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# Machine Learning Zoomcamp – Week 1: Linear Algebra Foundations
[](https://www.python.org/)
[](https://jupyter.org/)
[](https://numpy.org/)
This repository documents my journey through **Week 1** of the **Machine Learning Zoomcamp**, a comprehensive 4-month course offered by **DataTalksClub**. Week 1 focuses on building the **mathematical foundation** required for machine learning, including linear algebra and matrix operations.
---
## 📘 Week 1 Overview
The goal of this week was to understand the mathematical underpinnings of machine learning algorithms. Key topics included:
- **Matrix Operations**: Matrix multiplication, transposition, and inversion.
- **Linear Algebra Fundamentals**: Dot products, matrix shapes, and their relevance in ML.
- **Practical Applications**: Implementing linear algebra concepts using Python and NumPy.
---
## 🔧 Exercises and Implementations
The exercises involved:
- Computing the transpose of a matrix `X` and performing `X.T @ X`.
- Inverting the resulting matrix `(X.T @ X)^(-1)`.
- Using the inverse to solve linear equations, a fundamental step in linear regression.
---
## 🧪 Example Problem
One of the exercises included:
1. Creating a dataset:
```python
y = [1100, 1300, 800, 900, 1000, 1100, 1200]
````
2. Computing `X.T @ X`, inverting it, multiplying by `X.T`, and then multiplying by `y` to get the weight vector `w`.
```python
import numpy as np
# Example steps
XTX = X.T @ X
XTX_inv = np.linalg.inv(XTX)
w = XTX_inv @ X.T @ y
```
3. Summing all elements of `w` to analyze the result:
```python
total_weight = np.sum(w)
print("Sum of weights:", total_weight)
```
---
## 🛠️ Technologies Used
* **Python** – Programming language for implementation
* **NumPy** – Efficient numerical computations and linear algebra
* **Jupyter Notebooks** – Interactive environment for running exercises
---
## 📌 Key Takeaways
* Mastering linear algebra is essential for understanding machine learning algorithms.
* Operations like matrix multiplication and inversion form the core of regression and many ML models.
* Hands-on exercises help translate theoretical concepts into practical applications.
---
## 🔗 Resources
* [Machine Learning Zoomcamp](https://github.com/DataTalksClub/mlzoomcamp) – Official course repository
* [NumPy Documentation](https://numpy.org/doc/) – For matrix operations and linear algebra
* [Jupyter Notebooks](https://jupyter.org/) – Interactive coding environment
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