🎉 Welcome to Machine Learning Basics

📘 What is Machine Learning?

Machine learning is a branch of artificial intelligence that enables systems to learn from data, identify patterns, and make decisions with minimal human intervention.

🧩 Types of Machine Learning

• Supervised Learning (e.g., classification, regression)
• Unsupervised Learning (e.g., clustering, dimensionality reduction)
• Reinforcement Learning (e.g., Q-learning, policy gradients)

🧠 Machine Learning Workflow

1. Data Collection
   Gather relevant datasets for training and testing.
2. Data Preprocessing
   Clean, normalize, and split data into training/validation/test sets.
3. Model Training
   Select algorithms and train them on labeled data (if applicable).
4. Evaluation & Tuning
   Use metrics like accuracy or RMSE to refine the model.
5. Deployment
   Integrate the model into real-world applications.

🌐 Applications & Examples

• Predicting stock prices with time series analysis
• Classifying images using convolutional neural networks
• Recommending products via collaborative filtering

For a deeper dive into practical implementations, check our Machine Learning Practice Guide.

📚 Further Reading

• Introduction to AI
• Deep Learning Fundamentals
• Python for ML