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Domain Track: Data Science and Machine Learning

Domain Track: Data Science and Machine Learning

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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:

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 challenges7.2  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                    HTTP7.2  Sensors        Types of Sensors based on communication-I2C, SPI        Types of Sensors based on Application7.3  Overview of 32 -bit Controller         ESP8266         ESP32         Raspberry Pi7.4  AWS IoT for Cloud         AWS IoT Core services         AWS IoT Analytics services         AWS DynamoDB Services7.5  Thingspeak for IoT         Getting and posting Data to IoT Cloud using ESP devices         Posting Data to IoT Cloud using Raspberry Pi7.6  ThingWorx for Industrial IoT         Building Dashboard on Thingworx platform         Binding the senor value to the dashboardText 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 3

Practice 

Environmental setup - Anaconad and Jupyter notebook, Anaconda Navigator and Libraries Installation

https://www.youtube.com/watch?v=beh7GE4FdnM

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 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 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

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

Session 40 & 41

 

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

ML for Predictive Analysis (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
Demo: Concept through PPT + Implementation
2 Understanding Predictive Analytics:
 https://youtu.be/tdV9L3C-hxQ?si=Gt6TSJxq3F6VkY1Z
Demo: Concept through PPT + Implementation
3 Applications of Predictive Analytics
Predictive Analytics in Finance with Python: 
https://youtu.be/tdV9L3C-hxQ?si=troHVzFHg3CKuqHA
Demo: Concept through PPT + Implementation
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) 86hrs

Session-1,2,3
Accessing individual pixels using matrix in imagehttps://www.youtube.com/watch?v=j-ZLDEnhT3Q

Session-4,5,6
Image resize and splitting of imagehttps://www.youtube.com/watch?v=cWHW9MnX_F4

Session-7,8,9,10
Grey scale conversion and mathematical implementationhttps://www.youtube.com/watch?v=sTxGKae0Fck

Session-11,12,13
Colour channel splittinghttps://www.youtube.com/watch?v=PHIyhdcLsL8

Session-14,15,16,17
Histogram equalisation (CLACH)https://www.youtube.com/watch?v=jWShMEhMZI4

Session-18,19,20,21
Edge detection (Sobel, Canny)https://www.youtube.com/watch?v=h8Yp3M8SX2M

Session-22,23,24
Morphological operationshttps://www.youtube.com/watch?v=xSzsD4kXhRw

Session-25,26,27,28
Image segmentationhttps://www.youtube.com/watch?v=kIVk0IhDMwY

Session-29,30,31
Image Thresholdinghttps://www.youtube.com/watch?v=Zf1F4cz8GHU

Session-32,33,34
Binary conversion of segmented imagehttps://www.youtube.com/watch?v=X4icNTQSCss

Session-35,36,37,38
Cluster based segmentationhttps://www.youtube.com/watch?v=6CqRnx6Ic48

Session-39,40,41,42
Feature extraction based on size, shape and colourhttps://www.youtube.com/watch?v=IG3UkAqHnQI

Session-43,44,45,46

Feature extraction using predefined functions: SIFT, SURF, STAR, ORBhttps://www.youtube.com/watch?v=USl5BHFq2H4

Session-47,48,49,50
Feature Extraction using convolutional neural network (CNN).https://www.youtube.com/watch?v=Q21fWIdHGFMhttps://www.youtube.com/watch?v=WvoLTXIjBYU

Session-51,52,53,54
Matrix flattening in Pythonhttps://www.youtube.com/watch?v=6XLXmzv6ajQ

Session-55,56,57
Horizontal stacking and Vertical stackinghttps://www.youtube.com/watch?v=ksqPbrS-b78

Session-58,59,60,61
Creation of Feature matrix by padding required number of zeros and ones.https://www.youtube.com/watch?v=94ZmuGhuBIk

Session-62,63,64
Splitting the feature matrix (training/testing)https://www.youtube.com/watch?v=fwY9Qv96DJY

Session-65,66,67
Labelling the datasetshttps://www.youtube.com/watch?v=V2e0cygY9Vg

Session-68,69,70
Support vector machine (SVM) in image analysis.https://www.youtube.com/watch?v=Y6RRHw9uN9o

Session-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%29

Session-75,76,77
Gradient Boosting (GB)https://www.youtube.com/watch?v=jxuNLH5dXCs

Session-78, 79, 80
Multi-layer Perceptron (MLP)https://www.youtube.com/watch?v=AZEfmoWBXwg

Session-881,82,83
Deep learning for Image classification using CNNhttps://www.youtube.com/watch?v=AACPaoDsd50

Session-84,85,86
Deep learning for Image classification using  RNNhttps://www.youtube.com/watch?v=iMIWee_PXl8

