This tutorial provides Lab Programs on various topics of Machine Learning. It includes topics Baye's rule, k-nearest neighbours classification, k-means clustering, conditional probability, linear regression, Naive Bayes theorem and etc., .
1. | The probability that it is Friday and that a student is absent is 3%. Since there are 5 school days in a week, the probability that it is Friday is 20%. What is theprobability that a student is absent given that today is Friday? Apply Baye’s rule in python to get the result.(Ans: 15%) | View Solution |
2. | Extract the data from database using python | View Solution |
3. | Implement k-nearest neighbours classification using python | View Solution |
4. | Given the following data, which specify classifications for nine ombinations of VAR1 and VAR2 predict a classification for a case where VAR1=0.906 and VAR2=0.606, using the result of k-means clustering with 3 means (i.e., 3 centroids) | View Solution |
5. | The following training examples map descriptions of individuals onto high, medium and low credit-worthiness.Input attributes are (from left to right) income, recreation, job, status, age-group, home-owner. Find the unconditional probability of 'golf' and the conditional probability of 'single' given 'medRisk' in the dataset | View Solution |
6. | Implement linear regression using python | View Solution |
7. | Implement naive baye's theorem to classify the English text | View Solution |
8. | Implement an algorithm to demonstrate the significance of genetic algorithm | View Solution |
9. | Implement the finite words classification system using Back-propagation algorithm | View Solution |