Statistics and Machine Learning Labs 📊🤖

Welcome to the Statistics and Machine Learning Labs repository! Here, you'll find hands-on labs covering various topics in Statistics and Machine Learning. 📚✨

Table of Contents 📋

• Introduction
• Labs Overview
• Getting Started
• Lab Structure
• Contributing
• License

Introduction 💡

This repository contains 5 labs focusing on Statistics and Machine Learning concepts. Each lab is designed to provide practical experience and understanding of key topics in the field.

Labs Overview 🏫

1. Lab 1: Solving Probability Theory Problems 🎲

   • Introduction to Probability Theory.
   • Practical problem-solving exercises.

2. Lab 2: Calculating Statistics and Creating Samples 📊

   • Expected value, variance, and median calculations.
   • Generating custom samples.

3. Lab 3: Method of Moments, Maximum Likelihood, kNN/PCA 📈

   • Applying Method of Moments and Maximum Likelihood methods.
   • Introduction to kNN (k-Nearest Neighbors) and PCA (Principal Component Analysis).

4. Lab 4: Manual Implementation of Linear Regression 📉

   • Implementing Linear Regression using NumPy.
   • Hands-on exercises in regression analysis.

5. Lab 5: Manual Implementation of Logistic Binary Classifier 🤖

   • Building a Logistic Binary Classifier using NumPy.
   • Practical application of logistic regression in classification problems.

Getting Started 🚀

1. Clone the repository: git clone https://github.com/ivanovsdesign/labs_stats_ml
2. Navigate to the desired lab folder.
3. Follow the instructions in the lab's README for setup and exercises.

cd lab_stats_ml/lab_1

Lab Structure 🧪

Each lab folder contains:

• README.md: Lab overview, instructions, and exercises.
• Code/: Source code and solutions.
• Data/: Datasets for lab exercises.

Contributing 🤝

Contributions are encouraged! If you have ideas for new labs, improvements, or bug fixes, feel free to open issues or submit pull requests.

License 📝

No licesne is provided

Happy learning and experimenting! 🧠🤓