato leaf

Concept Required:
4.1 Image Pre-processing:-

• Accessing individual pixels using matrix concept

• Image resize, grey scale conversion, Colour channel splitting

• Histogram equalisation (CLACH).

4.2 Image Feature Extraction: -

• Edge detection (Sobel, Canny), Morphological operations

• Image segmentation, Image Thresholding, Binary conversion

• Cluster based segmentation

• Feature extraction based on size, shape and colour

• Feature extraction using predefined functions: SIFT, SURF, STAR, ORB.

• Feature Extraction using convolutional neural network (CNN).

4.3 Creation of Feature Matrix by combining Extracted Features: -

• Matrix flattening, Horizontal stacking, Vertical stacking, padding.

• Splitting the feature matrix (training/testing)  and labelling.

4.4 Classification algorithms: -

• Support vector machine (SVM)

• Different kernels of SVM (linear, polynomial, radial basis function).

• Gradient Boosting (GB)

• Multi-layer Perceptron (MLP), deep learning.

Course 5: Data Analytics using Tableau (0+2+2)

Course Outcomes:

COs	Course outcomes
CO1	Ability to connect various data sources to Tableau and import data for visualization.
CO2	Proficiency in creating a wide range of visualizations including bar charts, scatter plots, and maps using Tableau.
CO3	Skill in applying advanced visualization techniques such as heat maps, tree maps, and histograms to analyze complex data sets.
CO4	Competence in utilizing Tableau calculations including calculated fields, table calculations, and Level of Detail (LOD) expressions for custom data analysis.
CO5	Capability to design interactive dashboards and stories in Tableau that effectively communicate insights and trends to stakeholders.

5.1 Introduction to Tableau:

• Overview of Tableau
• Connecting to Data Sources

5.2 Data Visualization Principles

• Best Practices in Data Visualization
• Types of Visualizations

5.3 Building Basic Visualizations

• Bar Charts
• Line Charts
• Pie Charts
• Scatter Plots
• Maps

5.4 Advanced Visualizations:

• Heat Maps
• Tree Maps
• Bullet Charts
• Histograms
• Box Plots

5.5 Calculations in Tableau:

• Calculated Fields
• Table Calculations
• Level of Detail (LOD) Expressions

5.6 Dashboard Design:

• Creating Dashboards
• Dashboard Interactivity
• Storytelling with Data

5.7 Data Analysis and Reporting:

• Parameters and Filters
• Trend Analysis
• Forecasting
• Cohort Analysis

Practices

• Bar chart, Line chart, and Pie charts
• Scatter plots and Maps
• Heat maps, Tree maps, and Bullet charts
• Histograms and Box plots
• Text Object on Dashboard in Tableau
• Image Object on Dashboard in Tableau
• Tableau – Objects on Dashboard
• Tableau – Filters in Dashboard
• Tableau – Device Preview
• Format Dashboard Layout in Tableau
• Create a Dashboard in Tableau
• Text Object on Dashboard in Tableau
• Image Object on Dashboard in Tableau
• Tableau – Objects on Dashboard
• Tableau – Filters in Dashboard
• Tableau – Device Preview
• Format Dashboard Layout in Tableau

Projects

• Visualize detected plagiarism trends, sources, and similarity scores
• Visualize candidate skill trends and hiring patterns
• Display summary trends and sentiment analysis from multiple documents
• Predict emerging discussion themes for forums or social media
• Track generated questions by topic, difficulty, and accuracy
• Predict error trends for specific language pairs
• Track product sales, customer demographics, and seasonal trends
• Visualize patient risk factors and recovery probabilities
• Predict future stock movements using historical indicators
• Show real-time temperature, rainfall, and weather anomalies
• Visualize traffic density, congestion points, and travel time

Text Books:

1. "Learning Tableau" by Joshua N. Milligan
2. "Tableau Your Data!" by Daniel G. Murray

Course 6: ML for Spectral Imaging (0+2+2)
Project/Task: (Choose one among four Tasks)

• Agriculture

• Crop yield prediction.

• Crop quality prediction

• Soil health monitoring

• Mining

• Iron ore quality prediction

Concept Required:
5.1 Introduction to Remote Sensing: -

• Multi-Spectral Imagery (MSI)

• Hyperspectral Imagery (HSI)

5.2 Scientific Principles:

• Physics of imaging spectroscopy

• Electromagnetic propagation

• Sensor physics

• Atmospheric Corrections.

5.2 Hyperspectral Concepts and System Trade-offs:-

• Signal-to-Noise ratio (SNR)

• Spectral resolution, sampling.

5.3 HSI Data Processing Techniques:-

• Spectral angle mapping

• Principal Component Analysis (PCA)

• Minimum Noise Fraction (MNF)

• Spectral feature fitting.

5.4 Classification Techniques:-

• Support Vector Machine (SVM)

• Partial Least Squares Regression (PLSR)

• Neural Network

• Deep learning and CNN

5.5 Clustering Techniques:-

• K-mean clustering

Course 7: IoT Analytics (0-2-2)


7.1  Defining IoT Analytics and Challenges

IoT
Benefits of Deploying IoT
End to End IoT architecture
IoT challenges

7.2  IoT Protocols

7.2.1  Wireless Protocol

Connectivity Protocols (when Power is Limited)
Bluetooth Low Energy (BLE)
Zigbee
LoRaWAN
NFC
       7.2.2  Connectivity Protocols (when Power is Not a problem)
Wifi

7.2.3   Data Communication Protocol

MQTT
Web-Socket
HTTP

7.2  Sensors

Types of Sensors based on communication-I2C, SPI
Types of Sensors based on Application

7.3  Overview of 32 -bit Controller

ESP8266
ESP32
Raspberry Pi

7.4  AWS IoT for Cloud

AWS IoT Core services
AWS IoT Analytics services
AWS DynamoDB Services

7.5  Thingspeak for IoT

Getting and posting Data to IoT Cloud using ESP devices
Posting Data to IoT Cloud using Raspberry Pi
7.6  ThingWorx for Industrial IoT
Building Dashboard on Thingworx platform
Binding the senor value to the dashboard

Text Book:

• Minteer, Andrew. Analytics for the Internet of Things (IoT). Packt Publishing Ltd, 2017.

Reference Books:

2. Geng, Hwaiyu, ed. Internet of things and data analytics handbook. John Wiley & Sons, 2017.
Course 8: Project (0-0-4)Course 9: Internship (0-0-4)
Session Plan for the Entire Domain:Data Analysis and Visualisation Using Python (0+1+3) 60 hrs

Session 1

Course objective, outcome, methodology and assessment. 
Why data visualisation
https://www.youtube.com/watch?v=YaGqOPxHFkc
https://www.youtube.com/watch?v=3JWK5gRI9p0

Session 2

Story telling using Visuals & Infographics
https://venngage.com/blog/9-types-of-infographic-template/#1
https://www.edugrad.com/tutorials/learn-data-visualization-using-python/15
https://towardsdatascience.com/storytelling-with-data-a-data-visualization-guide-for-business-professionals-97d50512b407
Tips on good visuals
https://statedashboard.odisha.gov.in/
https://data.gov.in/
https://www.youtube.com/watch?v=4pymfPHQ6SA
Project Groups:
Students will be divided into groups and assigned projects. Each group will do two projects.

