https://github.com/ayushverma135/machine-learning-tutorials-lab-work

This repository contains a collection of Python programs designed to demonstrate various machine learning concepts and techniques. Each program focuses on different aspects of machine learning, providing practical examples and implementations.
https://github.com/ayushverma135/machine-learning-tutorials-lab-work

datasets machie-learning machine-learning-algorithms python techniques

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This repository contains a collection of Python programs designed to demonstrate various machine learning concepts and techniques. Each program focuses on different aspects of machine learning, providing practical examples and implementations.

• Host: GitHub
• URL: https://github.com/ayushverma135/machine-learning-tutorials-lab-work
• Owner: Ayushverma135
• License: mit
• Created: 2024-05-09T20:50:09.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-05-30T17:18:21.000Z (7 months ago)
• Last Synced: 2024-05-30T20:17:07.515Z (7 months ago)
• Topics: datasets, machie-learning, machine-learning-algorithms, python, techniques
• Homepage:
• Size: 16.9 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Machine-Learning-Tutorials-Lab-Work

Course Outcome (CO) Bloom’s Knowledge Level (KL) At the end of course , the student will be able to CO 1 Understand complexity of Machine Learning algorithms and their limitations; K5, K6 CO 2 Understand modern notions in data analysis-oriented computing; K5, K6 CO 3 Be capable of performing experiments in Machine Learning using real-world data. K5, K6 CO 4 Be capable of confidently applying common Machine Learning algorithms in practice and implementing their own; K5, K6 DETAILED SYLLABUS Implementation of following machine learning algorithms in various projects using Python: Lab Experiments:

- 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.

- 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.

- 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.

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

- Write a program to implement the naïve Bayesian classifier for a sample training data set stored as a .CSV file. Compute the accuracy of the classifier, considering few test data sets.

- Assuming a set of documents that need to be classified, use the naïve Bayesian Classifier model to perform this task. Built-in Java classes/API can be used to write the program. Calculate the accuracy, precision, and recall for your data set.

- 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. You can use Java/Python ML library classes/API.

- 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. You can add Java/Python ML library classes/API in the program.

- Write a program to implement k-Nearest Neighbour algorithm to classify the iris data set. Print both correct and wrong predictions. Java/Python ML library classes can be used for this problem.

- Implement the non-parametric Locally Weighted Regression algorithm in order to fit data points. Select appropriate data set for your experiment and draw graphs. Note: The Instructor may add/delete/modify/tune experiments