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.Well-posed learning problem
    • 6.Applications of machine learning
  • 2

    Module 2 : Data pre-processing

    • 7.Basic data types
    • 8.Data exploration
    • 9.Data exploration (continued)
    • 10.Data issues and remediation
    • 11.Issues in machine learning
    • QUESTION BOOKLET_AIML_Week1_QUIZ
    • ANSWER BOOKLET_AIML_WEEK1_QUIZ
  • 3

    Module 3 : Introduction to Python

    • 12.Getting started with Python
    • 13.Basic Python commands
    • 14.Basic Python commands (continued)
    • 15.Functions
    • 16.Operators
    • 17.Conditional (IF) Statement
    • 18.FOR Loops
    • 19.WHILE Loops
    • 20.Libraries - os, numpy, pandas, matplotlib
    • 21.numpy
    • 22.pandas
    • 23.Data exploration using matplotlib
    • 24.Data issues and remediation
    • QUESTION BOOKLET_AIML_Week2_QUIZ
    • ANSWER BOOKLET_AIML_WEEK2_QUIZ
  • 4

    Module 4 : Modelling and Evaluation

    • 25.What is a model
    • 26.Selecting a model
    • 27. Training model - Holdout Method
    • 28. Training model - k-fold cross-validation
    • 29. Training model - bootstrap sampling
    • 30. Model representation and interpretability - under-fitting, over-fitting
    • 31. Model performance evaluation - Classification
    • 32. Model performance evaluation - Regression
    • 33. Model performance evaluation - Clustering
    • 34. Model performance tuning
  • 5

    Module 5 : Feature Engineering

    • 35. Basics of feature engineering
    • 36. Feature Construction
    • 37. Feature extraction
    • 38. Feature selection 1
    • 39. Feature selection 2
    • 40. Feature selection 3
    • QUESTION BOOKLET _AIML_Week3_QUIZ
    • ANSWER BOOKLET_AIML_Week3_QUIZ
  • 6

    Module 6 : Supervised and unsupervised learning algorithms

    • 41. Classification algorithm - KNN 1
    • 42. Classification algorithm - KNN 2
    • 43. Classification algorithm - KNN 3
    • 44. Classification algorithm - KNN 4
    • 45. Classification algorithm - KNN 5
    • 46. Classification algorithm - Decision Tree 1
    • 47. Classification algorithm - Decision Tree 2
    • 48. Classification algorithm - Decision Tree 3
    • 49. Classification algorithm - Decision Tree 4
    • 50. Classification algorithm - Decision Tree 5
    • 51. Regression
    • 52. Unsupervised Learning - I
    • 53. Unsupervised Learning - II
    • 54. KMeans
    • 55. Association Analysis
    • QUESTION BOOKLET_AIML_Week4_QUIZ
    • ANSWER BOOKLET_AIML_Week4_QUIZ
  • 7

    Quiz

    • Question Paper - Quiz 1