👋 Welcome to the Machine Learning Wiki

Welcome to my Machine Learning Wiki — a curated collection of notes, visual guides, and practical experiments as I explore machine learning and data science.

This space serves as:

• 🧠 A learning journal — documenting key concepts and personal insights
• 🔍 A reference hub — summarizing algorithms with clean visuals and formulas
• 🧪 An experiment lab — running notebooks and testing ideas on real data

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📚 What You’ll Find Here

🔸 Foundations

• Perceptron & Adaline
• Logistic Regression
• Decision Trees & Random Forests
• Naive Bayes
• K-Nearest Neighbors

🔸 Deep Learning

• Neural Networks (MLP)
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)
• Transformers & NLP

🔸 Tools & Ecosystem

• Python & Jupyter Setup
• Using Docker for ML
• Google Colab Integration

🔸 Projects

• Titanic Survival Prediction
• MNIST Handwritten Digits
• Sentiment Analysis with BERT

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🚀 How to Use This Wiki

• Browse any topic to explore math-backed explanations, visuals, and code
• Use Colab badges to run notebooks instantly in the cloud
• Refer to notebooks for practical demos and experimentation

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🧰 Environment Support

💡 No GPU? No problem.

• Most notebooks are Colab-ready — just click and run
• For local development, spin up the full environment using the included Dockerfile

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💬 Questions or Suggestions?

If you find a bug, want to suggest an improvement, or just want to say hi:

• Open an issue
• Start a discussion

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✨ Let's Learn Together!

Whether you're just starting or deepening your understanding, I hope this wiki helps you learn, experiment, and grow throughout your ML journey.

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📚 References

This wiki draws inspiration from these excellent resources:

1. Machine Learning with PyTorch and Scikit-Learn
   — by Sebastian Raschka, Yuxi Liu, and Vahid Mirjalili

2. Deep Learning Interviews
   — by Shlomo Kashani

3. 《機器學習實戰》（Machine Learning in Action）
   — A practical guide to applying ML models with Python

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