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Google Cloud Training
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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/ykrfa1/computer
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What's inside
Syllabus
Introduction
Course Introduction
Introduction to Computer Vision and Pre-built ML Models for Image Classification
Vertex AI and AutoML Vision on Vertex AI
Read more
Syllabus
Introduction
Course Introduction
Introduction to Computer Vision and Pre-built ML Models for Image Classification
Vertex AI and AutoML Vision on Vertex AI
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Introduces different machine learning strategies for solving various computer vision use cases, including using pre-built ML models and AutoML Vision
Develops learners' understanding of image classification building and optimization, which are core skills in computer vision
Covers essential concepts such as augmentation, feature extraction, and hyperparameter tuning, empowering learners with techniques to enhance model accuracy and avoid overfitting
Provides hands-on practice with image classification models on public datasets, offering practical experience in model development
Requires learners to come in with experience in computer vision or related fields
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Reviews summary
Computer vision fundamentals with google cloud
According to learners, this course provides a solid introduction to computer vision fundamentals, particularly for those interested in leveraging Google Cloud's machine learning tools. Many find the hands-on labs using Vertex AI and AutoML to be a highly valuable aspect, providing practical experience. While the course covers different model types like CNNs, some students feel certain advanced topics could benefit from greater depth. It is also noted that having prior knowledge in machine learning or Python is beneficial to fully grasp the material, suggesting it might be more challenging for absolute beginners. Overall, it's seen as a practical starting point for applying CV concepts within the Google Cloud ecosystem.
Course heavily centers on Google Cloud services.
"I found the course heavily focused on the Google Cloud platform, which is great if that's your goal for learning computer vision."
"It teaches computer vision through the lens of Google Cloud tools like Vertex AI and AutoML."
"If you aren't interested in Google Cloud services for ML, this might not be the right course for you."
Provides a good overview of CV fundamentals.
"It gave me a solid understanding of computer vision basics and common techniques for image classification."
"A good starting point if you're new to computer vision or Google Cloud ML tools."
"I appreciated the overview of different model types from linear models to CNNs."
Hands-on labs provide valuable practice.
"The labs were the most valuable part for me, giving me hands-on experience with Vertex AI."
"Working through the practical exercises really solidified the concepts taught in the lectures."
"The hands-on parts using the Google Cloud environment are key to understanding how to apply the theory."
Learn to use Google Cloud CV tools.
"Learning how to use AutoML Vision and custom training on Vertex AI was very practical for my work."
"The sections on Google Cloud's specific tools like Vertex AI were highly relevant."
"Covers Vertex AI features specifically for computer vision tasks like image classification."
Some advanced topics could be deeper.
"While it covered CNNs, I felt some of the more advanced model architectures or training techniques could have been explored in greater depth."
"The pace felt a bit fast in certain modules, especially if you're not familiar with the concepts."
"Could use more in-depth coverage on complex topics or optimization techniques for models."
Beneficial to have prior ML/Python knowledge.
"You really need some background in machine learning concepts or Python programming to follow easily."
"Found it challenging without prior cloud experience; some stated prerequisites would help manage expectations."
"Assumes some level of comfort with basic ML concepts and data handling before diving in."
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:
Build Basics of Computer Vision
Show steps
Reinforce your understanding of the foundational concepts and techniques in computer vision, particularly image classification.
Browse courses on
Computer Vision
Show steps
-
Review the fundamentals of computer vision
-
Explore basic image processing operations
-
Get familiar with common image classification algorithms
Practice Image Classification with Pre-built ML APIs
Show steps
Deepen your understanding of pre-built ML APIs by exploring practical applications in image classification using platforms like Vertex AI and AutoML Vision.
Browse courses on
Image Classification
Show steps
-
Choose a pre-built ML API for image classification
-
Walk through API documentation and code samples
-
Develop a simple image classification application
Experiment with Image Data Augmentation
Show steps
Enhance your image classification skills by experimenting with data augmentation techniques to generate more diverse training data and improve model performance.
Show steps
-
Explore different data augmentation methods
-
Apply augmentation techniques to prepare image datasets
-
Evaluate the impact of augmentation on model accuracy
Five other activities
Expand to see all activities and additional details
Show all eight activities
Extract Features for Image Classification
Show steps
Gain proficiency in extracting meaningful features from images to improve the accuracy and efficiency of your image classification models.
