Hands-on NumPy practice covering array manipulation, mathematical operations, and numerical computing in Python.
This repository contains my hands-on practice of NumPy, focusing on numerical computing, array manipulation, and mathematical operations using Python.
The goal of this repository is to build a strong foundation in numerical programming, which is essential for Data Science, Machine Learning, and AI development.
NumPy is the core library for numerical computations in Python.
In this repository, I implemented various NumPy concepts from basic to intermediate level through practical examples and exercises.
This practice helped me understand how data is structured and manipulated efficiently before applying Machine Learning algorithms.
- Creating Arrays
- Array Attributes (shape, size, dtype)
- Indexing & Slicing
- Reshaping Arrays
- Element-wise Operations
- Broadcasting
- Aggregation Functions (sum, mean, min, max)
- Statistical Operations
- Boolean Masking
- Fancy Indexing
- Array Concatenation & Splitting
- Linear Algebra Operations
- Random Number Generation
- Python
- NumPy
✔ Strong understanding of array-based computation
✔ Improved performance awareness compared to Python lists
✔ Better understanding of data structures used in ML
✔ Practical foundation for Pandas and Machine Learning
The knowledge gained from this practice is directly applied in:
- Data Preprocessing
- Machine Learning Model Implementation
- Data Analysis
- Scientific Computing
This repository reflects my foundational training in numerical computing using NumPy.