Introduction to Machine learning

Course Materials

• • Lecture Notes

    • Module I: Introduction [Module 1] [Assignments] 

    • Module II: Parametric Learning [Module 2] [Hands on] [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. • Linear Regression [Document] [Example] [Assignment]

           • Multi-variate Linear Regression [Code] 

           • Classification [Documentation][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