Browse courses on
Image Classification
Show steps
-
Learn techniques for feature extraction
-
Apply feature extraction to extract representative features from images
-
Evaluate the impact of feature extraction on model performance
Develop a Custom Image Classifier
Show steps
Apply your knowledge to build a custom image classifier using machine learning techniques. Experiment with different models and hyperparameters to optimize accuracy.
Browse courses on
Image Classification
Show steps
-
Choose a suitable machine learning model
-
Train and evaluate the model on various image datasets
-
Fine-tune the model to improve its performance
Build an Image Classification Application
Show steps
Consolidate your learning by applying your skills to build a practical image classification application that addresses a specific problem or need.
Browse courses on
Image Classification
Show steps
-
Identify an application domain for the image classifier
-
Design and develop the application using appropriate technologies
-
Test and deploy the application
Attend a Workshop on Advanced Computer Vision Techniques
Show steps
Expand your knowledge and skills by attending a workshop focused on advanced computer vision techniques, ensuring you stay up-to-date with the latest advancements in the field.
Browse courses on
Computer Vision
Show steps
-
Identify and register for a relevant workshop
-
Attend the workshop and actively participate
-
Network with experts and fellow attendees
Contribute to Open-Source Computer Vision Projects
Show steps
Enrich your understanding and gain practical experience by contributing to open-source computer vision projects, engaging with a global community of developers and researchers.
Browse courses on
Computer Vision
Show steps
-
Explore open-source computer vision projects
-
Identify ways to contribute based on your skills
-
Fork and make meaningful contributions to selected projects
Build Basics of Computer Vision
Show steps
Reinforce your understanding of the foundational concepts and techniques in computer vision, particularly image classification.
Browse courses on
Computer Vision
Show steps
Show steps
Show steps
- Review the fundamentals of computer vision
- Explore basic image processing operations
- Get familiar with common image classification algorithms
Practice Image Classification with Pre-built ML APIs
Show steps
Deepen your understanding of pre-built ML APIs by exploring practical applications in image classification using platforms like Vertex AI and AutoML Vision.
Browse courses on
Image Classification
Show steps
Show steps
Show steps
- Choose a pre-built ML API for image classification
- Walk through API documentation and code samples
- Develop a simple image classification application
Experiment with Image Data Augmentation
Show steps
Enhance your image classification skills by experimenting with data augmentation techniques to generate more diverse training data and improve model performance.
Show steps
Show steps
Show steps
- Explore different data augmentation methods
- Apply augmentation techniques to prepare image datasets
- Evaluate the impact of augmentation on model accuracy
Five other activities
Expand to see all activities and additional details
Show all eight activities
Extract Features for Image Classification
Show steps
Gain proficiency in extracting meaningful features from images to improve the accuracy and efficiency of your image classification models.
Browse courses on
Image Classification
Show steps
Show steps
Show steps
- Learn techniques for feature extraction
- Apply feature extraction to extract representative features from images
- Evaluate the impact of feature extraction on model performance
Develop a Custom Image Classifier
Show steps
Apply your knowledge to build a custom image classifier using machine learning techniques. Experiment with different models and hyperparameters to optimize accuracy.
Browse courses on
Image Classification
Show steps
Show steps
Show steps
- Choose a suitable machine learning model
- Train and evaluate the model on various image datasets
- Fine-tune the model to improve its performance
Build an Image Classification Application
Show steps
Consolidate your learning by applying your skills to build a practical image classification application that addresses a specific problem or need.
Browse courses on
Image Classification
Show steps
Show steps
Show steps
- Identify an application domain for the image classifier
- Design and develop the application using appropriate technologies
- Test and deploy the application
Attend a Workshop on Advanced Computer Vision Techniques
Show steps
Expand your knowledge and skills by attending a workshop focused on advanced computer vision techniques, ensuring you stay up-to-date with the latest advancements in the field.
Browse courses on
Computer Vision
Show steps
Show steps
Show steps
- Identify and register for a relevant workshop
- Attend the workshop and actively participate
- Network with experts and fellow attendees
Contribute to Open-Source Computer Vision Projects
Show steps
Enrich your understanding and gain practical experience by contributing to open-source computer vision projects, engaging with a global community of developers and researchers.
Browse courses on
Computer Vision
Show steps
Show steps
Show steps
- Explore open-source computer vision projects
- Identify ways to contribute based on your skills
- Fork and make meaningful contributions to selected projects
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 focus on the design and development of computer systems that can interpret and understand visual data. The course will introduce learners to different types of computer vision use cases as well as machine learning strategies for solving these use cases. Furthermore, the content on image classification and dealing with image data is very relevant to this field.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers use their expertise to work with large, distributed systems to solve complex problems for businesses of all types. The course provides an introduction to different types of computer vision use cases as well as machine learning strategies for solving these use cases. This would be an important foundation for a Machine Learning Engineer to grasp computer vision related use cases. The course will also teach how to improve a model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters, which are essential skills for a Machine Learning Engineer to master.