Session 3

Practice 
Environmental setup - Anaconad and Jupyter notebook, Anaconda Navigator and Libraries Installation
https://www.youtube.com/watch?v=beh7GE4FdnM

Session 4 & 5

Practice
Python Fundamentals, Use Case - Data Analysis, Exploring and learning assignments on Jupyter Notebook
https://towardsdatascience.com/data-visualization-say-it-with-charts-in-python-138c77973a56
https://towardsdatascience.com/plotting-with-python-c2561b8c0f1f
https://towardsdatascience.com/introduction-to-data-visualization-in-python-89a54c97fbed

Session 5 & 6

Project - 1
For Project -1, the student group has to define the objective/s of the study, identify the data that will be needed and the source of such data
Make Presentations groupwise

Session 7, 8 & 9

Practice 
Data collection/importing and reading using Python function of different types of files, i.e. CSV, HTML, Excel - Get CSV data files from source and read them, get HTML file and read, Get Excel sheet and read
https://perso.telecom-paristech.fr/eagan/class/igr204/datasets
https://www.youtube.com/watch?v=eWFwe41LyWk
https://towardsdatascience.com/wrangling-data-with-pandas-27ef828aff01
https://www.youtube.com/watch?v=ndwuUzgAiPY
https://www.youtube.com/watch?v=Ycq3sDg6ji0
Sorting data, Missing values & Munging data
https://www.youtube.com/watch?v=-dwjEfv2R50
https://www.youtube.com/watch?v=EaGbS7eWSs0
https://www.youtube.com/watch?v=T11QYVfZoD0
https://www.askpython.com/python/python-csv-module

Session 10 & 11

Project - 1
Data collection and sorting for the assigned project
Pandas Tutorial 1. What is Pandas python? Introduction and Installation- https://www.youtube.com/watch?v=CmorAWRsCAw
Pandas Tutorial2. Dataframe and Series Basics- Selecting row and column- https://www.youtube.com/watch?v=zmdjNSmRXF4
Pandas Tutorial 3: Different Ways Of Creating DataFrame - https://www.youtube.com/watch?v=3k0HbcUGErE
Python Pandas Tutorial 4: Read Write Excel CSV File- https://www.youtube.com/watch?v=-0NwrcZOKhQ
Importing data in python - Read excel file - https://www.youtube.com/watch?v=lco-r5CgvhY
Pandas Tutorial 8 | How to import HTML data in Python | Importing HTML data in Python - https://www.youtube.com/watch?v=ndwuUzgAiPY
Pandas Tutorial 13, Crosstabs - https://www.youtube.com/watch?v=I_kUj-MfYys

Session 12 & 13

Practice
Basics of Numpy
https://www.youtube.com/watch?v=xECXZ3tyONo
Complete Python NumPy Tutorial (Creating Arrays, Indexing, Math, Statistics, Reshaping) - https://www.youtube.com/watch?v=GB9ByFAIAH4

Session 14 & 15

Practice 
Basic of Pandas
https://www.youtube.com/watch?v=dcqPhpY7tWk
https://www.tutorialspoint.com/python_pandas/python_pandas_pdf_version.htm

Session 16 & 17

Practice
Basic of Matplotlib
https://www.youtube.com/watch?v=MbKrSmoMads&pbjreload=10
Matplotlib Tutorial 1 - Introduction and Installation- https://www.youtube.com/watch?v=qqwf4Vuj8oM
Matplotlib Tutorial 2 - format strings in plot function - https://www.youtube.com/watch?v=zl5qPnqps8M
Matplotlib Tutorial 3 - Axes labels, Legend, Grid- https://www.youtube.com/watch?v=oETDriX9n1w
Matplotlib Tutorial 4 - Bar Chart - https://www.youtube.com/watch?v=iedmZlFxjfA
Matplotlib Tutorial 5 - Histograms - https://www.youtube.com/watch?v=r75BPh1uk38
Matplotlib Tutorial 6 - Pie Chart - https://www.youtube.com/watch?v=GOuUGWGUT14
Matplotlib Tutorial 7 - Save Chart To a File Using savefig - https://www.youtube.com/watch?v=XLJHkCn48lM
Plotting real-time data using Python - https://www.youtube.com/watch?v=GIywmJbGH-8

Session 18, 19, 20 & 21

Project (work on Project -1)
Work on the projects assigned using Python Libraries
Pandas Tutorial 1. What is Pandas python? Introduction and Installation- https://www.youtube.com/watch?v=CmorAWRsCAw
Pandas Tutorial 3: Different Ways Of Creating DataFrame - https://www.youtube.com/watch?v=3k0HbcUGErE
Python Pandas Tutorial 4: Read Write Excel CSV File
https://www.youtube.com/watch?v=-0NwrcZOKhQ
Importing data in python - Read excel file - https://www.youtube.com/watch?v=lco-r5CgvhY
Pandas Tutorial 8 | How to import HTML data in Python | Importing HTML data in Python - https://www.youtube.com/watch?v=ndwuUzgAiPY
Pandas Tutorial 13, Crosstabs - https://www.youtube.com/watch?v=I_kUj-MfYys

Session 22 & 24

Practice 
Plotly
https://plotly.com/python/basic-charts/
Graphing Library - https://plotly.com/python/
Plotly Python - Plotly multi line chart| Plotly Python data visualization- https://www.youtube.com/watch?v=pfhBbJ2MnMI
Plotly Data Visualization in Python | Part 13 | how to create bar and line combo chart - https://www.youtube.com/watch?v=AQG4RQolUC8
Plotly Web based visualisation - https://www.youtube.com/watch?v=B911tZFuaOM

Session 25, 26, 27 & 28

Project (Project-1)
Work on Project
Interim Presentation

Session 29 & 30

Practice
Seaborn - Scatter Chart, Bubble Chart, Gapminder
https://www.youtube.com/watch?v=MGOcVAOuXxo
https://www.tutorialspoint.com/seaborn/seaborn_tutorial.pdf
Interactive Data Visualization - https://www.youtube.com/watch?v=VdWfB30QTYI

Session 31 & 32

Practice
Web Scrapping
https://www.youtube.com/watch?v=mKxFfjNyj3c
Web scraping in Python (Part 4)_ Exporting a CSV with pandas - https://www.youtube.com/watch?v=Zh2fkZ-uzBU
Web scraping in Python (Part 2)_ Parsing HTML with Beautiful Soup - https://www.youtube.com/watch?v=zXif_9RVadI
Webscraping - Mode, Median, Mean, Range, and Standard Deviation- https://www.youtube.com/watch?v=mk8tOD0t8M0
Web Scraping Dynamic Graphs to CSV Files using Python - https://www.youtube.com/watch?v=NYK_1bVoBfU

Session 33 & 34

Practice
Solve the 10 problem (started from session 7)
-students will submit the assignment (in groups)
(upload the assignment ...done by Prof. Ramana)

