Computer Vision Fundamentals with Google Cloud

This course describes different types of computer vision use cases and then highlights different machine learning strategies for solving these use cases.

This course describes different types of computer vision use cases and then highlights different machine learning strategies for solving these use cases. The strategies vary from experimenting with pre-built ML models through pre-built ML APIs and AutoML Vision to building custom image classifiers using linear models, deep neural network (DNN) models or convolutional neural network (CNN) models. The course shows how to improve a model's accuracy with augmentation, feature extraction, and fine-tuning hyper-parameters while trying to avoid overfitting the data. The course also looks at practical issues that arise, for example, when one doesn't have enough data and how to incorporate the latest research findings into different models. Learners will get hands-on practice building and optimizing their own image classification models on a variety of public datasets in the labs they will work on.

• Introduction 5mins
• Introduction to Computer Vision and Pre-built ML Models for Image Classification 31mins
• Vertex AI and AutoML Vision on Vertex AI 26mins
• Custom Training with Linear, Neural Network and Deep Neural Network models 49mins
• Convolutional Neural Networks 42mins
• Dealing with Image Data 28mins
• Summary 4mins