AI Study Series Part 2: Machine Learning Essentials

Welcome to Part 2 of the University AI Study Series. This section focuses on Machine Learning (ML)—the engine behind modern AI. ML enables computers to learn patterns from data and make intelligent decisions without being explicitly programmed for every task.

What is Machine Learning?

Machine Learning is a subset of AI that uses statistical techniques to enable machines to improve with experience. It’s used in everything from recommendation systems and facial recognition to autonomous vehicles and language translation.

Key Concepts You’ll Learn

• Supervised Learning (e.g., classification, regression)
• Unsupervised Learning (e.g., clustering, dimensionality reduction)
• Overfitting vs Generalization
• Training, validation, and test sets
• Model evaluation metrics (accuracy, precision, recall, F1-score)

Courses & Platforms to Learn Machine Learning

• Machine Learning by Andrew Ng – Coursera: The legendary course that introduces practical ML with examples in Octave/Matlab.
• Practical Deep Learning for Coders – Fast.ai: A hands-on course teaching real-world ML using Python and PyTorch.
• DeepLearning.AI: Advanced ML and deep learning specializations designed for developers and researchers.
• Hugging Face Learn: A cutting-edge resource to learn how to train and deploy transformers and LLMs.

Key Tools and Libraries

• Scikit-learn – Classic library for traditional ML
• TensorFlow – Google’s deep learning library
• PyTorch – A flexible framework widely used in research and production
• RunwayML – A no-code platform to experiment with ML models visually

What’s Next?

Now that you've grasped how machines learn from data, you're ready to explore deep neural networks, computer vision, and natural language processing in Part 3: Deep Learning and Neural Networks.