🧮 NumPy_ML - Simple Machine Learning with NumPy

📋 About NumPy_ML

NumPy_ML is a machine learning tool made using only Python and NumPy. It shows how machine learning algorithms work from the ground up. This means no extra software or complicated tools. You get clear code that anyone can look at and learn from.

This library covers essential machine learning ideas like:

• Training models using the Adam optimizer
• Building convolutional neural networks (CNNs)
• Using softmax for classification problems
• Working with MNIST dataset (handwritten numbers)
• Learning core programming concepts in Python and NumPy

It is designed to be easy to extend. If you want to add new features or experiment, you can do so without much trouble.

🎯 Key Features

• Built entirely with NumPy for independence and clarity
• Clean and well-organized Python code to learn from
• Supports convolutional neural networks and softmax layers
• Uses Adam optimizer for efficient training
• Ready to explore MNIST, a popular dataset for handwriting
• Easy to add new models or change existing ones
• Does not require complex setup or programming tools

🖥️ System Requirements

To run NumPy_ML on your Windows PC, you need:

• Windows 10 or later
• Minimum 4 GB RAM (8 GB recommended)
• Python 3.7 or newer installed on your system
• At least 500 MB of free disk space
• Internet access to download the package

🚀 Getting Started

This section will help you get NumPy_ML up and running with straightforward instructions.

Step 1: Visit the Download Page

Click the big download button at the top or go to:

https://github.com/Quiet-unwariness496/NumPy_ML

On this page, you will find all files and instructions.

Step 2: Install Python

If you do not already have Python, follow these steps:

1. Go to the official Python download page: https://www.python.org/downloads/windows/
2. Choose the latest version (3.7 or above).
3. Download the Windows installer.
4. Run the installer and make sure to check the box that says Add Python to PATH.
5. Complete the installation by following the on-screen instructions.

Step 3: Download NumPy_ML Files

On the GitHub page, click on the green Code button, then select Download ZIP. Save the file to a place you can find easily, such as your Desktop or Downloads folder.

Step 4: Extract the Files

1. Right-click the ZIP file you downloaded.
2. Choose Extract All....
3. Pick a folder where you want the files and click Extract.

Step 5: Install Required Libraries

NumPy_ML needs NumPy and Matplotlib to work. Open the Command Prompt:

• Press Win + R, type cmd, and press Enter.
• Type the following commands and press Enter after each:

pip install numpy
pip install matplotlib

This will get everything NumPy_ML needs to run.

Step 6: Run NumPy_ML

1. Open the extracted folder.
2. Look for a file named main.py or a similar starter file. If not clear, look for files related to examples or tutorials.
3. In the Command Prompt, use the cd command to move to this folder. For example:

cd Desktop\NumPy_ML-main

4. Then run the program by typing:

python main.py

NumPy_ML should start, and you will see its machine learning examples or interface.

🛠️ How to Use NumPy_ML

Here are simple ways to explore what NumPy_ML offers:

• Run Examples: The library contains example scripts showing how to train models using MNIST or other data. Look for the examples folder.
• Modify Codes: Open the Python files in any text editor (like Notepad or VSCode) and change settings to understand how models respond.
• Add Data: You can try your own datasets if you learn the format (usually CSV or image files).
• Experiment with Models: Change optimizer settings or neural network layers to see how performance changes.

📂 Files Included

• Python scripts showing machine learning algorithms
• Sample datasets or links to download MNIST
• Documentation and comments inside the code
• README.md for instruction

🔧 Troubleshooting

• If Python commands like pip or python are not recognized, make sure Python is installed and added to your system PATH.
• Check your internet connection when installing libraries.
• If the program does not run, try restarting your PC and repeat the steps.
• Make sure you are in the right folder when running Python scripts (using the cd command).

🔗 Download and Setup Link

Click or visit below to access the latest files and instructions on GitHub:

https://github.com/Quiet-unwariness496/NumPy_ML

🧑‍💻 Learning Resources

To better understand machine learning and Python, consider these free resources:

• Python Basics
• NumPy Documentation
• Machine Learning Crash Course by Google
• MNIST Dataset Explanation

Use these to grow your skills while exploring NumPy_ML.