Programme Parameters
Foundation
3 Weeks (30 Hrs)
Online (e-LMS)
• No prior experience required — basic computer literacy is sufficient.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
This course, AI Foundations for Students and Beginners, is an 8-week program that provides a solid introduction to artificial intelligence (AI). Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course, AI Foundations for Students and Beginners, is a 3-week program that provides a solid introduction to artificial intelligence (AI).
📋 Course Objectives
1. Translate biotechnology theory into practical, reproducible analysis.
2. Assemble a documented case study that evidences your applied capability.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in biotechnology
• R&D engineers and working professionals applying biotechnology in industry
• Academics and educators building research or teaching capacity in biotechnology
🚀 Key Learning Outcomes
• A portfolio-grade biotechnology deliverable you can defend and extend.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Machine Learning and AI Basics (2 Weeks)
Intro to AI, its branches (ML, Deep Learning, NLP) • How AI is used in industries • Key concepts: supervised vs unsupervised learning, classification, regression • Simple AI models using Python and Scikit-learn
Python Programming for AI (2 Weeks)
Learn Python basics for AI programming • Setting up Python and Jupyter Notebooks • Python basics: data types, loops, functions • Introduction to NumPy and Pandas for data manipulation
Natural Language Processing (NLP) (2 Weeks)
Understanding NLP and how machines process language • Working with text data and preprocessing • Basic NLP models: tokenization, stemming, lemmatization • Using libraries like NLTK and spaCy
Scikit-learn for Machine Learning (2 Weeks)
Introduction to Scikit-learn • Building models like Decision Trees, K-Nearest Neighbors, and Linear Regression • Measuring performance with accuracy, precision, recall • Project: Predict student performance based on previous data
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | RStudio |
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