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Computer Vision Fundamentals with Google Cloud

Google Cloud

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

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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.

This course is no longer available. Find something similar by browsing:
Computer Vision Machine Learning Image Classification Linear Models Neural Networks Deep Learning AutoML

What's inside

Syllabus

Introduction
Introduction to Computer Vision and Pre-built ML Models for Image Classification
Vertex AI and AutoML Vision on Vertex AI
Custom Training with Linear, Neural Network and Deep Neural Network models
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Teaches learners how to classify images with ML models, beginning with pre-built models and moving toward custom models using linear, neural network, and deep neural network models
Examines topics in a range of complexity from introductory to advanced and from pre-built ML models all the way through to custom image classifiers using deep neural networks
Provides a comprehensive overview of computer vision concepts, strategies, and practical implementation issues
Covers how to enhance image classification model performance with data augmentation and tuning techniques
Offers hands-on labs for learners to build and optimize their own image classification models

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Reviews summary

Practical computer vision on google cloud

According to students, this course offers a solid introduction (positive) to Computer Vision fundamentals (positive) with a strong emphasis on Google Cloud Platform tools (positive) like Vertex AI and AutoML Vision (positive). Learners frequently praise the hands-on labs (positive) for providing practical experience, though some note that keeping up with platform updates can be a challenge (warning), leading to occasional outdated lab instructions (negative). It is generally considered ideal for those new to CV on GCP (positive) but might require some prior ML or Python knowledge (warning) for a smoother experience. The coverage of DNNs and CNNs (neutral) is foundational, yet some desire more advanced project work (warning).
Provides a strong base, but advanced topics may require further study.
"The course is an excellent introduction, but for deep dives into specific CNN architectures or advanced optimization, I'll need more resources."
"It covers a broad range of topics adequately but doesn't go extremely deep into the mathematical underpinnings of all algorithms."
"I feel confident in applying the basics of computer vision, though for very complex or research-level use cases, this course is just the starting point."
Effectively teaches computer vision using Google Cloud services.
"The focus on Vertex AI and AutoML Vision makes this course highly relevant for anyone working or planning to work with GCP."
"I found the sections on integrating computer vision with Google Cloud's pre-built ML APIs particularly valuable and efficient."
"It's great to see how everything ties into the Google Cloud ecosystem, making the learned skills immediately applicable to real projects."
Delivers a solid understanding of core computer vision principles.
"This course provided a fantastic overview of computer vision, from pre-built APIs to building custom CNNs, very clearly explained."
"I really benefited from the clear explanations of linear models, DNNs, and CNNs for image classification techniques."
"The introduction to different computer vision use cases was very helpful in setting the stage for practical applications."
Provides valuable practical experience with real-world applications.
"The labs are really the strongest part of this course; I found them incredibly useful for applying the concepts."
"I appreciate the practical approach, especially with the image classification models and public datasets provided for practice."
"Getting hands-on with Vertex AI in the labs really solidified my understanding of Google Cloud's offerings and workflow."
Benefits those with a basic background in Python or machine learning.
"While it's labeled 'fundamentals,' I felt more comfortable having some prior Python programming and basic ML concepts under my belt."
"For absolute beginners with no coding or machine learning experience, some sections might move a bit fast without prior exposure."
"It's not truly for someone with zero background; a basic understanding of coding and data science helps immensely to keep pace."
Some content or labs may require adjustments due to platform evolution.
"A few of the lab steps felt slightly outdated or required minor tweaks, likely due to frequent updates on the Google Cloud platform itself."
"I had to troubleshoot some commands because Google Cloud sometimes changes their interface or specific API calls after the course was made."
"While the core concepts are sound, I recommend cross-referencing with the latest GCP documentation for the labs to ensure smooth execution."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Computer Vision Fundamentals with Google Cloud with these activities:
Review basic computer vision concepts
Reviewing basic computer vision concepts will provide you with a foundation for the more advanced topics covered in the course.
Browse courses on Computer Vision
Show steps
  • Read articles or books on basic computer vision concepts.
  • Watch videos or tutorials on computer vision.
  • Complete online quizzes or exercises on computer vision.
Organize your notes and study materials
Organizing your notes and study materials will make it easier to review and retain the course material.
Browse courses on Note-Taking
Show steps
  • Review your notes and identify key concepts and ideas.
  • Create a system for organizing your notes, such as using folders or tags.
  • Regularly review your organized notes to reinforce your understanding.
Review linear algebra and calculus
Refreshing your understanding of linear algebra and calculus will provide you with a solid foundation for the course material.
Browse courses on Linear Algebra
Show steps
  • Review notes and textbooks from previous courses.
  • Solve practice problems to test your understanding.
  • Attend a refresher workshop or online course.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Complete the TensorFlow tutorials
Working through the TensorFlow tutorials will give you hands-on experience with the tools and techniques used in the course.
Browse courses on TensorFlow
Show steps
  • Follow the step-by-step instructions in the TensorFlow tutorials.
  • Build and train your own simple neural network models.
  • Experiment with different hyperparameters to see how they affect the model's performance.
Complete the PyTorch tutorials
Working through the PyTorch tutorials will give you hands-on experience with the tools and techniques used in the course.
Browse courses on PyTorch
Show steps
  • Follow the step-by-step instructions in the PyTorch tutorials.
  • Build and train your own simple neural network models.
  • Experiment with different hyperparameters to see how they affect the model's performance.
Join a study group or online forum
Joining a study group or online forum will allow you to connect with other students and discuss the course material.
Show steps
  • Find a study group or online forum related to the course.
  • Participate in discussions and ask questions.
  • Help other students with their understanding of the material.
Build a simple image classification model
Building a simple image classification model will help you understand the concepts and techniques covered in the course.
Browse courses on Image Classification
Show steps
  • Gather a dataset of images.
  • Preprocess the images and split them into training and testing sets.
  • Build and train a simple neural network model for image classification.
  • Evaluate the performance of your model using the testing set.
Participate in a Kaggle competition
Participating in a Kaggle competition will challenge you to apply your skills and knowledge to real-world problems.
Browse courses on Kaggle
Show steps
  • Find a Kaggle competition that interests you.
  • Download the data and familiarize yourself with the problem.
  • Build and train a model to solve the problem.
  • Submit your model and track your progress on the leaderboard.

