/ML-Elective-Theory-Practical

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ML-Elective-Theory-Practical

List of Practicals:

  1. Write a python program to handle the “Numpy” and “Matplotlib” library.
  2. Write a python program to plot 2D & 3D Gaussian distribution curve using probability of likelihood formula.
  3. Write a python program for Linear Regression Modeling.
  4. Write a python program for implementation of Baye's Theorem.
  5. Write a python program to implement discriminant function and decision boundary.
  6. Write a python program to implement Naïve Bayes Classifier.
  7. Write a python program to plot Receiver Operating Characteristics (ROC) curves.
  8. Write a python program for Multivariate Regression Modeling.
  9. Write a python program for Linear Regression using Gradient Descent algorithm.
  10. Write a python program to implement Parzen Window Density estimation for any randomly generated signal.
  11. Write a python program for implementation of k-nearest neighbor algorithm.