Update README.md

Author: mr-pylin

README.md

@@ -1,109 +1,146 @@
 # 🔢 NumPy Workshop
-An in-depth guide to mastering NumPy, covering fundamental to advanced array operations for data science and numerical computing.
+
+[![License](https://img.shields.io/github/license/mr-pylin/numpy-workshop)](https://github.com/mr-pylin/numpy-workshop/blob/main/LICENSE)
+[![Python Version](https://img.shields.io/badge/Python-3.12.3-blue?logo=python&logoColor=white)](https://www.python.org/downloads/release/python-3123/)
+[![Codacy Badge](https://app.codacy.com/project/badge/Grade/1faf9d4577d3406a9ac65a4cb8d3d4f1)](https://app.codacy.com/gh/mr-pylin/numpy-workshop/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade)
+[![Code Style](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+![Repo Size](https://img.shields.io/github/repo-size/mr-pylin/numpy-workshop)
+![Last Updated](https://img.shields.io/github/last-commit/mr-pylin/numpy-workshop)
+[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen)](https://github.com/mr-pylin/numpy-workshop/pulls)
+
+An in-depth guide to mastering **NumPy**, covering fundamental to advanced array operations for **data science** and **numerical computing**.
 
 ## 📖 Table of Contents
-1. [**Array Basics**](./codes/01_array-basics.ipynb) 
+
+### 📖 Main Notebooks
+
+1. [**Array Basics**](./codes/01-array-basics.ipynb) 
  Introduction to NumPy arrays
-1. [**Arithmetic Operations**](./codes/02_arithmetic-operations.ipynb) 
+1. [**Arithmetic Operations**](./codes/02-arithmetic-operations.ipynb) 
  Arithmetic operations between (array & scalar) or (array & array)
-1. [**Comparative Operations**](./codes/03_comparative-operations.ipynb) 
+1. [**Comparative Operations**](./codes/03-comparative-operations.ipynb) 
  Comparative operations between (array & scalar) or (array & array)
-1. [**Index & Slice**](./codes/04_index-&-slice.ipynb) 
+1. [**Index & Slice**](./codes/04-index-&-slice.ipynb) 
  Basic and advanced indexing and slicing arrays including Mask & Filters
-1. [**Axes**](./codes/05_axes.ipynb) 
+1. [**Axes**](./codes/05-axes.ipynb) 
  Understanding use of axes in multi-dimensional arrays combined with methods [the hardest part in NumPy for newbies in my opinion]
-1. [**Array Manipulations**](./codes/06_array-manipulations.ipynb) 
+1. [**Array Manipulations**](./codes/06-array-manipulations.ipynb) 
  Techniques for updating values, appending, inserting, reshaping, concatenating, ...
-1. [**NdArray properties & methods**](./codes/07_ndarray-members.ipynb) 
+1. [**NdArray properties & methods**](./codes/07-ndarray-members.ipynb) 
