The Computer Vision From Scratch lecture series offers a practical and structured pathway into modern computer vision, focusing on building intuition from first principles. Created to bridge theory and hands-on implementation, the course begins with a conceptual introduction to computer vision and progressively moves into core techniques such as filtering and convolution — the mathematical backbone of image processing. Learners then construct simple neural networks tailored for visual data, gaining insight into how machines interpret patterns in images. As the series advances, students explore image classification pipelines, hyperparameter tuning workflows, and experimental optimization strategies that mirror real-world ML practice. Important topics like regularization, dropout, batch normalization, and early stopping are covered to prevent overfitting and improve model generalization. The final modules introduce transfer learning, showing how pretrained models accelerate development and performance. By completing this series, learners gain both theoretical clarity and practical skills required to design, train, and refine computer vision systems. It serves as an excellent stepping stone toward advanced deep learning, research applications, or production-grade AI pipelines
