{"id":18950359,"url":"https://github.com/ayushverma135/machine-learning-tutorials-lab-work","last_synced_at":"2026-03-28T06:30:18.394Z","repository":{"id":241932445,"uuid":"798467260","full_name":"Ayushverma135/Machine-Learning-Tutorials-Lab-Work","owner":"Ayushverma135","description":"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. 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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:\n\n- 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.\n- 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.\n- 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.\n- Build an Artificial Neural Network by implementing the Backpropagation algorithm and test the same using appropriate data sets.\n- 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.\n- 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.\n- 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.\n- 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.\n- 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.\n- 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\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fayushverma135%2Fmachine-learning-tutorials-lab-work","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fayushverma135%2Fmachine-learning-tutorials-lab-work","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fayushverma135%2Fmachine-learning-tutorials-lab-work/lists"}