To begin machine learning for computer vision, start by understanding the basics of Python programming and foundational ML concepts like supervised learning. Learn key libraries like OpenCV, TensorFlow, or PyTorch for image processing and model building.
Practice on simple datasets like MNIST or CIFAR-10 to gain experience in tasks such as image classification. Gradually explore advanced techniques like convolutional neural networks (CNNs) and transfer learning.
Utilize free resources, such as Coursera courses or Stanford's CS231n, and participate in online challenges like Kaggle to deepen your understanding and gain hands-on experience.