Session 35 & 36

Practice 
Dashboard Basics
https://www.youtube.com/watch?v=e4ti2fCpXMI
Create Presentation Slides from Jupyter - https://www.youtube.com/watch?v=utNl9f3gqYQ
Dash and Python 1_ Setup -https://www.youtube.com/watch?v=Ldp3RmUxtOQ
Dash and Python 2_ Dash Core Components - https://www.youtube.com/watch?v=NM8Ue4znLP8
Dash and Python 3_ Using CSS - https://www.youtube.com/watch?v=x9mUZZ19dl0

Session 37 & 38

Practice
Dash (plotly) and Python
https://www.youtube.com/watch?v=Ldp3RmUxtOQ
Dash in 5 Minutes  - https://www.youtube.com/watch?v=e4ti2fCpXMI
How to Create a Slideshow using Jupyter+Markdown+Reveal.js- https://www.youtube.com/watch?v=EOpcxy0RA1A
ipython dashboard - https://www.youtube.com/watch?v=LOWBEYDkn90
Plotting real-time data using Python - https://www.youtube.com/watch?v=GIywmJbGH-8

Session 38 & 39

Practice 
Interactive charts/Maps using Bokeh,
Dash board using Dash
https://www.youtube.com/watch?v=o4TB6LTPDaY
https://towardsdatascience.com/how-to-build-a-complex-reporting-dashboard-using-dash-and-plotl-4f4257c18a7f

Session 40 & 41

Practice
IPython-Dashboard
Live graphs
https://pypi.org/project/IPython-Dashboard/
https://pythonprogramming.net/live-graphs-data-visualization-application-dash-python-tutorial/

Session 42 & 43

Project
Work on Project - 1 to make dash boards

Session 44, 45 & 46

Project
Final presentation of Project -1

Session 47 & 48

Project -2
Start Project - 2 (ERP Dash Board)
Define the objective and prepare the flow chart

Session 49 & 50

Project
Make presentations on the objective and flow chart of Project-2

Session 50 & 51

Project
Work on Project - 2

Session 52 & 53

Project
Make interim presentation on Project - 2

Session 54 & 55

Project
Work on Project - 2

Session 56 & 58

Project
Final Presentation on Project -2

Session 59 & 60

Project
Make final changes on Project -1 & Project -2 to make it ready for External Evaluation
Machine Learning using Python (1+2+1) 56 hrs

Session-1

Applications of Machine Learning

https://www.youtube.com/watch?v=ahRcGObyEZo

Session-2,3

Supervised vs Unsupervised Learning based on problem Definition

https://www.youtube.com/watch?v=cfj6yaYE86U

Session-4,5

Understanding the problem and its possible solutions using IRIS datasets.

https://www.youtube.com/watch?v=FLuqwQgSBDw

Session-6,7

Mathmatical library in Python numpy and its  functions

https://www.youtube.com/watch?v=EkYrfV7M1ks

Session-8,9

Science library in Python scipy and its  functions

https://www.youtube.com/watch?v=k8s-R3csOt0

session-10,11

ML library in Python scikit-learn and its  functions.

https://www.youtube.com/watch?v=bwZ3Qiuj3i8

Session-12

Defining student specific Project

Session-13

Linear Regression

https://www.youtube.com/watch?v=E5RjzSK0fvY

Session-14

Non-linear Regression

https://www.youtube.com/watch?v=sKrDYxQ9vTU

Session-15

Model Evaluation

https://www.youtube.com/watch?v=c68JLu1Nfkw

Session-16

Evaluation Metrics in Regression Models

https://www.youtube.com/watch?v=iLfgZfRGisE

Session-17,18

Multiple Linear Regression

https://www.youtube.com/watch?v=dQNpSa-bq4M

Session-19

Feature Reduction using PCA

https://www.youtube.com/watch?v=FgakZw6K1QQ

Session-20

Implementation of regression model on IRIS datasets.

https://www.youtube.com/watch?v=hd1W4CyPX58

Session-21

Defining Classification Problem with IRIS datasets.

https://www.youtube.com/watch?v=Y17Y_8RK6pc

Session-22,23

Create the train/test set using scikit-learn using scikit-learn

https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html

Session-24,25

Confussion Matrix, Accuraccy, Sensitivity, specificity

https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html

Session-26

Mathematical formulation of K-Nearest Neighbour Algorithm for binary classification.

https://www.youtube.com/watch?v=4HKqjENq9OU

Session-27,28

Implementation of K-Nearest Neighbour Algorithm using sci-kit learn.

https://www.youtube.com/watch?v=6kZ-OPLNcgE

Session-29,30

Classification using Decision tree.

https://www.youtube.com/watch?v=7VeUPuFGJHk

Session-31,32

Construction of  decision trees based on entropy.

https://www.youtube.com/watch?v=7VeUPuFGJHk

Session-33,34

Implementation of  Decision Tree using sci-kit learn

https://www.youtube.com/watch?v=PHxYNGo8NcI

Session-35,36

Classification using Support Vector Machines.

https://www.youtube.com/watch?v=Y6RRHw9uN9o

Session-37,38

SVM for Binary classification

https://www.youtube.com/watch?v=T5zJHhTO1FA

Session-39,40

Regulating different functional parameters of SVM using sci-kit learn.

https://www.youtube.com/watch?v=93AjE1YY5II

Session-41,42

SVM for multi class classification.

https://www.youtube.com/watch?v=kH6T_XL10-A

Session-43,44

 Implementation of Support Vector Machines.

https://www.youtube.com/watch?v=zEabrO9l1vg

Session-45,46

Defining clustering and its application in ML

https://www.youtube.com/watch?v=V-8E0KhNrI8

Session-47,48

Mathematical formulation of K-Means Clustering.

https://www.youtube.com/watch?v=YWgcKSa_2ag

Session-49,50

Defining K value and its importance in K-Means Clustering.

https://www.youtube.com/watch?v=4b5d3muPQmA

Session-51,52

Implementation of K-Means Clustering in  Scikit-learn

https://www.youtube.com/watch?v=asW8tp1qiFQ

Session-53,54

Finding appropriate K value using elbow technique for a particular problem.

https://www.youtube.com/watch?v=IEBsrUQ4eMc

Session-55,56

predicting iris flower species with k-means clustering.

https://medium.com/@belen.sanchez27/predicting-iris-flower-species-with-k-means-clustering-in-python-f6e46806aaee

