MachineLearning/README.md

410242: Machine Learning (ML)

This repository contains vital resources for the Machine Learning course under the SPPU Computer Engineering syllabus (2019 pattern). It includes codes, handouts, notes, previous year questions (PYQs), and write-ups for assignments. The materials focus on understanding the need for machine learning, data pre-processing methods, classification techniques, multi-class classifiers, clustering algorithms, and fundamental neural network algorithms, equipping students to tackle real-time applications effectively.

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Index

Notes

Codes

1. Code-1 (Uber)
2. Code-2 (Spam Email Detection)
3. Code-4 (Gradient Descent Algorithm)
4. Code-6 (Clustering)

Jupyter Notebooks

1. Notebook-1 (Uber)
2. Notebook-2 (Spam Email Detection)
3. Notebook-4 (Gradient Descent Algorithm)
4. Notebook-6 (Clustering)

Datasets

1. Dataset for Practical-1
2. Dataset for Practical-2
3. Dataset for Practical-3

Assignments

• Assignment-1:
  • Questions
  • Answers
• Assignment-2:
  • Questions
  • Answers
• Assignment-3:
  • Questions
  • Answers

Practical

• Practical-1
• Practical-2
• Practical-4
• Practical-6

Question Papers

• IN-SEM
• END-SEM

IN-SEM PYQ Answers

END-SEM PYQ Answers

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Miscellaneous

-> Disclaimer: Please read the DISCLAIMER file for important information regarding the contents of this repository.

-> Note: Content such as codes, softcopies, write-ups and question papers is provided by us, i.e. our contributors. You are free to use this content however you wish, without any restrictions. Some of the notes and handouts have been provided by our professors, thus to use them for anything other than educational purposes, please contact them.

-> Maintained by:

• notkshitij
• TanmaySpamzzz

-> Repository icon from Flaticon.

-> Motto:

-> Keywords:

SPPU, Savitribai Phule Pune University, Pune University, Computer Engineering, COMP, Fourth Year, Final Year, BE, Semester 7, SEM-7, Notes, Codes, Practical work, Handouts, Assignments, PYQs, Machine Learning, ML, Data Pre-processing, Classification Methods, Multi-class Classifiers, Clustering Algorithms, Neural Networks, Real-time Applications, Supervised Learning, Performance Measurement, Engineering Problems,

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