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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.
Read more
This course describes different types of computer vision use cases and then highlights different machine learning strategies for solving these use cases.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/inyedm/computer
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
Read more
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
Read more
Convolutional Neural Networks
Dealing with Image Data
Summary
Good to know
Good to know
Know what's good ,
what to watch for , 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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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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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.
Review basic computer vision concepts
Show steps
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
Show steps
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
Show steps
Organizing your notes and study materials will make it easier to review and retain the course material.
Browse courses on
Note-Taking
Show steps
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
Joining a study group or online forum will allow you to connect with other students and discuss the course material.
Show steps
Show steps
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
Show steps
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
Show steps
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
Show steps
Participating in a Kaggle competition will challenge you to apply your skills and knowledge to real-world problems.
Browse courses on
Kaggle
Show steps
Show steps
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 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.
Machine Learning 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.
Data Scientist
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.
Software Engineer
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.
Product Manager
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.
Business Analyst
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.
Operations Research 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.
Financial 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.
Market Researcher
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.
User Experience Designer
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.
Technical Writer
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.
Sales Engineer
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.
Project Manager
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.
Quality Assurance Analyst
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.
Business Development Manager
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.
For more career information including salaries, visit:
OpenCourser.com/course/inyedm/computer
Reading list
We've selected ten 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.
Save
This textbook exhaustively covers fundamental computer vision algorithms and provides a comprehensive overview of the field. It is recommended for those who seek in-depth knowledge of computer vision and wish to gain a strong foundation in the underlying algorithms.
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.
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.
Offers a comprehensive introduction to statistical learning methods. It covers topics such as linear regression, logistic regression, and support vector machines. While not specifically focused on computer vision, it provides a strong foundation for understanding the statistical principles underlying machine learning models used in computer vision.
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.
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.
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.
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.
This textbook provides a comprehensive overview of pattern recognition and image analysis techniques. It covers topics such as image processing, feature extraction, and classification. While not specifically focused on computer vision, it offers a strong foundation for understanding the underlying principles used in computer vision.
Provides a comprehensive overview of computer vision algorithms and techniques. It covers topics such as image processing, feature extraction, and object recognition. While not specifically focused on a particular programming language or framework, it offers a solid foundation for understanding the underlying principles used in computer vision.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/inyedm/computer
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Time
187 hours
Level
Intermediate
Via
Pluralsight
Instructor
Google Cloud
Language
English
Good to know
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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