a9na/applied-machine-learning-labwork

🧠💻 A hands-on repository for applied machine learning lab work, featuring practical implementations, real-world datasets 📊, and model evaluation techniques ✅ using Python 🐍 and popular ML libraries 📚

🧠 Applied Machine Learning Lab

Welcome to the Applied Machine Learning Lab repository! This repo contains hands-on experiments and practical implementations using real-world datasets, designed to deepen your understanding of machine learning concepts and tools.

🚀 What You'll Find

• 📊 Real-world datasets
• 🧪 Experiment notebooks
• ✅ Model training and evaluation
• 🐍 Python-based projects using libraries like scikit-learn, pandas, matplotlib, and more

🛠️ Technologies Used

• Python 🐍

• Scikit-learn 🤖

• Pandas 🐼

• NumPy ➗

• Matplotlib 📈

• Seaborn 🎨

• Jupyter Notebooks 📓

• 📚 Resources

Here are some great resources to help you along your machine learning journey:

• Google's Machine Learning Crash Course – Beginner-friendly interactive course from Google
• Coursera – Andrew Ng's ML Course – A classic course to get a strong ML foundation
• Scikit-learn Documentation – Official documentation for one of the most-used ML libraries
• Kaggle Learn – Hands-on coding tutorials and datasets
• Fast.ai Practical Deep Learning – Free deep learning course with a practical focus
• The Elements of Statistical Learning (Book) – A more advanced, theory-heavy resource
• Google Colab – Free online Jupyter notebooks with GPU support
• Machine Learning Mastery – Blog with clear, code-focused tutorials
• ML Cheatsheets – Concise, practical ML cheat sheets for quick reference
• Awesome Machine Learning (GitHub) – Curated list of ML frameworks, libraries, and software