Course: GNEST305 Semester: S3 Status: Active Contributions: Welcome License: MIT KTU: AI-DS

KTU S3: Introduction to Artificial Intelligence and Data Science

Credits: 4 | CIE Marks: 40 | ESE Marks: 60 | Exam: 2 Hrs 30 Min
Teaching Hours: L:T:P:R = 3:1:0:0

Repository Overview

📄 Syllabus

Complete course syllabus and curriculum

🗺️ Route Map to AI

Comprehensive learning pathway guide

Slides

Topic-wise presentation materials

Programming

Python notebooks and datasets

Module-wise Structured Index

🟩 Module 1: Introduction to AI and Machine Learning

Slides: Introduction to Machine Learning Slides: Classification Codes: Classification Codes: Social Media Classification Slides: Clustering Codes: Clustering Slides: SVM Slides: Neural Networks Codes: Neural Network Codes: Gradient Descent

🟧 Module 2: Mathematical Foundations of AI and Data Science

Slides: Data Representation Slides: Feature Engineering Codes: Feature Engineering Slides: PCA Slides: Matrix Decomposition Slides: Mathematical Fundamentals

🟦 Module 3: Applied Probability and Statistics for AI and Data Science

Slides: Probability and Statistics Slides: Regression Codes: Regression Codes: Concrete Strength Prediction

🟨 Module 4: Basics of Data Science

Slides: Data Science Overview Slides: Visualization Slides: Power BI App: Power BI Slides: Python Codes: Python Brushup Codes: Pandas Codes: Numpy Slides: Numpy Slides: Pandas Codes: Matplotlib

Additional Resources

Beyond syllabus but useful for comprehensive learning

Slides: Deep Learning Codes: Image Classification Codes: NLP Basics Codes: Text Classification Codes: PyCaret (Low-Code ML)

Tools & Technologies Covered

Python

Numpy

Pandas

Matplotlib

Scikit-learn

PyCaret

Power BI

Jupyter Notebooks

Creator

Dr. Shailesh Sivan

👨‍🏫 Dr. Shailesh Sivan

Assistant Professor, Department of Computer Science, CUSAT

Specializing in AI, Data Science, and Academic Enablement

shaileshsivan@gmail.com GitHub Profile