Career center

Learners who complete Computer Vision Fundamentals with Google Cloud will develop knowledge and skills that may be useful to these careers:
Computer Vision Engineer
Computer Vision Engineers design, develop, test, and evaluate computer vision systems. These systems can be used for a variety of purposes, such as image recognition, object detection, and tracking. This course can help you build a foundation in computer vision and machine learning, which are essential skills for Computer Vision Engineers. The course will also introduce you to different computer vision use cases and machine learning strategies, which will help you prepare for a career in this field.
See salaries and explore the career path for Computer Vision Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and evaluate machine learning systems. These systems can be used for a variety of purposes, such as data analysis, prediction, and classification. This course can help you build a foundation in machine learning, which is an essential skill for Machine Learning Engineers. The course will also introduce you to different machine learning strategies, which will help you prepare for a career in this field.
See salaries and explore the career path for Machine Learning Engineer
Data Scientist
Data Scientists use data to solve problems and make decisions. They use a variety of techniques, including machine learning, statistics, and data analysis. This course can help you build a foundation in data science, which is an essential skill for Data Scientists. The course will also introduce you to different machine learning strategies, which will help you prepare for a career in this field.
See salaries and explore the career path for Data Scientist
Software Engineer
Software Engineers design, develop, and maintain software applications. They use a variety of programming languages and technologies to create software that meets the needs of users. This course can help you build a foundation in computer vision and machine learning, which are increasingly important skills for Software Engineers. The course will also introduce you to different software development tools and techniques, which will help you prepare for a career in this field.
See salaries and explore the career path for Software Engineer
Product Manager
Product Managers are responsible for the development and marketing of products. They work with engineers, designers, and marketing teams to bring products to market. This course can help you build a foundation in computer vision and machine learning, which are increasingly important skills for Product Managers. The course will also introduce you to different product development and marketing strategies, which will help you prepare for a career in this field.
See salaries and explore the career path for Product Manager
Business Analyst
Business Analysts help organizations to improve their business processes. They use a variety of techniques, including data analysis, process mapping, and interviewing. This course can help you build a foundation in computer vision and machine learning, which are increasingly important skills for Business Analysts. The course will also introduce you to different business analysis techniques, which will help you prepare for a career in this field.
See salaries and explore the career path for Business Analyst
Operations Research Analyst
Operations Research Analysts use mathematical models to solve problems in a variety of industries. They use techniques such as linear programming, optimization, and simulation. This course can help you build a foundation in computer vision and machine learning, which are increasingly important skills for Operations Research Analysts. The course will also introduce you to different operations research techniques, which will help you prepare for a career in this field.
See salaries and explore the career path for Operations Research Analyst
Financial Analyst
Financial Analysts use financial data to make investment decisions. They use a variety of techniques, including financial modeling, data analysis, and forecasting. This course can help you build a foundation in computer vision and machine learning, which are increasingly important skills for Financial Analysts. The course will also introduce you to different financial analysis techniques, which will help you prepare for a career in this field.
See salaries and explore the career path for Financial Analyst
Market Researcher
Market Researchers conduct research to understand consumer behavior. They use a variety of techniques, including surveys, focus groups, and data analysis. This course can help you build a foundation in computer vision and machine learning, which are increasingly important skills for Market Researchers. The course will also introduce you to different market research techniques, which will help you prepare for a career in this field.
See salaries and explore the career path for Market Researcher
User Experience Designer
User Experience Designers design the user interface for websites and applications. They use a variety of techniques, including user research, prototyping, and testing. This course can help you build a foundation in computer vision and machine learning, which are increasingly important skills for User Experience Designers. The course will also introduce you to different user experience design techniques, which will help you prepare for a career in this field.
See salaries and explore the career path for User Experience Designer
Technical Writer
Technical Writers create documentation for software and other technical products. They use a variety of writing skills, including technical writing, editing, and proofreading. This course can help you build a foundation in computer vision and machine learning, which are increasingly important skills for Technical Writers. The course will also introduce you to different technical writing techniques, which will help you prepare for a career in this field.
See salaries and explore the career path for Technical Writer
Sales Engineer
Sales Engineers help customers to understand and use technical products. They use a variety of sales techniques, including product demonstrations, presentations, and negotiations. This course can help you build a foundation in computer vision and machine learning, which are increasingly important skills for Sales Engineers. The course will also introduce you to different sales techniques, which will help you prepare for a career in this field.
See salaries and explore the career path for Sales Engineer
Project Manager
Project Managers plan and execute projects. They use a variety of project management techniques, including planning, scheduling, and budgeting. This course can help you build a foundation in computer vision and machine learning, which are increasingly important skills for Project Managers. The course will also introduce you to different project management techniques, which will help you prepare for a career in this field.
See salaries and explore the career path for Project Manager
Quality Assurance Analyst
Quality Assurance Analysts test software and other products to ensure that they meet quality standards. They use a variety of testing techniques, including functional testing, performance testing, and security testing. This course can help you build a foundation in computer vision and machine learning, which are increasingly important skills for Quality Assurance Analysts. The course will also introduce you to different quality assurance testing techniques, which will help you prepare for a career in this field.
See salaries and explore the career path for Quality Assurance Analyst
Business Development Manager
Business Development Managers identify and develop new business opportunities. They use a variety of business development techniques, including market research, networking, and sales. This course can help you build a foundation in computer vision and machine learning, which are increasingly important skills for Business Development Managers. The course will also introduce you to different business development techniques, which will help you prepare for a career in this field.
See salaries and explore the career path for Business Development Manager