Machine Learning for Predictive Analytics (0+2+2) 52 hrs

Lect. No	Topic
1	Overview of Predictive Analytics
	Introduction to Predictive Analytics with Python:
	https://youtu.be/tdV9L3C-hxQ?si=NY0HHcWFLy9k5cuN
2	Understanding Predictive Analytics:
	https://youtu.be/tdV9L3C-hxQ?si=Gt6TSJxq3F6VkY1Z
3	Applications of Predictive Analytics
	Predictive Analytics in Finance with Python:
	https://youtu.be/tdV9L3C-hxQ?si=troHVzFHg3CKuqHA
4	Predictive Analytics in Healthcare using Python:
	https://youtu.be/g56U9OrSmE0?si=gothM14fokogpidw
	Demo: Concept through PPT + Implementation
5	Data Cleaning
	Data Cleaning in Python:
	https://youtu.be/bDhvCp3_lYw?si=bMb5GF3fhKwVO8Wl
	Demo: Concept through PPT + Implementation
6	Python Data Cleaning with Pandas:
	https://youtu.be/OS2m0f2gVJ0?si=Ne-qpdaa1IThxoFN
	Demo: Concept through PPT + Implementation
7	Feature Engineering
	Feature Engineering in Python:
	https://youtu.be/GduT2ZCc26E?si=uy7fnf_M2miKpg5R
	Demo: Concept through PPT + Implementation
8	Python Feature Engineering Tutorial:
	https://youtu.be/uu8um0JmYA8?si=i4zrT49vrFJbxQYC
	Demo: Concept through PPT + Implementation
9	Data Transformation
	Data Transformation in Python:
	https://youtu.be/w3jQyl8ojJA?si=wFxJ2lxHRc5blG8J
	Demo: Concept through PPT + Implementation
10	Data Transformation with Pandas:
	https://youtu.be/JPey7neLDzo?si=hAS5TJouV-biqxrV
	Demo: Concept through PPT + Implementation
11	Combining Data Cleaning and Transformation
	Cleaning and Transforming Data in Python:
	https://youtu.be/iaZQF8SLHJs?si=Cd60Yu3PvIiS4tx-
	Demo: Concept through PPT + Implementation
12	Data Cleaning and Transformation with Pandas:
	https://youtu.be/w3jQyl8ojJA?si=8nWj0r_YqjJsuscT\
	Demo: Concept through PPT + Implementation
13	Linear Regression
	Linear Regression in Python:
	https://youtu.be/EMIyRmrPWJQ?si=bF5Gw8J2aWeEUMXe
	Demo: Concept through PPT + Implementation
14	Python Linear Regression Tutorial:
	https://youtu.be/8jazNUpO3lQ?si=t-oBC4YU6il-TXqW
	Demo: Concept through PPT + Implementation
15	Linear Regression
	Advanced Linear Regression in Python:
	https://youtu.be/NUXdtN1W1FE?si=HMEOgy8KSu4KV0aR
	Demo: Concept through PPT + Implementation
16	Linear Regression with Scikit-Learn:
	https://youtu.be/b0L47BeklTE?si=sj0NPLw-oo9kpVNJ
	Demo: Concept through PPT + Implementation
17	Logistic Regression
	Logistic Regression in Python:
	https://youtu.be/HYcXgN9HaTM?si=SrqF2IQhknoWofsG
	Demo: Concept through PPT + Implementation
18	Python Logistic Regression Tutorial:
	https://youtu.be/YYEJ_GUguHw?si=8RdbiGZxs3PUh-st
	Demo: Concept through PPT + Implementation
19	Logistic Regression
	Advanced Logistic Regression in Python:
	https://youtu.be/XnOAdxOWXWg?si=0WWVe--0Co8P8yrs
	Demo: Concept through PPT + Implementation
20	Logistic Regression with Scikit-Learn:
	https://youtu.be/bSXIbCZNBw0?si=aHSaCoyQe2FPcC56
	Demo: Concept through PPT + Implementation
21	Decision Trees
	Decision Trees in Python:
	https://youtu.be/RmajweUFKvM?si=7OORxjrgixQz2u2N
	Demo: Concept through PPT + Implementation
22	Python Decision Trees Tutorial:
	https://youtu.be/PHxYNGo8NcI?si=kLiankY7qI1d3mNZ
	Demo: Concept through PPT + Implementation
23	Decision Trees
	Advanced Decision Trees in Python:
	https://youtu.be/RmajweUFKvM?si=Jw6pkTJsT5LmpWoh
	Demo: Concept through PPT + Implementation
24
	Decision Trees with Scikit-Learn:
	https://youtu.be/wxS5P7yDHRA?si=XutJFWMqQlKybjBH
	Demo: Concept through PPT + Implementation
25	Random Forests
	Random Forests in Python:
	https://youtu.be/ok2s1vV9XW0?si=zC3RrjBvOW-f2IFD
	Demo: Concept through PPT + Implementation
26	Python Random Forests Tutorial:
	https://youtu.be/MxiktOPmhV8?si=Qmu5xaCW0lZBrsUv
	Demo: Concept through PPT + Implementation
27	Random Forests
	Advanced Random Forests in Python:
	https://youtu.be/v6VJ2RO66Ag?si=-Y_X6KmHwJWBa-GZ
	Demo: Concept through PPT + Implementation
28	Random Forests with Scikit-Learn:
	https://youtu.be/Kt9_AI12qtM?si=YvzXDLvx_SOb9qac
	Demo: Concept through PPT + Implementation
29	Support Vector Machines (SVM)
		Support Vector Machines in Python:
	https://youtu.be/8A7L0GsBiLQ?si=O0CAiHWUaN9nKt72
	Demo: Concept through PPT + Implementation
	30	Python SVM Tutorial:
https://youtu.be/FB5EdxAGxQg?si=E640trkEWldE1mnr
Demo: Concept through PPT + Implementation
31	Support Vector Machines (SVM)
	Advanced SVM in Python:
	https://youtu.be/7sz4WpkUIIs?si=Hpr2vmMknBdi0QvP
	Demo: Concept through PPT + Implementation
32	SVM with Scikit-Learn:
	https://youtu.be/kPkwf1x7zpU?si=p3krdI2qy0Xzc8Qm
	Demo: Concept through PPT + Implementation
33	Train-Test Split and Cross-Validation
	Train-Test Split in Python:
	https://youtu.be/BUkqYGPnLZ8?si=EyGn0nt_4ZNR-Q7D
	Demo: Concept through PPT + Implementation
34	Cross-Validation Techniques:
	https://youtu.be/-8s9KuNo5SA?si=utuoPsFU-seW-Ies
	Demo: Concept through PPT + Implementation
35	Train-Test Split and Cross-Validation
	Advanced Cross-Validation Techniques:
	https://youtu.be/xE9cIcJf48A?si=dg-ksI83BYzChrbk
	Demo: Concept through PPT + Implementation
