Course curriculum

  • 1

    Module 1 : Introduction to Machine Learning

    • 1. Introduction to the Course
    • 2. Human learning and it's types
    • 3. Machine learning and it's types
    • 4. Process of Machine Learning
    • 5. Applications of machine learning
  • 2

    Module 2 : Data pre-processing

    • 6. Basic data types
    • 7. Data exploration
    • 8. Data exploration (continued)
    • 9. Data issues and remediation
    • 10. Issues in machine learning
  • 3

    Module 3 : Introduction to Python

    • 11. Getting started with Python
    • 12. Basic Python commands
    • 13. Basic Python commands (continued)
    • 14. Functions
    • 15. Operators
    • 16. Conditional (IF) Statement
    • 17. FOR Loops
    • 18. WHILE Loops
    • 19. Libraries - os, numpy, pandas, matplotlib
    • 20. numpy
    • 21. pandas
  • 4

    Module 4 : Modelling and Evaluation

    • 22. What is a model
    • 23. Selecting a model
    • 24. Training model - Holdout Method
    • 25. Model performance evaluation - Classification
  • 5

    Module 5 : Machine learning algorithms

    • 26. Classification algorithm - K-Nearest Neighbour 1
    • 27. Classification algorithm - K-Nearest Neighbour 2
    • 28. Classification algorithm - K-Nearest Neighbour 3
    • 29. Classification algorithm - K-Nearest Neighbour 4
    • 30. Classification algorithm - K-Nearest Neighbour 5
    • 31. Regression