Machine Learning Skill Course ( 2025-26)

Syllabus : click here
Lecture plan : click here

Date	Theory Topic	Practical topic	Resources	Submission
01/12/2025	Introduction to Machine Learning	Numpy and Pandas	Theory , Lab , Data Set	Submit
08/12/2025	Data Pre-Processing and Feature Engineering	Data Cleaning, Transformation, Feature Engineering with Numpy, Pandas	Theory , Lab	Submit
15/12/2025		Data Visualization with Matplotlib & Seaborn	Lab	Submit
22/12/2025		Recap
29/12/2025	K-NN and Naive Bayes	K-NN and Naive Bayes using sciki-learn	Theory, Lab

General Resources:

• Python3 Tutorial
• Python for Everbody
• Basics of Python
• Kaggle
• NumPy Tutorial
• Pandas Tutorial

Machine Learning - Minor (2025-26)

Welcome to the course

• Lecture material : Click here
• Videos: Click here

Design and Analysis of Algorithms ( 2025-26)

Welcome to the course

• Lecture material : Click here
• Selected Toipcs Videos: Click here
• Quiz-1 paper with solutions : QPaper , Deatiled_solutions , Explanation Video
• MID-1 paper with solutions : QPaper , Detailed_solutions, Explanation Video
• hacker rank coding contest (Greedy and DP-1D): JNTUGV_III_CSE_Greedy_DP1D

• Assignment -2 : Solving 4 X 4 Sudoku using back tracking and draw a state space tree
• Select a puzzle based on last two digits your roll number (501 should select puzzle 01)
• Click here for list of puzzles

We will be following two text books
• Text book1 : Fundamentals of Computer Algorithms by Ellis Horowitz, Sartaj Sahni, Sanguthevar Rajasekaran
• Text book2 : Introduction to Algorithms by Thomas H. Cormen, charles e. leiserson, ronald l. rivest, clifford stein
• (Unit-I) Introduction : Text book1 (Pages 1 to 62)
• (Unit-II) Divide and conquer : Text book1 (Pages 136 to 196)
• (Unit-III) Greedy : Text book1 (Pages 210 to 269)
• (Unit-III) Dynamic Programming : Text book1 (Pages 272 to 317)
• (Unit-IV) BackTracking : Text book1 (Pages 359 to 392)
• (Unit-V) Branch and Bound : Text book1 (Pages 399 to 431)