Welcome to the ml_fundamentals_challenge! This repository guides you through a 30-day journey from the basics of machine learning (ML) to advanced deep learning architectures. You will find hands-on Python implementations and clear explanations that make learning enjoyable.
To get started, you will need to download the software. Please visit the following page:
On this page, you will see the latest release. Simply click on the appropriate version for your system, and follow the instructions to download. The process is straightforward.
Before you begin, please ensure your system meets the following requirements:
- Operating System: Windows, macOS, or Linux
- Python: Version 3.6 or higher
- RAM: At least 4 GB (8 GB recommended for deep learning tasks)
- Disk Space: At least 1 GB free space
You can download Python from the official Python website. Make sure to install basic libraries such as NumPy and PyTorch. You can do this using the package manager pip.
The learning journey is broken down into clear topics. Each day introduces new concepts and hands-on exercises:
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Day 1-7: Machine Learning Basics
- Understand core concepts like supervised vs. unsupervised learning.
- Learn key algorithms including linear regression and decision trees.
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Day 8-14: Mathematics for Machine Learning
- Explore essential math concepts such as linear algebra and calculus.
- Apply these concepts to real-world problems.
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Day 15-21: Deep Learning Fundamentals
- Discover neural networks and their architectures.
- Implement basic models using TensorFlow and PyTorch.
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Day 22-30: Advanced Deep Learning
- Dive into complex networks like transformers.
- Implement advanced architectures and participate in mini-projects.
Each module contains code snippets and exercises. Follow these steps to run the application:
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Setup Python Environment
- Open your command line or terminal.
- Create a new virtual environment using:
python -m venv myenv - Activate the environment:
- For Windows:
myenv\Scripts\activate - For macOS/Linux:
source myenv/bin/activate
- For Windows:
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Install Required Libraries
- In the terminal, run:
pip install numpy pytorch
- In the terminal, run:
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Run Sample Code
- Navigate to the directory where you downloaded the repository.
- Locate the example files in the
examplesfolder. - Run a Python script as a test:
python examples/sample_script.py
Machine learning is a field of artificial intelligence that allows computers to learn from data and improve performance without explicit programming.
No! This learning path is designed for beginners. Each day builds on the previous concepts in an easy-to-understand way.
Yes, you can ask questions in the discussion section of the repository. Community members and maintainers are there to help.
If you face problems, first ensure all steps were followed. If issues persist, visit the issues section in the repository to find solutions or to report a new issue.
This project is licensed under the MIT License. You can view the full license in the LICENSE file in this repository.
We would like to thank all of the contributors and the open-source community for making this project possible. Your support and feedback are greatly appreciated.
For further assistance or inquiries, feel free to reach out through the repository's issues page or discussion forum. Happy learning!