Reading list

We've selected six books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Computer Vision Fundamentals with Google Cloud.
Cover image
Concurrent NumPy in Python
Save
Provides a practical introduction to deep learning for computer vision tasks. It covers essential concepts, architectures, and techniques for building and deploying deep learning models for image classification, object detection, and other computer vision applications.
Concurrent NumPy in Python: Faster NumPy With BLAS,...
Paperback
$$
Concurrent NumPy in Python: Faster NumPy With BLAS,...
Kindle Edition
$$
Cover image
Hybrid Imaging and Visualization
Save
Offers a hands-on approach to computer vision using Python. It guides readers through building practical computer vision applications, covering topics such as image processing, feature extraction, and object detection.
Hybrid Imaging and Visualization: Employing Machine...
Kindle Edition
$$$$
Cover image
Introduction to Applied Linear Algebra
Save
Provides a comprehensive overview of convex optimization theory and algorithms. While not specifically focused on computer vision, it offers a strong foundation for understanding the optimization techniques used in training machine learning models for computer vision.
Introduction to Applied Linear Algebra: Vectors,...
Hardcover
$$
Introduction to Applied Linear Algebra: Vectors,...
Kindle Edition
$$
Cover image
Pattern Recognition and Machine Learning
Save
Offers a comprehensive overview of pattern recognition and machine learning. It covers topics such as generative models, Bayesian inference, and supervised learning. While not specifically focused on computer vision, it provides a solid foundation for understanding the underlying principles of machine learning used in computer vision.
Pattern Recognition and Machine Learning...
Hardcover
$$$
Pattern Recognition and Machine Learning...
Paperback
$$$
Pattern Recognition and Machine Learning (text...
Unknown Binding
$$$$
Cover image
Probability and Statistics for Computer Science
Save
This textbook provides a comprehensive overview of computer vision algorithms and techniques. It is recommended for those who seek in-depth knowledge of computer vision and wish to gain a strong foundation in the underlying algorithms.
Probability and Statistics for Computer Science
Paperback
$$$
Probability and Statistics for Computer Science
Kindle Edition
$$$
Cover image
Introduction to Biometrics
Save
Provides a comprehensive overview of machine learning algorithms and techniques for computer vision. It covers topics such as supervised learning, unsupervised learning, and deep learning. While not specifically focused on a particular programming language, it offers a solid foundation for understanding the underlying principles of machine learning used in computer vision.
Introduction to Biometrics (Texts in Computer...
Hardcover
$$$
Introduction to Biometrics (Texts in Computer...
Kindle Edition
$$$

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Course image
Time
187 hours
Level
Intermediate
Via
Pluralsight
Instructor
Google Cloud
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Teaches learners how to classify images with ML models, beginning with pre-built models and moving toward custom models using linear, neural network, and deep neural network models
Examines topics in a range of complexity from introductory to advanced and from pre-built ML models all the way through to custom image classifiers using deep neural networks
Provides a comprehensive overview of computer vision concepts, strategies, and practical implementation issues
Covers how to enhance image classification model performance with data augmentation and tuning techniques
Offers hands-on labs for learners to build and optimize their own image classification models
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