Week 1/readme.md

Machine Learning Zoomcamp – Week 1: Linear Algebra Foundations

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.

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📘 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.

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🔧 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.

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🧪 Example Problem

One of the exercises included:

1. Creating a dataset:

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.

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:

total_weight = np.sum(w)
print("Sum of weights:", total_weight)

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🛠️ Technologies Used

• Python – Programming language for implementation
• NumPy – Efficient numerical computations and linear algebra
• Jupyter Notebooks – Interactive environment for running exercises

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📌 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.

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🔗 Resources

• Machine Learning Zoomcamp – Official course repository
• NumPy Documentation – For matrix operations and linear algebra
• Jupyter Notebooks – Interactive coding environment