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Basic and not-so-basic ML projects I created for teaching ML to post-grad students.

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ML Projects for Lectures

Welcome to my collection of machine learning projects! This repository contains various projects that I use for delivering lectures. Each project is designed to be a comprehensive example of different machine learning techniques and concepts.

Major Technologies Used

  • Pandas, Numpy, Matplotlib
  • Scikit-learn
  • Tensorflow and Keras

Table of Contents

Projects

California Housing

This project involves predicting housing prices in California. It includes data preprocessing, feature engineering, and model training using various regression techniques.

CIFAR

Exploratory Data Analysis (EDA) on the UCI HAR dataset. It involves image classification using convolutional neural networks (CNNs).

Custom Objects

This project contains custom Keras layers, models, losses, metrics etc., plus some general purpose Python modules that have been used throughout other projects in the repository.

Digit Recognition

A project for recognizing handwritten digits using neural networks. This includes preparing the MNIST dataset.

Fashion MNIST

Working with the Fashion MNIST dataset, this project covers TFRecords and Protobufs.

Flowers

Image classification on a dataset of flower images, including preprocessing steps.

Human Activity Recognition

This project involves using SVM and Logistic Regression to classify human activities based on smartphone sensor data from the UCI HAR dataset.

LA Housing

Predictive analysis on housing data from Los Angeles. Final project presentation included.

Large Movie Review

Sentiment analysis on a large dataset of movie reviews.

Olivetti Faces

Facial recognition using the Olivetti Faces dataset.

Reinforcement Learning

Exploring policy gradients in reinforcement learning.

Spam Email Classifier

Building a classifier to identify spam emails.

Titanic

Predicting the survival of passengers on the Titanic using various machine learning algorithms.

Yelp Unsupervised

Unsupervised learning on Yelp data to identify patterns and insights.

Getting Started

To get started with these projects, clone the repository:

git clone https://github.com/ryuukkk/small-projects-ml.git

Create a conda environment:

conda env create -f environment.yaml

..and start exploring!

Contact

Email: thorykartik111@gmail.com LinkedIn: Kartik Kumar

About

Basic and not-so-basic ML projects I created for teaching ML to post-grad students.

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