The article provides an overview of the evolution and application of machine learning techniques in computer vision, tracing the development from early image classification methods to advanced convolutional neural networks (CNNs). It highlights key milestones such as the introduction of the perceptron algorithm, the SVM algorithm, and Yann Lecun's LeNet, which paved the way for modern CNNs like AlexNet. The text explores various computer vision tasks, including image classification, object localization, detection, and image segmentation, detailing how these have been tackled with CNN architectures like R-CNN, Fast R-CNN, Faster R-CNN, YOLO, and U-Net. It also discusses the significance of datasets such as ImageNet and COCO in advancing the field and mentions the implementation of novel techniques like GANs and VAEs for tasks like image generation, domain transformation, and neural style transfer. The article serves as a comprehensive introduction to the key concepts and advances in computer vision, offering insights into both foundational and cutting-edge approaches.