- Course
- Cloud
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.
What you'll learn
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.
Table of contents
- What Is Computer Vision | 4m 14s
- Different Type of Computer Vision Problems | 6m 13s
- Computer Vision Use Cases | 3m 44s
- Vision API - Pre-built ML Models | 11m 54s
- Lab Introduction - Detecting Labels, Faces, and Landmarks in Images with the Cloud Vision API | 18s
- Pluralsight: Getting Started with GCP and Qwiklabs | 3m 48s
- Lab: Detecting Labels, Faces, and Landmarks in Images with the Cloud Vision API | 10s
- Lab Introduction - Lab: Extracting Text from the images using the Google Cloud Vision API | 32s
- Lab: Extracting Text from the Images using the Google Cloud Vision API | 10s
- Readings | 10s
About the author
Build, innovate, and scale with Google Cloud Platform.