ML for Hyperspectral Imaging (0-4-2) 86 hrs

Session-1,2,3
Multi-Spectral Imagery (MSI)https://www.youtube.com/watch?v=b0webdvlySo

Session-4,5,6
Hyperspectral Imagery (HSI)https://www.youtube.com/watch?v=e6hbmSfPnMQ

Session-7,8,9
Hyperspectral remote sensing and its applicationshttps://www.youtube.com/watch?v=2LNXeUS25Vw

Session-10,11,12,13
Physics of imaging spectroscopyhttps://www.youtube.com/watch?v=PMk85e90p6s

Session-14,15,16
Electromagnetic propagationhttps://www.youtube.com/watch?v=lTjSdnEcJV8

Session-17,18,19
Sensor physicshttps://www.youtube.com/watch?v=3iaFzafWJQE

Session-20,21,22,23
Atmospheric Correctionshttps://www.youtube.com/watch?v=4VVxuQI5yqQ

Session-24,25,26
Signal-to-Noise ratio (SNR)https://www.youtube.com/watch?v=ZOchEgeuobM

Session-27,28,29
Spectral resolutionhttps://www.youtube.com/watch?v=Hu1T_rEb7D0

Session-30,31,32,33
Image sampling and quantization in digital image processinghttps://www.youtube.com/watch?v=0_255tTnhLQ

Session-34,35,36,37
Spectral angle mapping using different methodshttps://www.youtube.com/watch?v=xq8bOmWQXqU

Session-38,39,40,41
Principal Component Analysis (PCA) in HSI Imaginghttps://www.youtube.com/watch?v=VaiolMYETmE

Session-42,43,44,45
Minimum Noise Fraction (MNF) using different methodshttps://www.youtube.com/watch?v=6sEx9cUvLtw

Session-46,47,48,49
Spectral feature fittinghttps://www.youtube.com/watch?v=5hX7kU52cEU

Session-55,51,52,53
Support Vector Machine (SVM) for HSIhttps://www.youtube.com/watch?v=F5C6FexFfjE

Session-54,55,56,57
Partial Least Squares Regression (PLSR)https://www.youtube.com/watch?v=WKEGhyFx0Dg

Session-58,59,60,62

Spectral Clustering  using Pythonhttps://www.youtube.com/watch?v=Z10BXWPFnas

Session-62,63,64,65
Hyperspectral image classification using multiple spectral and spatial featureshttps://www.youtube.com/watch?v=vxh53StWaUk

Session-67,68,69,70
Models and Algorithms for Hyperspectral Image Processinghttps://www.youtube.com/watch?v=RZu1LHumbiQ

Session-71,72,73,74

Applied Hyperspectral Imaging Fundamentals and Case Studieshttps://www.youtube.com/watch?v=Z_Zub7wJTFs

Session-75,76,77,78
Classification For Hyperspectral Remote Sensing Imaging Using Neural Networkhttps://www.youtube.com/watch?v=Co2Dw-HRFw8

Session-79,80,81,82
Hyperspectral image classification using Deep learning and CNNhttps://www.youtube.com/watch?v=7pdOBBrRqIQ

Session-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-Python

Session-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-Python

IoT Analytics (0+2+2) -60 hrs

Session- 1
Practice 1:        Creating Things, Certificates, Policies in AWS IoT core ServicesPractice 2:        Connect NodeMCU with AWS IoT Core ServicesPractice 3:        Connect ESP32 with AWS IoT Core ServicesPractice 4:        Connect Raspberry Pi with AWS IoT Core ServicesPractice 5:        Posting Sensor Data to AWS IoT Core ServicesPractice 6:         Controlling Devices from AWS IoT Core ServicesPractice 7:         Storing Sensor Data into DynamoDB using AWS IoT corePractice 8:         Get Raspberry Pi to interact with Amazon Web Services & push data into the DynamoDBPractice 9:         Posting Sensor Data to the Thingspeak to aggregate, visualize and analyze live data streams in the cloudPractice 10:         Portable IoT Based Fingerprint Biometric Attendance SystemPractice 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 Project

Gate 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

Our Main Teachers

Dr. Sujata Chakravarty

HoD & Associate Professor, Department of CSE, SoET
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Dr. Sujata Chakravarty is a Senior Member of IEEE. Her research area includes multidisciplinary fields like Application of Computational Intelligence and Evolutionary Computing Techniques in the field of Financial Engineering, Bio-medical data classification, Smart Agriculture, Intrusion Detection System in Computer-Network, Analysis and prediction of different financial time series data. She is a reviewer of many […]

MANOJ KUMAR BEHERA

Asst. Prof. Dept of CSE
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Manoj Kumar Behera, M. tech. in Computer Science, NIT Rourkela, Qualified GATE in 2008. His research area includes application of machine learning and image processing in the fields of smart agriculture and Bio-medical applications. He has published about 20 articles in many international journals and conferences.