This crash course offers a complete introduction to machine learning (ML) — from core concepts like data, models, and loss functions to practical coding examples and real-world applications. Inside, you’ll explore supervised and unsupervised learning, neural networks, evaluation techniques, and advanced topics such as ensemble methods, transfer learning, and explainable AI. Designed for beginners and intermediate learners, this guide equips you with step-by-step explanations, Python examples, and resources to kickstart your ML journey.

Whether you’re a student, developer, or aspiring AI engineer, this PDF provides the roadmap to mastering ML with clarity, depth, and practical insights.