MACHINE LEARNING USING PYTHON LAB

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 MACHINE LEARNING USING PYTHON LAB


Course Outcomes:

At the end of the course the student should be able to:

1.     Select data, model selection, model complexity and identify the trends.

2.     Understand a range of machine learning algorithms along with their strengths and weaknesses.

3.     Build predictive models from data and analyze their performance.

List of Experiments

WEEK 1 :

a)     Write a program using scikit-learn to implement K-means Clustering

b)     Program to calculate the entropy and the information gain

c)     Program to implement perceptron.

WEEK 2:

·       Implement and demonstrate the FIND-S algorithm for finding the most specific hypothesis based on a given set of training data samples. Read the training data from a .CSV file.

WEEK 3:

·       For a given set of training data examples stored in a .CSV file, implement and demonstrate the Candidate-Elimination algorithm to output a description of the set of all hypotheses consistent with the training examples.

WEEK 4:

·       Write a program to demonstrate the working of the decision tree based ID3 algorithm. Use an appropriate data set for building the decision tree and apply this knowledge to classify a new sample.

WEEK 5:

·       Build an Artificial Neural Network by implementing the Backpropagation algorithm and test the same using appropriate data sets.

WEEK 6:

·       Write a program to construct a Bayesian network considering medical data. Use this model to demonstrate the diagnosis of heart patients using standard Heart Disease Data Set.

 WEEK 7:

·       Apply EM algorithm to cluster a set of data stored in a .CSV file. Use the same data set for clustering using k-Means algorithm. Compare the results of these two algorithms and comment on the quality of clustering.

WEEK 8:

·       Write a program to implement k-Nearest Neighbors algorithm to classify the iris data set. Print both correct and wrong predictions.

WEEK 9:

·       Writea program to implement SVM algorithm to classify the iris data set. Print bothcorrect and wrong predictions.

WEEK 10:

·       Implement the non-parametric Locally Weighted Regression algorithm in order to fit data points. Select appropriate data set for your experiment and draw graphs. 

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