36	Cross-Validation with Scikit-Learn:
	https://youtu.be/dKncsQpMTKQ?si=dYEFRDu8qql2fqeA
	Demo: Concept through PPT + Implementation
37	Performance Metrics
	Performance Metrics in Python:
	https://youtu.be/aWAnNHXIKww?si=A1imtKoa7UEPvwzY
	Demo: Concept through PPT + Implementation
38	Python Metrics Tutorial:
	https://youtu.be/ogZXKrOjdCM?si=6cSlkOD_k301eHf0
	Demo: Concept through PPT + Implementation
	Performance Metrics
	Advanced Performance Metrics in Python:
	https://youtu.be/aWAnNHXIKww?si=RDB5u4nYgfnpjLHz
	Demo: Concept through PPT + Implementation
39	Metrics with Scikit-Learn:
	https://youtu.be/0B5eIE_1vpU?si=_BVz1ztVx2g9wPgK
	Demo: Concept through PPT + Implementation
	Ensemble Methods
	Ensemble Methods in Python:
	https://youtu.be/RtrBtAKwcxQ?si=-AdJhbeOq2xIcSU0
	Demo: Concept through PPT + Implementation
40	Python Ensemble Methods Tutorial:
	https://youtu.be/KIOeZ5cFZ50?si=-wSzRi8BvXSJ3ETU
	Demo: Concept through PPT + Implementation
	Ensemble Methods
	Advanced Ensemble Methods in Python:
	https://youtu.be/p_vu0g-MrAw?si=QDsCOi1Adf0huVpX
	Demo: Concept through PPT + Implementation
41	Ensemble Methods with Scikit-Learn:
	https://youtu.be/NqdyfMbVo1Q?si=djTchtvp8e0ljSjF
	Demo: Concept through PPT + Implementation
	Gradient Boosting Machines (GBM)
	Gradient Boosting Machines in Python:
	https://youtu.be/Nol1hVtLOSg?si=hAr_p-Bh4_AF3YrR
	Demo: Concept through PPT + Implementation
42	Python GBM Tutorial:
	https://youtu.be/BVMa4nXjQCM?si=eTsAcjLv0F9wQM0E
	Demo: Concept through PPT + Implementation
	Gradient Boosting Machines (GBM)
	Advanced GBM in Python:
	https://youtu.be/SW3akc0ho7M?si=vHlZfawBE4O-v2BZ
	Demo: Concept through PPT + Implementation
43	GBM with Scikit-Learn:
	https://youtu.be/E2mCaIZNE2g?si=-wQl-0F5wtYWyqq2
	Demo: Concept through PPT + Implementation
	Hyperparameter Tuning
	Hyperparameter Tuning in Python:
	https://youtu.be/jUxhUgkKAjE?si=d34Z2wrZsVS9Sv1s
	Demo: Concept through PPT + Implementation
44	Python Hyperparameter Tuning Tutorial:
	https://youtu.be/KFost_u85YI?si=fyuMOCELOd_p_PML
	Demo: Concept through PPT + Implementation
	Clustering - Part 1 (K-Means)
	K-Means Clustering in Python:
	https://youtu.be/EItlUEPCIzM?si=L5tyW0WU_WzvK9XN
	Demo: Concept through PPT + Implementation
45	Python K-Means Tutorial:
	https://youtu.be/5w5iUbTlpMQ?si=z9Q8_Skvkt1eYUL6
	Demo: Concept through PPT + Implementation
	Clustering - Part 2 (Hierarchical Clustering)
	Hierarchical Clustering in Python:
	https://youtu.be/lQt92mh0N8I?si=xQq2n-W8bEyNacjr
	Demo: Concept through PPT + Implementation
46	Python Hierarchical Clustering Tutorial:
	https://youtu.be/v7oLMvcxgFY?si=SkzSqIQui3y0N-uB
	Demo: Concept through PPT + Implementation
	Dimensionality Reduction - Part 1 (PCA)
	PCA in Python:
	https://youtu.be/8klqIM9UvAc?si=TKb1YDjFncevYxPk
	Demo: Concept through PPT + Implementation
47	Python PCA Tutorial:
	https://youtu.be/Oi4SJqJIL2E?si=lKCzKYaigZVnwXc9
	Demo: Concept through PPT + Implementation
	Dimensionality Reduction - Part 2 (LDA)
	LDA in Python:
	https://youtu.be/9IDXYHhAfGA?si=7Sn08hiwnBzjLAGG
	Demo: Concept through PPT + Implementation
48	Python LDA Tutorial:
	https://youtu.be/zJ7O8k3VYL8?si=3a2xXaZvGDKFHjWP
	Demo: Concept through PPT + Implementation
	Applications of Clustering
	Clustering Applications in Python:
	https://youtu.be/iNlZ3IU5Ffw?si=fQXYPMflTxjnvvq9
	Demo: Concept through PPT + Implementation
49	Python Clustering Tutorial:
	https://youtu.be/EItlUEPCIzM?si=_IuLm8YkFh_N03GY
	Demo: Concept through PPT + Implementation
	Practical Clustering - Hands-on
	Hands-on Clustering with Python:
	https://youtu.be/pSbHTeXaoXg?si=HwCnpathU_zJjGgp
	Demo: Concept through PPT + Implementation
50	Practical Clustering Tutorial:
	https://youtu.be/lX-3nGHDhQg?si=TQpiUQQANEKkwiQ4
	Demo: Concept through PPT + Implementation
	Introduction to Model Deployment
	Model Deployment in Python:
	https://youtu.be/Mrv3CZNWYEg?si=5j9MnkxFkArL7J9k
	Demo: Concept through PPT + Implementation
51	Python Model Deployment Tutorial:
	https://youtu.be/jMrp6XfYIyk?si=oOnSXeh9eG7O-S1r
	Demo: Concept through PPT + Implementation
	Tools for Model Deployment
	Model Deployment Tools:
	https://youtu.be/tSiS15ubQFQ?si=xTSIXtEZDvYXe9js
	Deploying Models with Docker:
	https://youtu.be/vA0C0k72-b4?si=rEHGOYeqmfp03nNh
	Demo: Concept through PPT + Implementation
	Techniques for Model Deployment
	Model Deployment Techniques:
	https://youtu.be/AWVTKBUnoIg?si=s3B5DsOtskRH7cME
	Model Deployment Best Practices:
	https://youtu.be/Mrv3CZNWYEg?si=qBKj2Q0xxlkogs6E
	Demo: Concept through PPT + Implementation
52	Hands-on Project
	Hands-on Machine Learning Project:
	https://youtu.be/-dK_80wu4xs?si=XQ8RT7inX29qsFbL
	Project Deployment:
	https://youtu.be/goiW0g7A0WE?si=B_JXUYZNBltYJ4cy
	Real-World Applications
	Real-World Machine Learning Applications:
	https://youtu.be/HKcO3-6TYr0?si=zqrbY7hQjHfIrXjO
	Applications in Industry:
	https://youtu.be/f5zBF7Xha90?si=dlmGJj1Rvjf8UCcy
	Demo: Concept through PPT + Implementation

