Machine Learning using Python

per person /

Free

Home Courses

Machine Learning using Python

Machine Learning using Python

Teacher

Dr. Sujata Chakravarty

Category

Core Courses

Course Attendees

Still no participant

Course Reviews

Still no reviews

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.

Learning Outcomes

  • Students will able to Create and incorporate ML solutions in their respective fields of study.

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

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

Recent Comments

    Our Main Teachers

    Dr. Sujata Chakravarty

    HoD & Associate Professor, Department of CSE, SoET
    VIEW PROFILE

    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 […]