  Comprehensive overview of properties and methods associated with NumPy arrays.
-1. [**Array Creation**](./codes/08_array-creation.ipynb) 
+1. [**Array Creation**](./codes/08-array-creation.ipynb) 
  Various methods to create NumPy arrays (e.g., `numpy.array`, `numpy.zeros`, etc.).
-1. [**Mathematics**](./codes/09_mathematics.ipynb) 
+1. [**Mathematics**](./codes/09-mathematics.ipynb) 
  Mathematical functions and operations available in NumPy
-1. [**Statistics**](./codes/10_statistics.ipynb) 
+1. [**Statistics**](./codes/10-statistics.ipynb) 
  Statistical functions for data analysis (mean, median, variance, etc.).
-1. [**Sort, Search & Count**](./codes/11_sort-search-count.ipynb) 
+1. [**Sort, Search & Count**](./codes/11-sort-search-count.ipynb) 
  Methods for sorting, searching, and counting elements in arrays.
-1. [**Logic**](./codes/12_logic.ipynb) 
+1. [**Logic**](./codes/12-logic.ipynb) 
  Logical operations and boolean indexing with NumPy arrays.
-1. [**Set**](./codes/13_set.ipynb) 
+1. [**Set**](./codes/13-set.ipynb) 
  Set operations for array elements (union, intersection, difference).
-1. [**Linear Algebra**](./codes/14_linear-algebra.ipynb) 
+1. [**Linear Algebra**](./codes/14-linear-algebra.ipynb) 
  Fundamental linear algebra operations using NumPy (matrix multiplication, determinants).
-1. [**Structured Array**](./codes/15_structured-array.ipynb) 
+1. [**Structured Array**](./codes/15-structured-array.ipynb) 
  Creating and manipulating structured arrays with custom data types.
-1. [**Input/Output**](./codes/16_input-output.ipynb) 
+1. [**Input/Output**](./codes/16-input-output.ipynb) 
  Techniques for reading from and writing to files using NumPy.
-1. [**Random Generator**](./codes/17_random-generator.ipynb) 
+1. [**Random Generator**](./codes/17-random-generator.ipynb) 
  Generating random numbers and distributions with NumPy's random module.
-1. [**Fourier Transform**](./codes/18_fourier-transform.ipynb) 
+1. [**Fourier Transform**](./codes/18-fourier-transform.ipynb) 
  Understanding and applying Fourier transforms in NumPy.
-1. [**Efficient Computing**](./codes/19_efficient-computing.ipynb) 
+1. [**Efficient Computing**](./codes/19-efficient-computing.ipynb) 
  Strategies for optimizing performance and memory usage in NumPy operations.
-1. [**Miscellaneous**](./codes/20_miscellaneous.ipynb) 
+1. [**Miscellaneous**](./codes/20-miscellaneous.ipynb) 
  Additional topics and advanced features in NumPy.
-1. [**Looking Ahead**](./codes/21_looking-ahead.ipynb) 
+1. [**Looking Ahead**](./codes/21-looking-ahead.ipynb) 
  Introduction to *Pandas* for data manipulation and *Matplotlib* for data visualization.
 
