Crandi

Github Tharunkorivi Machine Learning Lab Machine Learning Lab A

Leo Migdal
-
github tharunkorivi machine learning lab machine learning lab a

A collection of hands-on experiments and assignments designed to reinforce core concepts in machine learning. This repo covers the full ML pipeline—from data preprocessing to model training and evaluation—structured week-wise for an academic lab setting. ⚠️ Student Information (Required for Lab Submission) Explore each week's lab in its respective folder. This repository contains a collection of lab exercises, practical assignments, and projects designed to help learners understand and apply various machine learning concepts. Each exercise focuses on specific algorithms, techniques, or tasks commonly encountered in machine learning.

Follow the provided tasks and complete the exercises in the notebook. Contributions are welcome to expand the scope of exercises or improve the existing solutions: This project is licensed under the MIT License. Gratitude to educators, researchers, and open-source contributors whose tools and frameworks have made these exercises possible. Welcome to the Machine Learning Lab Exercises repository! This repository contains a collection of practical experiments that cover various concepts, techniques, and algorithms in machine learning.

Each experiment is implemented in Python using popular libraries like Pandas, Scikit-learn, Matplotlib, TensorFlow, and PyTorch. Below is the detailed documentation of all experiments included in this repository. Objective: Learn to preprocess datasets using Pandas and Scikit-learn. Objective: Implement the Find-S algorithm to identify the most specific hypothesis from training data. Objective: Explore datasets using univariate visualizations. Objective: Understand the concept of PCA and its applications in dimensionality reduction.

IMPLEMENT AND DEMONSTRATETHE 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 ALGORITHMTO 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 TOCLASSIFY 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. Open solution to the Mapping Challenge 🌎 Repository with programs for the Machine Learning Lab VTU - (15CSL76) 📚 All Machine Learning Lab Programs for VTU 7th sem 2018 is updated with code, dataset, and Description on how to execute the program. Jupyter Notebooks for Machine Learning Lab for the syllabus of the Visveswaraya Technological University.

Contains Jupyter Notebooks of 10 different Machine Learning programs and algorithms ranging from extremely basic to intermediate. Sab-AI Lab is a boutique AI and machine learning lab in Nagoya-Japan. This repository contains all Machine Learning lab exercises done in Google Colab. It includes notebooks on data preprocessing, visualization, and core ML algorithms, organized by lab sessions. Perfect for quick reference and revision. Programs for Machine Learning Lab (20CS67L)

4) Box Plot and minimax Algo(Alpha beta) 5) Naive Bayes Classifier with Titanic Dataset 9) Agglomerative clustering based on single-linkage, complete-linkage criteria 10) Principal Component Analysis(PCA) and Linear Discriminant Analysis(LDA) algorithms. There was an error while loading. Please reload this page.

There was an error while loading. Please reload this page. You can create a release to package software, along with release notes and links to binary files, for other people to use. Learn more about releases in our docs.

People Also Search

A Collection Of Hands-on Experiments And Assignments Designed To Reinforce

A collection of hands-on experiments and assignments designed to reinforce core concepts in machine learning. This repo covers the full ML pipeline—from data preprocessing to model training and evaluation—structured week-wise for an academic lab setting. ⚠️ Student Information (Required for Lab Submission) Explore each week's lab in its respective folder. This repository contains a collection of l...

Follow The Provided Tasks And Complete The Exercises In The

Follow the provided tasks and complete the exercises in the notebook. Contributions are welcome to expand the scope of exercises or improve the existing solutions: This project is licensed under the MIT License. Gratitude to educators, researchers, and open-source contributors whose tools and frameworks have made these exercises possible. Welcome to the Machine Learning Lab Exercises repository! T...

Each Experiment Is Implemented In Python Using Popular Libraries Like

Each experiment is implemented in Python using popular libraries like Pandas, Scikit-learn, Matplotlib, TensorFlow, and PyTorch. Below is the detailed documentation of all experiments included in this repository. Objective: Learn to preprocess datasets using Pandas and Scikit-learn. Objective: Implement the Find-S algorithm to identify the most specific hypothesis from training data. Objective: Ex...

IMPLEMENT AND DEMONSTRATETHE FIND-S ALGORITHM FOR FINDING THE MOST SPECIFIC

IMPLEMENT AND DEMONSTRATETHE 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 ALGORITHMTO OUTPUT A DESCRIPTION OF THE SET OF ALL HYPOTHESES CONSISTENT WITH THE TRAINING EXAMPLES. WRITE A...

WRITE A PROGRAM TO IMPLEMENT THE NAÏVE BAYESIAN CLASSIFIER FOR

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. Open solution to the Mapping Challenge 🌎 Repository with programs for the Machine Learning Lab VTU - (15CSL76) 📚 All Machine Learning Lab Programs for VTU 7th sem 2018 is updated with code, dataset, and Descriptio...