🔢 NumPy Practice Notebook

A hands-on practice notebook focused on mastering NumPy, the fundamental library for numerical computing in Python. This repository documents my learning journey and practical exercises to build strong foundations for data science and machine learning.

📌 Project Purpose

The goal of this notebook is to:

Develop a strong understanding of NumPy arrays

Practice efficient numerical computations

Build a vectorization mindset for performance

Strengthen core skills required for data science and ML

🛠 Topics Covered ✅ Array Fundamentals

Creating arrays

Data types

Shape and dimensions

Reshaping arrays

✅ Indexing & Slicing

Basic and advanced indexing

Boolean masking

Subsetting data

✅ Broadcasting

Rules of broadcasting

Element-wise operations

Practical examples

✅ Stacking & Splitting

vstack, hstack

concatenate

split

✅ Linear Algebra Basics

Dot product

Matrix multiplication

Determinant and inverse

Eigenvalues (intro level)

✅ Performance Optimization

Why NumPy is faster than loops

Vectorization techniques

Efficient computations

🚀 Key Learning Outcomes

Improved problem-solving using arrays

Ability to write faster, optimized code

Better understanding of numerical operations

Stronger foundation for ML libraries like pandas, scikit-learn, and TensorFlow

📂 Repository Structure numpy-practice.ipynb README.md

🎯 Who This Is For

Beginners learning NumPy

Data science students

Anyone strengthening Python numerical skills

👤 Author

Shorya Bisht Data Scientist | Analytics Enthusiast Passionate about learning and applying data-driven skills. https://www.linkedin.com/in/shorya-bisht-a20144349/