Machine Learning Lab Experiments Repository

This repository contains a collection of lab experiments for the Machine Learning course offered at MIT-ADT College. Each experiment is designed to provide hands-on experience with various machine learning concepts and techniques. The experiments are primarily conducted using Python and its popular libraries for machine learning and data analysis.

Experiment List

Installation and Configuration of Machine Learning Environment

Setup Anaconda environment on Windows or Ubuntu. Utilize Jupyter Notebook for interactive coding. Data Pre-processing with Python/R

Download and preprocess datasets from UCI, Data.org, or other repositories.

Perform basic data cleaning, transformation, and visualization. Single and Multilayer Perceptron Implementation

Implement single-layer and multi-layer perceptrons on a chosen dataset.

Explore neural network architecture and training. Bayesian Classifier for IRIS Dataset

Develop a Bayesian classifier for the IRIS dataset.

Understand probabilistic classification techniques. Correlation and Best Fit Line

Create or download correlated datasets.

Find the best fit line for the data and visualize the relationship. Principal Component Analysis (PCA)

Utilize scikit-learn's inbuilt Breast Cancer dataset.

Implement PCA for dimensionality reduction and visualization. Decision Tree Classification/Regression

Implement decision tree models for classification and regression tasks.

Interpret decision boundaries and tree structures. Support Vector Machine (SVM) Implementation

Implement SVM for classification or regression on a given dataset.

Explore kernel functions and tuning parameters. K-Means Clustering

Apply K-means algorithm to multidimensional datasets (Cars or Wine) from UCI repository.

Understand clustering and group similar data points. K-Nearest Neighbors (KNN) Algorithm

Download and utilize the famous Iris dataset.

Implement KNN for classification and evaluate performance metrics. Explore the impact of different K values on error rate. Convolutional Neural Network (CNN)

Implement CNN using TensorFlow on MNIST or CIFAR-10 dataset.

Build and train deep learning models for image classification. Basic Image Processing with OpenCV

Perform fundamental image processing operations using OpenCV.

Enhance, filter, and manipulate images. Feel free to explore each experiment's directory for detailed instructions, code examples, and datasets.

Contributing

If you would like to contribute to this repository by adding new experiments, fixing issues, or improving documentation, please follow the standard GitHub workflow:

Fork the repository. Create a new branch for your work. Make your changes and commit them. Open a pull request with a descriptive explanation of your changes. We hope you find these experiments insightful and valuable for your machine learning journey. Happy learning and experimenting!