/labs_stats_ml

Statistics and ML labs for Data Science course

Primary LanguageJupyter Notebook

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 💡

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! 🧠🤓