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Computer Vision Fundamentals with Google Cloud
  • Level Expert
  • Duration 19 hours
  • Course by Google Cloud
  • Offered by

About

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.

Modules

Welcome to the course

    • 1 Videos
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1 Videos

  • Course Introduction

Course Feedback

    • 1 Readings
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1 Readings

  • How to Send Feedback

What Is Computer Vision

    • 1 Videos
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1 Videos

  • What Is Computer Vision

Different Type of Computer Vision Problems

    • 1 Videos
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1 Videos

  • Different Type of Computer Vision Problems

Computer Vision Use Cases

    • 1 Videos
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1 Videos

  • Computer Vision Use Cases

Vision API - Pre-built ML Models

    • 4 Videos
    • 1 Readings
    • 2 ExternalTool
    • 1 Assignment
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1 Assignment

  • Quiz

2 External Tool

  • Lab: Detecting Labels, Faces, and Landmarks in Images with the Cloud Vision API
  • Lab: Extracting Text from the images using the Google Cloud Vision API

4 Videos

  • Vision API - Pre-built ML Models
  • Lab Introduction - Detecting Labels, Faces, and Landmarks in Images with the Cloud Vision API
  • Getting Started with Google Cloud Platform and Qwiklabs
  • Lab Introduction - Lab: Extracting Text from the images using the Google Cloud Vision API

1 Readings

  • Readings

What is Vertex AI and why does a unified platform matter?

    • 1 Videos
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1 Videos

  • What is Vertex AI and why does a unified platform matter?

Introduction to AutoML Vision on Vertex AI

    • 1 Videos
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1 Videos

  • Introduction to AutoML Vision on Vertex AI

How does Vertex AI help with the ML workflow, part 1 ?

    • 1 Videos
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1 Videos

  • How does Vertex AI help with the ML workflow, part 1 ?

How does Vertex AI help with the ML workflow, part 2 ?

    • 1 Videos
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1 Videos

  • How does Vertex AI help with the ML workflow, part 2 ?

Which vision product is right for you ?

    • 2 Videos
    • 1 Readings
    • 1 ExternalTool
    • 1 Assignment
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1 Assignment

  • Quiz

1 External Tool

  • Lab: Identifying Damaged Car Parts with Vertex AI for AutoML Vision users

2 Videos

  • Which vision product is right for you ?
  • Lab Introduction - Identifying Damaged Car Parts with Vertex AI for AutoML Vision users

1 Readings

  • Readings

Introduction

    • 1 Videos
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1 Videos

  • Introduction

Introduction to Linear Models

    • 1 Videos
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1 Videos

  • Introduction to Linear Models

Reading the Data

    • 1 Videos
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1 Videos

  • Reading the Data

Implementing Linear Models for Image Classification

    • 2 Videos
    • 1 ExternalTool
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1 External Tool

  • Lab: Classifying Images with a Linear Model

2 Videos

  • Implementing Linear Models for Image Classification
  • Lab Introduction - Classifying Images with a Linear Model

Neural Networks and Deep Neural Networks for Image Classification

    • 2 Videos
    • 1 ExternalTool
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1 External Tool

  • Lab: Classifying Images with a NN and DNN Model

2 Videos

  • Neural Networks and Deep Neural Networks for Image Classification
  • Lab Introduction - Classifying Images with a NN and DNN Model

Deep Neural Networks with Dropout and Batch Normalization

    • 2 Videos
    • 1 Readings
    • 1 ExternalTool
    • 1 Assignment
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1 Assignment

  • New Quiz

1 External Tool

  • Lab: Classifying Images using Dropout and Batchnorm Layer

2 Videos

  • Deep Neural Networks with Dropout and Batch Normalization
  • Lab Introduction - Classifying Images using Dropout and Batchnorm Layer

1 Readings

  • Readings

Introduction

    • 1 Videos
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1 Videos

  • Introduction

Convolutional Neural Networks

    • 1 Videos
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1 Videos

  • Convolutional Neural Networks

Understanding Convolutions

    • 1 Videos
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1 Videos

  • Understanding Convolutions

CNN Model Parameters

    • 1 Videos
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1 Videos

  • CNN Model Parameters

Working with Pooling Layers

    • 1 Videos
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1 Videos

  • Working with Pooling Layers

Implementing CNNs on Vertex AI by using a pre-built TF container

    • 2 Videos
    • 1 Readings
    • 1 ExternalTool
    • 1 Assignment
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1 Assignment

  • Quiz

1 External Tool

  • Lab: Classifying Images with pre-built TF Container on Vertex AI

2 Videos

  • Implementing CNNs on Vertex AI by using a pre-built TF container
  • Lab Introduction - Classifying Images with pre-built TF Container on Vertex AI

1 Readings

  • Readings

Introduction

    • 1 Videos
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1 Videos

  • Introduction

Preprocessing the image data

    • 1 Videos
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1 Videos

  • Preprocessing the Image data

Model parameters and the data scarcity problem

    • 1 Videos
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1 Videos

  • Model Parameters and the Data Scarcity Problem

Data Augmentation

    • 2 Videos
    • 1 ExternalTool
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1 External Tool

  • Lab: Classifying Images using Data Augmentation

2 Videos

  • Data Augmentation
  • Lab Introduction - Classifying Images using Data Augmentation

Transfer Learning

    • 2 Videos
    • 1 Readings
    • 1 ExternalTool
    • 1 Assignment
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1 Assignment

  • Quiz

1 External Tool

  • Lab: Classifying Images with Transfer Learning

2 Videos

  • Transfer Learning
  • Lab Introduction - Classifying Images with Transfer Learning

1 Readings

  • Readings

Summary

    • 1 Videos
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1 Videos

  • Summary

Auto Summary

"Computer Vision Fundamentals with Google Cloud" focuses on computer vision use cases and machine learning strategies, from pre-built ML models to custom classifiers using DNNs and CNNs. Taught by Coursera, it covers model accuracy, data augmentation, and hyperparameter tuning. Learners get hands-on practice with public datasets. Duration: 1140 minutes. Subscription options: Starter, Professional, Paid. Ideal for expert-level learners in Data Science & AI.

Google Cloud Training
Instructor

Google Cloud Training