Welcome to the Statistics and Machine Learning Labs repository! Here, you'll find hands-on labs covering various topics in Statistics and Machine Learning. 📚✨
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.
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Lab 1: Solving Probability Theory Problems 🎲
- Introduction to Probability Theory.
- Practical problem-solving exercises.
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Lab 2: Calculating Statistics and Creating Samples 📊
- Expected value, variance, and median calculations.
- Generating custom samples.
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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).
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Lab 4: Manual Implementation of Linear Regression 📉
- Implementing Linear Regression using NumPy.
- Hands-on exercises in regression analysis.
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Lab 5: Manual Implementation of Logistic Binary Classifier 🤖
- Building a Logistic Binary Classifier using NumPy.
- Practical application of logistic regression in classification problems.
- Clone the repository:
git clone https://github.com/ivanovsdesign/labs_stats_ml
- Navigate to the desired lab folder.
- Follow the instructions in the lab's README for setup and exercises.
cd lab_stats_ml/lab_1
Each lab folder contains:
- README.md: Lab overview, instructions, and exercises.
- Code/: Source code and solutions.
- Data/: Datasets for lab exercises.
Contributions are encouraged! If you have ideas for new labs, improvements, or bug fixes, feel free to open issues or submit pull requests.
No licesne is provided
Happy learning and experimenting! 🧠🤓