A beginner-friendly guide to Machine Learning and Deep Learning using real world examples and practical code to help you understand the concepts.
This course makes complex Machine Learning concepts easy to understand through:
- Real-world analogies that relate complex concepts to everyday experiences you're familiar with
- Clear, detailed explanations that help you understand not just what to do, but why you're doing it
- Hands-on coding exercises that reinforce learning through practical implementation
- Essential mathematical concepts explained simply, avoiding unnecessary complexity
- Basic Python knowledge required (Recommended: 30 Days Of Python)
- No prior Machine Learning or Deep Learning experience needed
- Willingness to learn through examples and practice
- Work through modules sequentially, starting with foundations
- Each notebook includes:
- Interactive code examples runnable in Google Colab (click rocket icon)
- Real-world analogies explaining complex concepts
- Practical exercises to reinforce learning
- Clear explanations without mathematical equations
- Run all code examples yourself - typing code helps reinforce learning
- Use AI tools like Perplexity when stuck on challenging concepts
- Practice with the provided exercises in each chapter
- Create new code cells to experiment with concepts
- Report issues or suggestions in the GitHub repo
If you come across any issues or have suggestions to improve this open-source project email me at [email protected]
We welcome contributions to improve this open-source project:
- Suggest clearer analogies and real-world examples
- Add new practical exercises and code samples
- Fix technical errors or unclear explanations
- Share your learning experience to help others
- Improve documentation and explanations
- How do I run the code in Google Colab?
- Click on the rocket icon in the top right corner of the code cell to run it in Google Colab.
If you need help:
- Experiment with the code in Google Colab
- Use AI chat tools like Perplexity for concept clarification
- Create an issue in the GitHub repo
- Try modifying the example code to deepen understanding
Remember: Everyone learns differently - if this approach doesn't resonate with you, consider exploring visual learning resources like MLU-Explain.