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Google Cloud

Computer Vision Fundamentals with Google Cloud

Google Cloud and Google via Coursera

Overview

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 hyperparameters 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.

Syllabus

  • Introduction
    • Course Introduction
  • Introduction to Computer Vision and Pre-built ML Models for Image Classification
    • Introduction to Computer Vision and Pre-built ML Models for Image Classification
  • Vertex AI and AutoML Vision on Vertex AI
    • Learn about Vertex AI and AutoML Vision on Vertex AI
  • Custom Training with Linear, Neural Network and Deep Neural Network models
    • Learn about Custom Training with Linear, Neural Network and Deep Neural Network models
  • Convolutional Neural Networks
    • Learn about Convolutional Neural Networks
  • Dealing with Image Data
    • Learn about dealing with Image Data
  • Summary
    • Course Summary

Taught by

Google Cloud Training

Reviews

4.6 rating at Coursera based on 517 ratings

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