Introduction to Machine learning

Course Materials

• • Lecture Notes

    • Module 0: Introduction [Module 0] [Assignments] 

    • Module I: Introduction [Module 1] [Supplementary Notes] [Assignments] [Class test 1 - A&B  C&D&ECE]

    • Module II: Parametric Learning [Module 2] [Hands on - 1] [Class test]

    • Module III: Non Linear Learning: [Module 3] [Hands on] [Viva]

    • Module IV: Expert Systems [Module 4] [Supplementary Material] [Group Presentation]

    • Module V: Models Evaluation and Ensemble Learning [Module 5] [Supplementary Material] [Quiz]

  • Practical Materials 

    • • 1. • Statistical Analysis and How to report? [Example]

           • Develop an indigenous neural network to accept AND, OR, XOR, XNOR Gate [Document]

           • Regression with Single and multiple variables [Document] [Example] [Single] [Multiple]

           • Classification [Documentation][Dataset(Train, Test)]

           • Leaf Disease Detection using Deep learning Model [Dataset: Train, validation, Test] [Code]

           • Support Vector Machine [Document]

           • Decision Tree [Document]

           • K-means Clustering [Link]

           • Random Forest [Link]

           • Neural Network [Link]

           • Practical Applications [Course Project in Group of 4]

• • • • Previous Year Course Feedback