Machine Learning Practice Assignments 📘🤖

Welcome to the repository for the Machine Learning Practice course! This is part of the IIT Madras BSc Degree in Data Science. Here, you'll find a collection of hands-on assignments that will help you solidify your understanding of core machine learning concepts. Let's dive in! 🚀

Table of Contents 📋

• Overview
• Assignments
• Technologies Used
• Getting Started
• Usage
• License

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Overview 📝

Machine Learning can feel like a lot at first, but the best way to learn is by doing! This repository contains practical exercises that build on what you learn in the course lectures. From data preprocessing to building your first neural network, each assignment is a step forward in your ML journey.

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Assignments 📂

Here’s what you can expect:

1. Data Preprocessing 🛠️

   • Learn how to handle missing data, scale features, and encode categorical variables. These skills are the foundation of any ML pipeline.

2. Supervised Learning 🎯

   • Get hands-on with algorithms like Linear Regression, Logistic Regression, Decision Trees, and Support Vector Machines. Each has its own strengths, and you'll learn when to use what.

3. Unsupervised Learning 🔍

   • Dive into clustering and dimensionality reduction techniques like K-Means and Hierarchical Clustering. Great for finding hidden patterns in data!

4. Model Evaluation and Optimization 📊

   • Understand how to evaluate your models with cross-validation, tune their hyperparameters, and use metrics to measure success.

5. Neural Networks and Deep Learning (Optional) 🌐

   • Ready for a challenge? Explore the basics of neural networks, including forward and backpropagation.

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Technologies Used 💻

This repository uses:

• Programming Language: Python 🐍
• Libraries:
  • NumPy for numerical computations.
  • Pandas for data manipulation.
  • Scikit-learn for machine learning models.
  • Matplotlib and Seaborn for data visualization.
• Tools:
  • Jupyter Notebook for an interactive coding experience.
  • Google Colab (if you prefer working in the cloud).

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Getting Started 🚀

Prerequisites

Before you start, make sure you have:

1. Python 3.7 or above installed on your machine.
2. The following libraries:

   pip install numpy pandas scikit-learn matplotlib seaborn

Clone the Repository

To get started:

git clone <repository-url>
cd <repository-folder>

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Usage 🛠️

1. Pick an assignment folder you want to work on.
2. Open the Jupyter Notebook file (.ipynb) or Python script (.py).
3. Follow the instructions and run the code to complete the exercises.
4. Check out the comments in the code—they’re there to help you out!

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License 📜

Feel free to use this repository to learn and grow. It’s licensed under the MIT License, so adapt and share as you wish. Happy coding! 🎉