This repository contains my hands-on practice notebooks covering NumPy concepts from basic to advanced levels. It also includes introductory examples of data visualization using Matplotlib and basic image processing techniques using scikit-image.
- Array Creation and Initialization
- Array Attributes and Operations
- Indexing and Slicing
- Reshaping and Manipulating Arrays
- Broadcasting
- Mathematical and Statistical Functions
- Boolean Masking and Filtering
- Random Module
- Practice Exercises
- Line Plots using Matplotlib
- Scatter Plots
- Bar Charts
- Histograms
- Basic Plot Customization
- Reading and Displaying Images
- Understanding Image Arrays using NumPy
- Basic Image Transformations
- Simple Image Processing Operations using scikit-image
- Build a strong foundation in NumPy for Data Science and Machine Learning.
- Develop problem-solving skills through regular practice.
- Gain introductory exposure to data visualization and image processing concepts.
- Maintain a structured record of my learning journey.
This repository also includes handwritten notes prepared during my learning journey. The notes summarize important NumPy concepts, basic Matplotlib visualizations, and introductory image processing examples using scikit-image.
- Python
- NumPy
- Matplotlib
- scikit-image
- Google Colab
- Jupyter Notebook
This repository reflects my continuous learning process in Data Science. The notebooks include examples, experiments, and practice exercises completed while exploring NumPy and related Python libraries.
Author: Abhay Dwivedi