Machine Learning Using Python Programming

By Dr. Manjari Gupta, Dr. Manoj Kumar Mishra | Institute of Science, Banaras Hindu University

Learners enrolled: 6143 | Exam registration: 749

ABOUT THE COURSE:

Machine learning is being applied to address a wide array of challenges across various domains. In today's data-driven world, where vast amounts of data are generated in nearly every field, the ability to automatically analyze this data is crucial for improving business strategies, forecasting the future, and advancing humanity. This course will cover essential topics such as data pre-processing, regression, classification, dimensionality reduction, clustering techniques & association rule mining along with guidance on when and how to apply them. Upon completing the course, students will be equipped to assess whether machine learning is the right approach for solving a given problem and, if so, select and implement the appropriate techniques.

INTENDED AUDIENCE: UG, PG, PhD & Professional

PREREQUISITES: 10+2+Graduation with Mathematics/Computer science and basic understanding of Python Programming.

INDUSTRY SUPPORT: All Top industries are working using this technology including Google, Microsoft, IBM and Facebook etc

Summary

Course Status :	Ongoing
Course Type :	Elective
Language for course content :	English
Duration :	12 weeks
Category :	Computer Science
Credit Points :	3
Level :	Undergraduate/Postgraduate
Start Date :	20 Jan 2025
End Date :	11 Apr 2025
Enrollment Ends :	03 Feb 2025
Exam Registration Ends :	28 Feb 2025
Exam Date :	25 Apr 2025 IST

Note: This exam date is subject to change based on seat availability. You can check final exam date on your hall ticket.

Page Visits

Course layout

Week 1: Introduction to Machine learning (ML), Types of Machine Learning, Basic of Python Programming.

Week 2: Types of Data, General Characteristics of dataset, Data Pre-processing: Feature scaling, One Hot encoding, outlier detection, missing data handling & its implementation.

Week 3: Exploratory Data Analysis, Evaluation Measures, Measures of similarity and Dissimilarity, K-Fold Cross Validation.

Week 4: ROC curve, Simple Linear Regression, Gradient Descent & its implementation.

Week 5: Multiple Linear Regression, Polynomial Linear Regression & its implementation.

Week 6: Decision Trees & K-Nearest Neighbours for regression analysis, Regularization: Ridge Regression, Lasso Regression, Elastic Net Regression, Implementation of these techniques.

Week 7: Decision Trees: attribute selection measures, stopping criteria, pruning & it implementation.

Week 8: Occam’s Razor, Pessimistic error, K-Nearest Neighbours, Logistic Regression & it implementation.

Week 9: Naïve Bayes Classification, Support Vector Machine (SVM), kernel SVM, implementation of these techniques.

Week 10: Ensemble learning: Random Forest, Bagging, Boosting, Model election & its implementation of these techniques.

Week 11: Clustering: K-means & Hierarchical clustering, Dendrogram, implementation of these techniques,

Week 12: Fuzzy C-means, Association Rule Mining: Frequent Patterns, Apriori Algorithm, Dimensionality reduction: PCA & Its implementation

Books and references

• Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Geron Aurelien

• Introduction to Machine Learning with Python by Andreas C. Muller & Sarah Guido

• Machine Learning in Data Science using Python by R Nageswara Rao

• Christopher M. bishop, Pattern recognition and Machine Learning, Springer

• Pang-ning Tan, Michael Steinbach, Vipin Kumar, Introduction to Data Mining,

• Jiawei Han, Micheline Kamber, Jian Pei, Data Mining Concepts and Techniques

• Bing liu, Web Data Mining

Instructor bio

Dr. Manjari Gupta

Institute of Science, Banaras Hindu University

Prof. Manjari Gupta (Professor) has been serving as Professor in Department of Computer Science. She has joined Banaras Hindu University in the year of 2007. Before that she taught at IIIT Allahabad from 2006 to 2007. She received her PhD degree in Computer Science and Engineering from IIT BHU. Her research interests are detecting software code smells and design patterns, Neurological and Heart Diseases prediction using Artificial intelligence/Machine learning/Deep Learning.

Dr. Manoj Kumar Mishra

Dr. Manoj Kumar Mishra is an Assistant Professor at Banaras Hindu University (BHU), where he has been serving since 2017. Prior to this, he taught at Christ (Deemed to be University), Bangalore, from 2015 to 2017. Dr. Mishra completed his doctoral studies in the Department of Computer Science & Engineering at IIT (ISM) Dhanbad. His teaching expertise spans subjects such as Machine Learning, Automata Theory, Data Structures and Algorithms, Compiler Design, Deep Learning, Cryptography, and Image Processing.

Course certificate

The course is free to enroll and learn from. But if you want a certificate, you have to register and write the proctored exam conducted by us in person at any of the designated exam centres.

The exam is optional for a fee of Rs 1000/- (Rupees one thousand only).

Date and Time of Exams: April 25, 2025 Morning session 9am to 12 noon; Afternoon Session 2pm to 5pm.

Registration url: Announcements will be made when the registration form is open for registrations.

The online registration form has to be filled and the certification exam fee needs to be paid. More details will be made available when the exam registration form is published. If there are any changes, it will be mentioned then.

Please check the form for more details on the cities where the exams will be held, the conditions you agree to when you fill the form etc.

CRITERIA TO GET A CERTIFICATE

Average assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.
Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

Please note that assignments encompass all types (including quizzes, programming tasks, and essay submissions) available in the specific week.

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

Certificate will have your name, photograph and the score in the final exam with the breakup.It will have the logos of INI and BHU .

Only the e-certificate will be made available. Hard copies will not be dispatched.

Once again, thanks for your interest in our online courses and certification. Happy learning.

- INI team