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.
Author Name: Google Cloud
Author Description:
Google Cloud can help solve your toughest problems and grow your business. With Google Cloud, their infrastructure is your infrastructure. Their tools are your tools. And their innovations are your innovations.

Table of Contents

• 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