Data Analytics using Tableau (0+2+2) 52 hrs

Lect. No	TOPIC
1	Tableau Tutorial for Beginners | What is Tableau? : https://www.youtube.com/watch?v=NLCzpPRCc7U&pp=ygUzbwlUYWJsZWF1IFR1dG9yaWFsIGZvciBCZWdpbm5lcnMgfCBXaGF0IGlzIFRhYmxlYXU_
	Demo: Concept through PPT + Implementation
2	Getting Started with Tableau | Tableau Software: https://www.youtube.com/watch?v=3GT_TLIeCbE&pp=ygUxbwlHZXR0aW5nIFN0YXJ0ZWQgd2l0aCBUYWJsZWF1IHwgVGFibGVhdSBTb2Z0d2FyZQ%3D%3D
	Demo: Concept through PPT + Implementation
3	Connecting to Data Sources in Tableau: https://youtu.be/vRu_2nZXBPI?si=uv5j0f8syLQZP1b7
	Demo: Concept through PPT + Implementation
4	How to Connect Excel Files to Tableau: https://youtu.be/8pN5lK3-LxA?si=boMyWS_WkuNtgKa_
	Demo: Concept through PPT + Implementation
5	Tableau Data Connection Overview: https://youtu.be/XJqErDNTFRQ?si=GlGzBgmszrzBYbpA
	Demo: Concept through PPT + Implementation
	Connecting Tableau to SQL Database: https://youtu.be/kuDgfwGTeuY?si=vgG7yNU7PPGKU5zR
	Demo: Concept through PPT + Implementation
6	Connecting to Multiple Data Sources in Tableau
	https://youtu.be/RavphFZTqTg?si=8P0ZsnYLQ1Y82cXa
	Demo: Concept through PPT + Implementation
	Data Blending in Tableau https://youtu.be/75HV2OziWGM?si=zF_mTAc0mmCH04Ot
	Demo: Concept through PPT + Implementation
7	Data Visualization Principles: Best Practices in Data Visualization, Types of Visualizations
	Data Visualization Best Practices
	https://youtu.be/OVKbdxF2Czs?si=l_11BNm8tkKm10Xt
	Demo: Concept through PPT + Implementation
	Tips for Better Data Visualizations
	https://youtu.be/t3cAUt7sOQg?si=BjbWsmhKpruzDGfm
	Demo: Concept through PPT + Implementation
8	Types of Data Visualizations
	https://youtu.be/csXmVBw8cdo?si=C1pmHDlnP9BMxav0
	Demo: Concept through PPT + Implementation
	Choosing the Right Chart Type
	https://youtu.be/C07k0euBpr8?si=Z6zUzAZA1fcSBvYW
	Demo: Concept through PPT + Implementation
9	Data Visualization with Tableau
	https://youtu.be/C7VemYmy3rA?si=fZ6uKPsVozy5p8yJ
	Demo: Concept through PPT + Implementation
	Tableau Best Practices for Dashboard Design
	https://www.youtube.com/live/-VENXLh2Lh0?si=MO6g18r43rhwisyq
	Demo: Concept through PPT + Implementation
10	Data Visualization: Effective Design
	https://youtu.be/HuZiJ44_71M?si=oIVRwpexCKmdthd0
	Demo: Concept through PPT + Implementation
	Tableau Visualization Types
	https://youtu.be/J91tW6egjDw?si=gFDNJK_UCKrp_CTX
	Demo: Concept through PPT + Implementation
11	Creating Bar Charts in Tableau
	https://youtu.be/lyNYPMw-ANs?si=M4P5EA65-O0bbA6L
	Demo: Concept through PPT + Implementation
	How to Create Line Charts in Tableau
	https://youtu.be/XNSB3COfIZU?si=VoL23Cs2AmwGGzOU
	Demo: Concept through PPT + Implementation
12	Tableau Pie Chart Tutorial
	https://youtu.be/dLhojoAuiEI?si=kggiTlOdtJPoBJI2
	Demo: Concept through PPT + Implementation
	Creating Scatter Plots in Tableau
	https://youtu.be/WNK0PlUlCRw?si=QowqMj94pcgSTI9J
	Demo: Concept through PPT + Implementation
13	Building Maps in Tableau
	https://youtu.be/r7eRXu9mHns?si=NqCLT8Y7YzSea9-J
	Demo: Concept through PPT + Implementation
	Tableau Map Tutorial for Beginners
	https://youtu.be/fHe0jFgVPJU?si=5670lXYVduD6uheI
	Demo: Concept through PPT + Implementation
14	Customizing Bar Charts in Tableau
	https://youtu.be/x46lB4iPPcA?si=q6lbzxwaroIuF1P2
	Demo: Concept through PPT + Implementation
	Line Chart Formatting in Tableau
	https://youtu.be/XNSB3COfIZU?si=muotdviweYGBfVH2
	Demo: Concept through PPT + Implementation
15	Creating Advanced Pie Charts in Tableau
	https://youtu.be/dLhojoAuiEI?si=0_AIQ1TSgOeDVJw5
	Demo: Concept through PPT + Implementation
	Enhancing Scatter Plots in Tableau
	https://youtu.be/yB3vg9uIZ-E?si=VPtnK07BO_aegqSA
	Demo: Concept through PPT + Implementation
16	Advanced Map Techniques in Tableau
	https://youtu.be/aKyxMDBPXn8?si=pi_-7e8OpefDGEi2
	Demo: Concept through PPT + Implementation
	Creating Geo Maps in Tableau
	https://youtu.be/fHe0jFgVPJU?si=cO8ZUQCgd3F_H70g
	Demo: Concept through PPT + Implementation
17	Creating Heat Maps in Tableau
	https://youtu.be/VVH0l1qytAg?si=54_UgJwWWpaPxBEr
	Demo: Concept through PPT + Implementation
	Advanced Heat Map Techniques
	https://youtu.be/nHgLJ_SKkNQ?si=eVXW85-aLm2Vup00
	Demo: Concept through PPT + Implementation
18	Creating Tree Maps in Tableau
	https://youtu.be/4Sx3VQg7LgI?si=vuOF_frCbRQPvpOk
	Demo: Concept through PPT + Implementation
	Advanced Tree Map Techniques
	https://youtu.be/50vqQrid1mQ?si=_YcTXtPdXjBgOvwY
	Demo: Concept through PPT + Implementation
19	Creating Bullet Charts in Tableau
	https://youtu.be/zGpfxdArx-o?si=BBCb4Djx-Cq4EQ0r
	Demo: Concept through PPT + Implementation
20	Bullet Charts for KPI in Tableau
	https://youtu.be/1mx78l8Uw3E?si=8DVxs3Z5LV-52z0K
	Demo: Concept through PPT + Implementation
21	Creating Histograms in Tableau
	https://youtu.be/THJcN4qr00U?si=sNNh0jkYJqo9AqS2
	Demo: Concept through PPT + Implementation
22	Advanced Histogram Techniques
	https://youtu.be/lUjZa4YBixI?si=WKk2YXCXslZoDn7k
	Demo: Concept through PPT + Implementation
23	Creating Box Plots in Tableau
	https://youtu.be/_NzllWQDuBY?si=8pH59vVLw123gNwJ
	Demo: Concept through PPT + Implementation
24	Advanced Box Plot Techniques
	https://youtu.be/nV8jR8M8C74?si=QzzLDR2HwGd499cF
	Demo: Concept through PPT + Implementation
25	Combining Advanced Visualizations in Tableau
	https://youtu.be/x46lB4iPPcA?si=Xtj7_GZMnk0vNwVR
	Demo: Concept through PPT + Implementation
26	Best Practices for Advanced Visualizations
	https://youtu.be/ZUeWXNK-9yA?si=fEelTOiMjMqNc6vr
	Demo: Concept through PPT + Implementation
27	Creating Calculated Fields in Tableau
	https://youtu.be/jp_enyU__4Y?si=Ra2X9uNIvcZLA50z
	Demo: Concept through PPT + Implementation
28	Advanced Calculated Fields Techniques
	https://youtu.be/b-K1jUY3jDs?si=mP-KjU9Asud2__KP
	Demo: Concept through PPT + Implementation
29	Introduction to Table Calculations in Tableau
	https://youtu.be/2f7Sl5WEExw?si=ofP-k3eU7Lqbtpb3
	Demo: Concept through PPT + Implementation
30	Advanced Table Calculations in Tableau
	https://youtu.be/XPYtenDXCdI?si=3RDFFUXtSokC0Vrk
	Demo: Concept through PPT + Implementation
31	Using Level of Detail (LOD) Expressions in Tableau
	https://youtu.be/X-fMb2g0Oho?si=LE-P5b69ruOimYbd
	Demo: Concept through PPT + Implementation
32	Advanced LOD Expressions in Tableau
	https://youtu.be/X-fMb2g0Oho?si=oK2ZjkYtNBEVEhPy
	Demo: Concept through PPT + Implementation
33	Combining Calculated Fields and Table Calculations
	https://youtu.be/jp_enyU__4Y?si=2xDpL2dwMSZQk9bh
	Demo: Concept through PPT + Implementation
34	Best Practices for Calculations in Tableau
	https://youtu.be/jp_enyU__4Y?si=HQZbo_4l9SYsaIrY
	Demo: Concept through PPT + Implementation
35	Using Nested Calculations in Tableau
	https://youtu.be/mu-4m9-NH6o?si=mSYVy6ceEkBlDaQa
	Demo: Concept through PPT + Implementation
36	Dynamic Calculations with Parameters
	https://youtu.be/ETP5X0OWNi8?si=wf_s6SVtMSgMEr66
	Demo: Concept through PPT + Implementation
37	Combining LOD Expressions and Table Calculations
	https://youtu.be/gLYV1Wy4VKk?si=ZeICRNzxQ2bNZvXK
	Demo: Concept through PPT + Implementation
38	Advanced Dynamic Calculations
	https://youtu.be/ead__ySyefo?si=sKOxfW8-EyNjcvn_
	Demo: Concept through PPT + Implementation
39	Creating Dashboards in Tableau
	https://youtu.be/6oFTdbrugUs?si=iZXvxcaNG0gloSa5
	Demo: Concept through PPT + Implementation
40	Dashboard Design Best Practices
	https://youtu.be/t3cAUt7sOQg?si=X6Phyv9bl4917dVD
	Demo: Concept through PPT + Implementation
41	Dashboard Interactivity in Tableau
	https://youtu.be/Aql27yGqHkE?si=SBcGTJIV7Jm6_TF0
	Demo: Concept through PPT + Implementation
42	Interactive Dashboards with Filters
	https://youtu.be/MTlQvyNQ3PM?si=PNZ_7jsgbq8M9scf
	Demo: Concept through PPT + Implementation
43	Storytelling with Data in Tableau
	https://youtu.be/9IIR7-09rz0?si=Ahkxd9HDwuubL8Sz
	Demo: Concept through PPT + Implementation
44	Creating Data Stories in Tableau
	https://youtu.be/K7sGGWtZpZc?si=3_PmFfKJsyLNhPFh
	Demo: Concept through PPT + Implementation
45	Advanced Dashboard Design Techniques
	https://youtu.be/t3cAUt7sOQg?si=JlvT9-AREhIf48Ux
	Demo: Concept through PPT + Implementation
46	Creating Professional Dashboards in Tableau
	https://youtu.be/oAIubTqg-Kw?si=dWQTU0WMoWGD5t9-
47	Using Actions for Dashboard Interactivity
	https://youtu.be/BnYCwgX7hZQ?si=JgPjaEvdiX7uvbl9
	Demo: Concept through PPT + Implementation
48	Dashboard Design Tips and Tricks
	https://youtu.be/nCrD5g8d3ow?si=blRsGWpfUe_MYHTX
	Demo: Concept through PPT + Implementation
49	Using Parameters and Filters in Tableau
	https://youtu.be/y5P90Qme13k?si=L7qAmUNpER27xftr
	Demo: Concept through PPT + Implementation
	Advanced Parameter Usage in Tableau
	https://youtu.be/sa2VlsLk98k?si=BDFoyFTMFEoHi4fo
	Demo: Concept through PPT + Implementation
50	Forecasting in Tableau
	https://youtu.be/JdkWZN-7JYk?si=dz58B5xuQWVHp7F0
	Advanced Forecasting Techniques
	https://youtu.be/gCiUcpnR3p4?si=JKFS6ZILtj1PIZVq
	Demo: Concept through PPT + Implementation
	Cohort Analysis in Tableau
	https://www.youtube.com/live/hnt167n_bdE?si=nl64T5fdh04Oa99x
	Advanced Cohort Analysis
	https://youtu.be/vbg4Je1tuis?si=p7K6qu4TT-l_7zL0
	Demo: Concept through PPT + Implementation
51	Sharing Visualizations with Tableau Public
	https://youtu.be/UM-bYKrHVbo?si=Hb1fFHGZRYBJmhff
	Demo: Concept through PPT + Implementation
52	Tableau Case Study: Sales Analysis
	https://youtu.be/_qReGTOrKTk?si=JlsnvTdppRKqgzwVDemo: Concept through PPT + Implementation
	Real-World Application: Financial Dashboard
	https://youtu.be/jeYjtEX3RAE?si=xuMOdSyS1fSfKKKY
	Demo: Concept through PPT + Implementation

