JNTUH CSE-AIML MACHINE LEARNING LAB SYLLABUS

Course Objective: The objective of this lab is to get an overview of the various machine learning
techniques and can able to demonstrate them using python.
Course Outcomes: After the completion of the course the student can able to:
 understand complexity of Machine Learning algorithms and their limitations;
 understand modern notions in data analysis-oriented computing;
 be capable of confidently applying common Machine Learning algorithms in practice and implementing their own;
 Be capable of performing experiments in Machine Learning using real-world data.
List of Experiments
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 the probability that a student is
absent given that today is Friday? Apply Baye’s rule in python to get the result. (Ans: 15%)
2. Extract the data from database using python
3. Implement k-nearest neighbor’s classification using python
4. Given the following data, which specify classifications for nine combinations of VAR1 and VAR2
predict a classification for a case where VAR1=0.906 and VAR2=0.606, using the result of kmeans clustering with 3 means (i.e., 3 centroids)
VAR1 VAR2 CLASS
1.713 1.586 0
0.180 1.786 1
0.353 1.240 1
0.940 1.566 0
1.486 0.759 1
1.266 1.106 0
1.540 0.419 1
0.459 1.799 1
0.773 0.186 1
5. The following training examples map descriptions of individuals onto high, medium and low
credit-worthiness.
medium skiing design single twenties no -> highRisk
high golf trading married forties yes -> lowRisk
low speedway transport married thirties yes -> medRisk
medium football banking single thirties yes -> lowRisk
high flying media married fifties yes -> highRisk
low football security single twenties no -> medRisk
medium golf media single thirties yes -> medRisk
medium golf transport married forties yes -> lowRisk
high skiing banking single thirties yes -> highRisk
low golf unemployed married forties yes -> highRisk
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?
6. Implement linear regression using python.
7. Implement Naïve Bayes theorem to classify the English text
8. Implement an algorithm to demonstrate the significance of genetic algorithm
9. Implement the finite words classification system using Back-propagation algorithm

CSE-AIML