 ## 📋 Prerequisites
- - **Programming Fundamentals**
- - Proficiency in Python (data types, control structures, functions, etc.).
- - My Python Workshop: [github.com/mr-pylin/python-workshop](https://github.com/mr-pylin/python-workshop)
- - **Mathematics for Machine Learning**
- - Linear Algebra: Vectors, matrices, matrix operations.
- - [*Linear Algebra Review and Reference*](https://www.cs.cmu.edu/%7Ezkolter/course/linalg/linalg_notes.pdf) written by [Zico Kolter](https://zicokolter.com)
- - [*Notes on Linear Algebra*](https://webspace.maths.qmul.ac.uk/p.j.cameron/notes/linalg.pdf) written by [Peter J. Cameron](https://cameroncounts.github.io/web)
- - [*MATH 233 - Linear Algebra I Lecture Notes*](https://www.geneseo.edu/~aguilar/public/assets/courses/233/main_notes.pdf) written by [Cesar O. Aguilar](https://www.geneseo.edu/~aguilar/)
- - Probability & Statistics: Probability distributions, mean/variance, etc.
- - [*MATH1024: Introduction to Probability and Statistics*](https://www.sujitsahu.com/teach/2020_math1024.pdf) written by [Sujit Sahu](https://www.southampton.ac.uk/people/5wynjr/professor-sujit-sahu)
-
-# ⚙️ Setup
-This project was developed using Python `v3.12.3`. If you encounter issues running the specified version of dependencies, consider using this specific Python version.
-
-## 📦 Installing Dependencies
-You can install all dependencies listed in `requirements.txt` using [pip](https://pip.pypa.io/en/stable/installation/).
+
+- 👨‍💻 **Programming Fundamentals**
+ - Proficiency in **Python** (data types, control structures, functions, classes, etc.).
+ - My Python Workshop: [github.com/mr-pylin/python-workshop](https://github.com/mr-pylin/python-workshop)
+- 🔣 **Mathematics for Machine Learning**
+ - 🔲 **Linear Algebra**: Vectors, matrices, matrix operations.
+ - [**Linear Algebra Review and Reference**](https://www.cs.cmu.edu/%7Ezkolter/course/linalg/linalg_notes.pdf) written by [*Zico Kolter*](https://zicokolter.com).
+ - [**Notes on Linear Algebra**](https://webspace.maths.qmul.ac.uk/p.j.cameron/notes/linalg.pdf) written by [*Peter J. Cameron*](https://cameroncounts.github.io/web).
+ - [**MATH 233 - Linear Algebra I Lecture Notes**](https://www.geneseo.edu/~aguilar/public/assets/courses/233/main_notes.pdf) written by [*Cesar O. Aguilar*](https://www.geneseo.edu/~aguilar/).
+ - 🎲 **Probability & Statistics**: Probability distributions, mean/variance, etc.
+ - [**MATH1024: Introduction to Probability and Statistics**](https://www.sujitsahu.com/teach/2020_math1024.pdf) written by [*Sujit Sahu*](https://www.southampton.ac.uk/people/5wynjr/professor-sujit-sahu).
+
+## ⚙️ Setup
+
+This project was developed using Python **v3.12.3**. If you encounter issues running the specified version of dependencies, consider using this specific Python version.
+
+### 📝 List of Dependencies
+
+Installing **matplotlib** and **pandas** is **OPTIONAL**. They are used exclusively in the [**Looking Ahead Notebook**](./codes/21-looking-ahead.ipynb) to show an application of using them.
+
+[![numpy](https://img.shields.io/badge/numpy-2.1.3-orange)](https://pypi.org/project/numpy/2.1.3/)
+[![matplotlib](https://img.shields.io/badge/matplotlib-3.9.1-green)](https://pypi.org/project/matplotlib/3.9.1/)
+[![pandas](https://img.shields.io/badge/pandas-2.2.2-yellow)](https://pypi.org/project/pandas/2.2.2/)
+
+### 📦 Installing Dependencies
+
+You can install all dependencies listed in [**requirements.txt**](./requirements.txt) using [**pip**](https://pip.pypa.io/en/stable/installation/):
+
 ```bash
 pip install -r requirements.txt
 ```
 
