A Basic Course in Machine Learning for All

By Sumitra Padmanabhan | uGDX School of Technology, ATLAS SkillTech University, Mumbai

Learners enrolled: 15773

Foundation of Machine Learning is a beginner-friendly course specifically designed for individuals with no engineering background. This course aims to introduce non-engineers to the core concepts of machine learning in a simple and accessible manner. Students will learn the basics of machine learning, focusing on practical applications and real-world scenarios.

To gain foundational knowledge in data science and machine learning, including understanding types of data, basic principles of machine learning algorithms, and their applications. Understand basic concepts of data and machine learning
To develop the skill of translating real-world challenges into machine learning problems, suitable for different applications across various industries. Apply user-friendly tools to visualize data and develop machine learning models to solve simple real world problems
To acquire practical skills in using accessible tools for data visualization and to build basic machine learning models for solving simple real-world problems.
Assess and reflect on the societal, ethical, and legal consequences linked with AI and machine learning technologies.

Indicative Industry Sectors :

Others

Note: This is an indicative list based on suggestions provided by course coordinators and should not be treated as final and binding.

Indicative Program Alignments :

M.B.A.

Summary

Course Status :	Ongoing
Course Type :	Core
Language for course content :	English
Duration :	8 weeks
Category :	Management Studies
Level :	Postgraduate
Start Date :	15 Jul 2025
End Date :	31 Oct 2025
Enrollment Ends :	31 Aug 2025
Exam Date :
Translation Languages :	English
NCrF Level	6.5
Industry Details :	Technology (High-tech)

Page Visits

Course layout

Week 1: Introduction to Machine Learning

Week 2: Data and its importance

Week 3: Supervised Learning

Week 4: Unsupervised Learning

Week 5: Evaluation and Interpretation

Week 6: Future Trends and Responsible AI

Week 7: Final Project

Week 8: Building Machine Learning Models

Books and references

"Machine Learning for Dummies" by John Paul Mueller and Luca Massaron.
"The Hundred-Page Machine Learning Book" by Andriy Burkov.

Instructor bio

Sumitra Padmanabhan

uGDX School of Technology, ATLAS SkillTech University, Mumbai

Dr. Sumitra Padmanabhan is a passionate academician and believes in integrating the "learning through reflection on doing" approach into academics. She has more than 20 years of experience as a teacher/ mentor in a Technology college. At present she is working as an Associate Professor at uGDX School of Technology, ATLAS SkillTech University, Mumbai. She received her Ph.D. from the Nirma Institute of Technology, Ahmedabad, in 2022. Her research approach combined the emerging data science methods along with physics of equatorial ionosphere and her main research interests are in applying Machine learning, algorithms and Data Science techniques to Earth and atmospheric science problems. She has developed Machine learning algorithms to forecast the behaviour of ionospheric total electron content in the equatorial region as part of her thesis work. She has a Master's in Electronics Science from Pune University, an MPhil in Information Technology from YCMOU, Nasik, and a Master's in Data Science from Liver Pool John Moore's University. She has also completed her PG diploma in Data Science from IIIT Bangalore and at present pursuing her MTech in Data Science and Engineering from BITS Pilani.

Course certificate

Given that this is a non-credit course, it is important to note that there will be no final examination. Consequently, learners will not receive a certificate upon completion of the course. This structure aligns with the academic nature of the program, emphasizing the absence of a formal assessment and certification process for non-credit courses.

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