Python Math Exercises

A collection of short, daily Python notebooks designed to strengthen both math intuition and coding fluency.
Each notebook tackles a small but meaningful problem, progressing from scratch implementations toward more Pythonic and library-based solutions.

The goal: move from guided learning → real-world problem solving in Jupyter, while practicing version control with Git/GitHub.

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Table of Contents 📑

• Repository Structure
• Exercises
• Setup & Usage
• Learning Goals
• About Me

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Repository Structure 📘

python-math-exercises/
│
├── day1_vectors.ipynb
├── day2_matrix_multiplication.ipynb
├── day3_probabilities.ipynb
├── day4_fromscratch_to_numpy.ipynb
└── README.md

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Exercises ✏️

Week 1 Wrap-Up — Python in Practice + Math Warmup

This week established strong Python fundamentals through hands-on math exercises in Jupyter.
Each day built confidence in implementing core concepts from scratch (vectors, matrices, probabilities) and then validating them with NumPy.
By the end of the week, I pushed my first GitHub repo with 4 completed notebooks, developed a daily terminal → Jupyter → GitHub workflow, and saw firsthand how NumPy streamlines performance and syntax.
Ready to move into Week 2: NumPy & Pandas Foundations.

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Day 1 – Vectors

• Implemented vector addition and dot products from scratch.
• Compared with NumPy’s built-in vector operations.

Day 2 – Matrices

• Built matrix multiplication manually with loops.
• Verified results using NumPy’s dot function.
• Timed manual vs. NumPy operations.

Day 3 – Probabilities (Dice Simulation)

• Simulated dice rolls with Python’s random module.
• Calculated frequencies of sums (2–12) and compared with theoretical probabilities.
• Optional visualization using matplotlib for simulated vs. theoretical distributions.

Day 4 – From Scratch → NumPy

• Rebuilt a previous math exercise (vector ops, matrix multiplication, or dice simulation) in pure Python.
• Refactored the same task using NumPy arrays and vectorized operations.
• Compared performance of pure Python vs. NumPy with timing tests on small and large matrices.
• Reflected on how NumPy simplifies syntax and speeds up computation.

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Setup & Usage 🔧

1. Clone the repo:

   git clone https://github.com/cleburn/python-math-exercises.git
   cd python-math-exercises

2. Install dependencies (most are standard in Jupyter):

   pip install numpy matplotlib

3. Launch Jupyter:

   jupyter lab

4. Open any notebook and run the cells.

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Learning Goals 🎯

• Practice Python fundamentals (loops, lists, dicts, functions).
• Build intuition for linear algebra & probability through code.
• Get comfortable with NumPy and matplotlib.
• Develop Git + GitHub workflow habits (add → commit → push).

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About Me 👤

Hi, I’m Cleburn Walker — a lifelong learner exploring the intersection of math, AI, and problem-solving with Python.
I’m currently building a strong foundation in machine learning, data analysis, and AI engineering while documenting the journey through daily coding exercises and projects.

• 📍 Based in Texas, USA
• 💻 Background in military, sales, real estate, & REI consulting
• 🚀 Pivoting into AI/ML engineering with a focus on practical projects and portfolio building
• 🌐 Connect with me on LinkedIn

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Part of my ongoing ML/AI Engineer Roadmap — documenting steady, daily progress toward mastery.