-## 🛠️ Usage Instructions
- - Open the root folder with [VS Code](https://code.visualstudio.com/)
- - **Windows/Linux**: `Ctrl + K` followed by `Ctrl + O`
- - **macOS**: `Cmd + K` followed by `Cmd + O`
- - Open `.ipynb` files using [Jupyter extension](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter) integrated with **VS Code**
- - Allow **VS Code** to install any recommended dependencies for working with Jupyter Notebooks.
- - Note: Jupyter is integrated with both **VS Code** & **[Google Colab](https://colab.research.google.com/)**
-
-# 🔗 Useful Links
- - **NumPy Website**:
- - The official website for NumPy, providing information, tutorials, and resources for the NumPy library
- - Official site: [numpy.org](https://numpy.org/)
- - **NumPy Documentation**:
- - Comprehensive guide and reference for all functionalities and features of the NumPy library
- - Doc: [numpy.org/doc](https://numpy.org/doc/)
- - **NumPy Source Code**:
- - Over 1500 contributors are currently working on NumPy.
- - Link: [github.com/numpy/numpy](https://github.com/numpy/numpy)
- - **Looking Ahead**:
- - **Pandas**
- - A powerful, open-source data analysis and manipulation library built on top of NumPy for Python
- - Official site: [pandas.pydata.org](https://pandas.pydata.org/)
- - My Pandas Workshop: [Coming Soon](https://github.com/mr-pylin/#)
- - **MatPlotLib**
- - A comprehensive library for creating static, animated, and interactive visualizations in Python
- - Official site: [matplotlib.org](https://matplotlib.org/)
- - My MatPlotLib Workshop: [Coming Soon](https://github.com/mr-pylin/#)
- - **PyTorch**
- - An open-source machine learning library for Python developed by [Meta AI](https://ai.meta.com/), used for applications such as deep learning and neural networks.
- - Official site: [pytorch.org](https://pytorch.org/)
- - My PyTorch Workshop: [github.com/mr-pylin/pytorch-workshop](https://github.com/mr-pylin/pytorch-workshop)
-
-# 🔍 Find Me
+### 🛠️ Usage Instructions
+
+1. Open the root folder with [**VS Code**](https://code.visualstudio.com/):
+ - **Windows/Linux**: `Ctrl + K` followed by `Ctrl + O`
+ - **macOS**: `Cmd + K` followed by `Cmd + O`
+1. Open `.ipynb` files using [**Jupyter extension**](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter) integrated with **VS Code**.
+1. Allow **VS Code** to install any **recommended dependencies** for working with Jupyter Notebooks.
+
+✍️ **Notes**: 
+
+- The **table of contents** embedded in the **notebooks** is not fully functional on **GitHub**!
+- To navigate the **table of contents** effectively, open the notebooks **locally** or view them via [**nbviewer**](https://nbviewer.org/github/mr-pylin/pytorch-workshop) for a better experience.
+
+## 🔗 Useful Links
+
+### **NumPy**
+
+- Official Website:
+ - The official website for NumPy, providing information, tutorials, and resources for the NumPy library
+ - Official site: [numpy.org](https://numpy.org/)
+- Documentation
+ - Comprehensive guide and reference for all functionalities and features of the NumPy library
+ - Doc: [numpy.org/doc](https://numpy.org/doc/)
+- Source Code
+ - Over 1500 contributors are currently working on NumPy.
+ - Link: [github.com/numpy/numpy](https://github.com/numpy/numpy)
+
+### **Looking Ahead**
+
+- **Pandas**
+ - A powerful, open-source data analysis and manipulation library built on top of NumPy for Python
+ - Official site: [pandas.pydata.org](https://pandas.pydata.org/)
+ - My Pandas Workshop: [Coming Soon](https://github.com/mr-pylin/#)
+- **Data Visualization**
+ - A comprehensive collection of Python libraries for creating static, animated, and interactive visualizations: **Matplotlib**, **Seaborn**, and **Plotly**.
+ - Official sites: [matplotlib.org](https://matplotlib.org/) | [seaborn.pydata.org](https://seaborn.pydata.org/) | [plotly.com](https://plotly.com/)
+ - My MatPlotLib Workshop: [github.com/mr-pylin/data-visualization-workshop](https://github.com/mr-pylin/data-visualization-workshop)
+- **PyTorch**
+ - An open-source **machine learning** library for Python developed by [**Meta AI**](https://ai.meta.com/), used for applications such as **deep learning** and **neural networks**.
+ - Official site: [pytorch.org](https://pytorch.org/)
+ - My PyTorch Workshop: [github.com/mr-pylin/pytorch-workshop](https://github.com/mr-pylin/pytorch-workshop)
+
+## 🔍 Find Me
+
 Any mistakes, suggestions, or contributions? Feel free to reach out to me at:
- - 📍[linktr.ee/mr_pylin](https://linktr.ee/mr_pylin)
- 
+
+- 📍[**linktr.ee/mr_pylin**](https://linktr.ee/mr_pylin)
+
 I look forward to connecting with you! 🏃‍♂️
 
-# 📄 License
+## 📄 License
+
 This project is licensed under the **[Apache License 2.0](./LICENSE)**. 
-You are free to use, modify, and distribute this code, but you must include copies of both the [**LICENSE**](./LICENSE) and [**NOTICE**](./NOTICE) files in any distribution of your work.
+You are free to **use**, **modify**, and **distribute** this code, but you **must** include copies of both the [**LICENSE**](./LICENSE) and [**NOTICE**](./NOTICE) files in any distribution of your work.