ML for Image Analytics (0-4-2) 86hrsSession-1,2,3
Accessing individual pixels using matrix in imagehttps://www.youtube.com/watch?v=j-ZLDEnhT3QSession-4,5,6
Image resize and splitting of imagehttps://www.youtube.com/watch?v=cWHW9MnX_F4Session-7,8,9,10
Grey scale conversion and mathematical implementationhttps://www.youtube.com/watch?v=sTxGKae0FckSession-11,12,13
Colour channel splittinghttps://www.youtube.com/watch?v=PHIyhdcLsL8Session-14,15,16,17
Histogram equalisation (CLACH)https://www.youtube.com/watch?v=jWShMEhMZI4Session-18,19,20,21
Edge detection (Sobel, Canny)https://www.youtube.com/watch?v=h8Yp3M8SX2MSession-22,23,24
Morphological operationshttps://www.youtube.com/watch?v=xSzsD4kXhRwSession-25,26,27,28
Image segmentationhttps://www.youtube.com/watch?v=kIVk0IhDMwYSession-29,30,31
Image Thresholdinghttps://www.youtube.com/watch?v=Zf1F4cz8GHUSession-32,33,34
Binary conversion of segmented imagehttps://www.youtube.com/watch?v=X4icNTQSCssSession-35,36,37,38
Cluster based segmentationhttps://www.youtube.com/watch?v=6CqRnx6Ic48Session-39,40,41,42
Feature extraction based on size, shape and colourhttps://www.youtube.com/watch?v=IG3UkAqHnQISession-43,44,45,46
Feature extraction using predefined functions: SIFT, SURF, STAR, ORBhttps://www.youtube.com/watch?v=USl5BHFq2H4Session-47,48,49,50
Feature Extraction using convolutional neural network (CNN).https://www.youtube.com/watch?v=Q21fWIdHGFMhttps://www.youtube.com/watch?v=WvoLTXIjBYUSession-51,52,53,54
Matrix flattening in Pythonhttps://www.youtube.com/watch?v=6XLXmzv6ajQSession-55,56,57
Horizontal stacking and Vertical stackinghttps://www.youtube.com/watch?v=ksqPbrS-b78Session-58,59,60,61
Creation of Feature matrix by padding required number of zeros and ones.https://www.youtube.com/watch?v=94ZmuGhuBIkSession-62,63,64
Splitting the feature matrix (training/testing)https://www.youtube.com/watch?v=fwY9Qv96DJYSession-65,66,67
Labelling the datasetshttps://www.youtube.com/watch?v=V2e0cygY9VgSession-68,69,70
Support vector machine (SVM) in image analysis.https://www.youtube.com/watch?v=Y6RRHw9uN9oSession-71,72,73,74
Different kernels of SVM (linear, polynomial, radial basis function)https://www.youtube.com/results?search_query=Different+kernels+of+SVM+%28linear%2C+polynomial%2C+radial+basis+function%29Session-75,76,77
Gradient Boosting (GB)https://www.youtube.com/watch?v=jxuNLH5dXCsSession-78, 79, 80
Multi-layer Perceptron (MLP)https://www.youtube.com/watch?v=AZEfmoWBXwgSession-881,82,83
Deep learning for Image classification using CNNhttps://www.youtube.com/watch?v=AACPaoDsd50Session-84,85,86
Deep learning for Image classification using  RNNhttps://www.youtube.com/watch?v=iMIWee_PXl8ML for Hyperspectral Imaging (0-4-2) 86 hrsSession-1,2,3
Multi-Spectral Imagery (MSI)https://www.youtube.com/watch?v=b0webdvlySoSession-4,5,6
Hyperspectral Imagery (HSI)https://www.youtube.com/watch?v=e6hbmSfPnMQSession-7,8,9
Hyperspectral remote sensing and its applicationshttps://www.youtube.com/watch?v=2LNXeUS25VwSession-10,11,12,13
Physics of imaging spectroscopyhttps://www.youtube.com/watch?v=PMk85e90p6sSession-14,15,16
Electromagnetic propagationhttps://www.youtube.com/watch?v=lTjSdnEcJV8Session-17,18,19
Sensor physicshttps://www.youtube.com/watch?v=3iaFzafWJQESession-20,21,22,23
Atmospheric Correctionshttps://www.youtube.com/watch?v=4VVxuQI5yqQSession-24,25,26
Signal-to-Noise ratio (SNR)https://www.youtube.com/watch?v=ZOchEgeuobMSession-27,28,29
Spectral resolutionhttps://www.youtube.com/watch?v=Hu1T_rEb7D0Session-30,31,32,33
Image sampling and quantization in digital image processinghttps://www.youtube.com/watch?v=0_255tTnhLQSession-34,35,36,37
Spectral angle mapping using different methodshttps://www.youtube.com/watch?v=xq8bOmWQXqUSession-38,39,40,41
Principal Component Analysis (PCA) in HSI Imaginghttps://www.youtube.com/watch?v=VaiolMYETmESession-42,43,44,45
Minimum Noise Fraction (MNF) using different methodshttps://www.youtube.com/watch?v=6sEx9cUvLtwSession-46,47,48,49
Spectral feature fittinghttps://www.youtube.com/watch?v=5hX7kU52cEUSession-55,51,52,53
Support Vector Machine (SVM) for HSIhttps://www.youtube.com/watch?v=F5C6FexFfjESession-54,55,56,57
Partial Least Squares Regression (PLSR)https://www.youtube.com/watch?v=WKEGhyFx0DgSession-58,59,60,62
Spectral Clustering  using Pythonhttps://www.youtube.com/watch?v=Z10BXWPFnasSession-58,59,60,61
K-mean clustering in hyperspectral imaginghttps://www.neonscience.org/classification-kmeans-pca-python#:~:text=KMeans%20is%20an%20iterative%20clustering,to%20classify%20unsupervised%20data%20(eg.&text=Each%20pixel%20in%20the%20image,pixels%20assigned%20to%20the%20cluster.Session-62,63,64,65
Hyperspectral image classification using multiple spectral and spatial featureshttps://www.youtube.com/watch?v=vxh53StWaUkSession-67,68,69,70
Models and Algorithms for Hyperspectral Image Processinghttps://www.youtube.com/watch?v=RZu1LHumbiQSession-71,72,73,74
Applied Hyperspectral Imaging Fundamentals and Case Studieshttps://www.youtube.com/watch?v=Z_Zub7wJTFsSession-75,76,77,78
Classification For Hyperspectral Remote Sensing Imaging Using Neural Networkhttps://www.youtube.com/watch?v=Co2Dw-HRFw8Session-79,80,81,82
Hyperspectral image classification using Deep learning and CNNhttps://www.youtube.com/watch?v=7pdOBBrRqIQSession-83,84,85,86
Case study-Implements dimensionality reduction on hyper spectral image(Indian Pines) with classification.Dimensionality-reduction-and-classification-on-Hyperspectral-Images-Using-PythonSession-83,84,85,86
Case study-Implements dimensionality reduction on hyper spectral image(Indian Pines) with classification.Dimensionality-reduction-and-classification-on-Hyperspectral-Images-Using-PythonIoT Analytics (0+2+2) -60 hrs
Session- 1