Data Scientist
Data Scientist
Data Scientists are responsible for collecting and analyzing data, and using that data to solve real-world problems. The course will introduce Data Scientists to computer vision use cases and machine learning strategies for solving computer vision related problems. Furthermore, Data Scientists will find the content on improving a model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters to be invaluable.
Software Engineer
Software Engineer
Software Engineers will find the course's focus on computer vision and machine learning strategies for solving computer vision related problems to be a useful tool in their repertoire. As a Software Engineer, being able to improve a model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters can help improve the accuracy of complex software systems.
Data Analyst
Data Analyst
Data Analysts play a vital role in helping businesses make informed decisions. The course will introduce Data Analysts to computer vision use cases and machine learning strategies for solving these use cases. Furthermore, Data Analysts will find the content on improving a model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters to be useful in their work.
Research Scientist
Research Scientist
Research Scientists play a vital role in advancing our understanding of the world around us, particularly those within the field of computer vision. This course will help build a foundation for Research Scientists to explore machine learning strategies for solving computer vision use cases. Additionally, the content on improving a model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters will be valuable as Research Scientists seek to refine their models.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze data and make predictions. The course will introduce Quantitative Analysts to computer vision use cases and machine learning strategies for solving these use cases. Additionally, the content on improving a model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters will be useful in their work.
Business Analyst
Business Analyst
Business Analysts help organizations improve their performance by analyzing data and identifying opportunities for improvement. The course will introduce Business Analysts to computer vision use cases and machine learning strategies for solving these use cases. Additionally, the content on improving a model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters will be useful in their work.
Product Manager
Product Manager
Product Managers oversee the development and launch of new products. The course will help Product Managers understand the potential of computer vision and machine learning in product development. Additionally, the content on improving a model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters can help Product Managers make informed decisions about product specifications.
Consultant
Consultant
Consultants provide advice and guidance to businesses on a wide range of topics. The course will help Consultants understand the potential of computer vision and machine learning in business. Additionally, the content on improving a model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters can help Consultants make informed recommendations to their clients.
Technical Writer
Technical Writer
Technical Writers create documentation and other materials to explain complex technical concepts. The course may be useful for Technical Writers who need to explain computer vision and machine learning concepts to non-technical audiences. Additionally, the content on improving a model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters may be useful for Technical Writers who need to explain these concepts in a clear and concise way.
Teacher
Teacher
Teachers help students learn and grow. The course may be useful for Teachers who want to incorporate computer vision and machine learning into their lessons. Additionally, the content on improving a model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters may be useful for Teachers who want to help their students understand these concepts.
Entrepreneur
Entrepreneur
Entrepreneurs start and run their own businesses. The course may be useful for Entrepreneurs who want to use computer vision and machine learning in their businesses. Additionally, the content on improving a model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters may be useful for Entrepreneurs who want to develop products or services that use these technologies.
Manager
Manager
Managers oversee the work of others. The course may be useful for Managers who want to understand the potential of computer vision and machine learning in their organizations. Additionally, the content on improving a model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters may be useful for Managers who want to make informed decisions about using these technologies.
Student
Student
Students are always learning and growing. The course may be useful for Students who want to learn about computer vision and machine learning. Additionally, the content on improving a model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters may be useful for Students who want to improve their understanding of these concepts.
For more career information including salaries, visit:
OpenCourser.com/course/ykrfa1/computer
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.
Save
Comprehensive introduction to computer vision algorithms and applications, including image processing, feature detection, object recognition, and image understanding.
Advanced textbook on computer vision, covering topics such as image formation, feature detection, object recognition, and image understanding.
In-depth guide to deep learning for vision systems, covering topics such as convolutional neural networks, object detection, and image segmentation.
Theoretical and practical introduction to computer vision, covering topics such as image formation, feature extraction, and object recognition.
Save
Advanced textbook on deep learning for image processing, covering topics such as convolutional neural networks, object detection, and image segmentation.
Introductory textbook on computer vision, covering topics such as image formation, feature detection, object recognition, and image understanding.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ykrfa1/computer
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Effort
5 - 7 hours per week
Level
Advanced
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
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