Machine Learning using Python

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Machine Learning using Python

Machine Learning using Python

Teacher

Dr. Sujata Chakravarty

Category

Core Courses

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Course Name : Machine Learning using Python

Code(Credit) : CUTM1019(1-2-1)

Course Objectives

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

  • Explore important packages of python, such as numpy, scipy, OpenCV and scikit-learn.

Course Outcomes

 

COs

 

Course outcomes

 

Mapping COs with POs (High-3, Medium-2, Low- 1)

 

CO1

 

Able to gain knowledge on ML solutions in their respective fields of study

 

PO1 (3)

 

CO2

 

Able to analyze several problems and apply ML techniques to solve it

 

PO2(3)

 

CO3

 

Ability    to    design    prediction    and classification models

 

PO3 (3)

Course outcome

CO1: Able to gain knowledge on ML solutions in their respective fields of study: PO1

CO2: Able to analyze several problems and apply ML techniques to solve it: PO2

CO3: Ability to design prediction and classification models: PO3

PO1: Engineering knowledge: Apply knowledge of mathematics, science, Engineering fundamentals, and electronics engineering to the solution of engineering problems.

PO2: Problem analysis: Identify, formulate, review literature and analyze Computer Science and Engineering problems to design, conduct experiments, analyze data and interpret data.

PO3: Design /development of solutions: Design solution for Computer Science and Engineering problems and design system component of processes that meet the desired needs with appropriate consideration for the public health and safety, and the cultural, societal and the environmental considerations.

Course Syllabus

Module 1 – Application and Environmental-setup (12 hrs)

  • 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.

Module 2 - Regression (8 hrs)

  • 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.

Module 3 - Classification (24 hrs)

  • 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.

Module 4 - Unsupervised Learning (12 hrs)

  • 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

practical

Practical 1: Introduction to Numpy module

Practical 2: Introduction to Pandas module

Practical 3: Simple Linear regression: Predict the sepal length (cm) of the iris flowers

Practical 4: Implementation of Non Linear regression using IRIS

Practical 5: Implementation of Multiple linear regression using IRIS

Practical 6: Comparison between Linear and Non linear regression

Practical 7: Implementation of  PCA for feature reduction

Practical 8: Implementation of k-Nearest Neighbour algorithm using IRIS

Practical 9: Implementation of  Tree construction using Decision tree Classifier using IRIS

Practical 10: Implementation of  Finding Accuracy using Decision tree using IRIS

Practical 11: Implementation of  SVM Classification using Binary class

Practical 12: Implementation of  SVM Classification using multiclass using IRIS

Practical 13: Implementation of  Evaluation metrics

Practical 14: Implementation of  KMeans

Session Plan

Session-4,5

 

Understanding the problem and its possible solutions using IRIS datasets.

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

session-10,11

 

ML library in Python scikit-learn and its  functions.

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

https://scikit-learn.org/stable/tutorial/basic/tutorial.html

Session-12

 

Defining student specific Project

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-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-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-49,50

 

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

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

Session-53,54

 

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

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

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 […]

G.Rama Devi

Assistant Professor
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G RAMA DEVI ,MCA,M.TECH(CSE),(Ph.d) working as Assistant professor, Department of CSE, Centurion University of Technology and Management, Andhra Pradesh. Interested to work on Data Structures, Python, Machine Learning, Database Management Systems and Web Development, Problem Solving Methodologies. Programming Skill: Data structures, C programming, python, Web development (Html, CSS,JS), Database Management Systems.

Dr. Konga Upendar

Assistant Professor
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