Practice 1:

Creating Things, Certificates, Policies in AWS IoT core Services

Practice 2:

Connect NodeMCU with AWS IoT Core Services

Practice 3:

Connect ESP32 with AWS IoT Core Services

Practice 4:

Connect Raspberry Pi with AWS IoT Core Services

Practice 5:

Posting Sensor Data to AWS IoT Core Services

Practice 6:

Controlling Devices from AWS IoT Core Services

Practice 7:

Storing Sensor Data into DynamoDB using AWS IoT core

Practice 8:

Get Raspberry Pi to interact with Amazon Web Services & push data into the DynamoDB

Practice 9:

Posting Sensor Data to the Thingspeak to aggregate, visualize and analyze live data streams in the cloud

Practice 10:

Portable IoT Based Fingerprint Biometric Attendance System

Practice 11:

IoT-based Covid Patient Blood Oxygen monitor & calling an ambulance on critical blood oxygen levels
Project (CUML2006)- (0+0+4)

• IoT based Water Management
• IoT Disease and Pest Management in Smart Agriculture
• Soil Health Monitoring
• IoT based Apparel Tracking
• Intruder Tracking System

Gate Process for ProjectGate 0:       Problem Identification
Gate 1:       Data Collection
Gate 2:       Model Development
Gate 3:       Testing and Validation
Gate 4:       Publication, Patent, Product

COs	Course Outcomes
CO1	Students will gain comprehensive knowledge of predictive analytics, including key concepts, methodologies, and applications.
CO2	Students will develop strong analytical skills and the ability to critically assess and interpret data for predictive modeling.
CO3	Students will enhance their problem-solving skills and make informed decisions based on predictive analysis results.
CO4	Students will gain hands-on experience with various machine learning tools and techniques, designing and developing predictive models.
CO5	Students will be able to conduct research in predictive analytics, utilizing advanced techniques and methodologies to solve complex problems.
Introduction to Predictive Analytics with Python:  https://youtu.be/tdV9L3C-hxQ?si=NY0HHcWFLy9k5cuN Demo: Concept through PPT + Implementation

Course Materials

• bigstock-Discrete-Maths-Glyph-Color-Ico-255022318-Converted-01-1-1024x1024
• aiexcerpt