Learning Resource is a centralized repository containing study materials, notes, and practical programs for students in Artificial Intelligence, Machine Learning, Data Science, and Big Data.
This repository is designed to support learning, revision, lab practice, and exam preparation.
It contains a structured collection of theory notes and hands-on practicals, organized subject-wise for easy access.
✅ Undergraduate and postgraduate students in AI, ML, Data Science, and Big Data
✅ Students preparing for practical labs, assignments, or university exams
✅ Beginners who want conceptual clarity and hands-on experience
✅ Anyone looking for syllabus-aligned study resources
- Syllabus-aligned Theory Notes: Detailed and well-structured notes for quick revision and understanding
- Laboratory Practicals: Python-based implementations and experiments for practical learning
- Step-by-step Guidance: Clear instructions, mapping of practicals to Course Outcomes (COs) and Learning Outcomes (LLOs/TLOs)
- Unit-wise & Topic-wise Organization: Easy navigation through subjects, topics, and experiments
- Python programming (NumPy, SymPy, etc.)
- Machine Learning & Deep Learning frameworks
- Hadoop ecosystem (HDFS, MapReduce, YARN)
- NoSQL databases (MongoDB)
- Hive & Pig
- Apache Spark, Spark SQL, Spark Streaming, MLlib
- Clone or download the repository
- Navigate to the subject folder of interest
- Access notes for theory or practicals for hands-on experiments
- Follow instructions, run programs, and practice examples
- Use for revision, lab practice, and exam preparation
We welcome contributions to expand this learning resource repository!
- If you have syllabus-aligned notes, tutorials, or practical solutions for other subjects, you can contribute them.
- Please submit a pull request with your resources organized subject-wise, following the same structure as existing folders.
- Ensure your notes and programs are clear, well-documented, and educational.
All resources in this repository are created strictly for educational purposes and are aligned with university syllabi.
They are intended as learning aids and do not replace textbooks or classroom instruction.