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Amit Yadav
In this 1.5-hour long project-based course, you will learn how to apply image data augmentation in Keras. We are going to focus on using the ImageDataGenerator class from Keras’ image preprocessing package, and will take a look at a variety of options available in this class for data augmentation and data normalization.
Read more
In this 1.5-hour long project-based course, you will learn how to apply image data augmentation in Keras. We are going to focus on using the ImageDataGenerator class from Keras’ image preprocessing package, and will take a look at a variety of options available in this class for data augmentation and data normalization.
Read more
In this 1.5-hour long project-based course, you will learn how to apply image data augmentation in Keras. We are going to focus on using the ImageDataGenerator class from Keras’ image preprocessing package, and will take a look at a variety of options available in this class for data augmentation and data normalization.
Since this is a practical, project-based course, you will need to prior experience with Python programming, convolutional neural networks, and Keras with a TensorFlow backend.
Data augmentation is a technique used to create more examples, artificially, from an existing dataset. This is useful if your dataset is small and you want to increase the number of examples. Data augmentation can often solve over-fitting so that your model generalizes well after training. For images, a variety of augmentation can be applied to increase the number of examples.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/8v78h0/image
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What's inside
Syllabus
Image Data Augmentation with Keras
In this 1.5-hour long project-based course, you will learn how to apply image data augmentation in Keras. We are going to focus on using the ImageDataGenerator class from Keras’ image preprocessing package, and will take a look at a variety of options available in this class for data augmentation and data normalization. Since this is a practical, project-based course, you will need prior experience with Python programming, convolutional neural networks, and Keras with a TensorFlow backend. Data augmentation is a technique used to create more examples, artificially, from an existing dataset. This is useful if your dataset is small and you want to increase the number of examples. Data augmentation can often solve over-fitting so that your model generalizes well after training. For images, a variety of augmentation can be applied to increase the number of examples.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Intended for students who want to augment their ability to build complex models in Keras for computer vision tasks
Not recommended for those with no experience in computer vision or data augmentation
May not align with beginners who look to learn image data augmentation from scratch
Save this course
Save Image Data Augmentation with Keras to your list so you can find it easily later:
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Reviews summary
Informative and practical image data augmentation course
Learners say this course is full of informative, practical, and useful content, especially for beginners. Hands-on assignments and clear explanations from the expert instructor, Amit Yadav, make this an engaging and valuable learning experience. Students appreciate the real-world applications of data augmentation techniques provided in this course.
The course is beginner-friendly.
"Perfect course for beginners, requires very little base to start."
"Highly recommend it, certainly worth the time."
"A great course for beginners getting into the underlying concepts just enough and not go very deep yet be very clear with the explanation."
Students had a positive experience with the course.
"I was very gradfully for this study"
"It was educative i really learn a lot about data augmentation"
"I felt the hands-on Interface screen was pretty much slow."
The instructor, Amit Yadav, is clear and engaging.
"An excellent course, Mr. Amit Yadav explains very well."
"Amit sir has explained all the necessary concepts very briefly"
"The instructor, Amit Yadav, is very clear in his instruction and provide great explanation on his model building, and compiling."
The content is informative and useful.
"Learnt a new and very useful skill of image data augmentation"
"Really enjoyable and satisfied with the instructor and the course content and course materials as well."
"For me, it's a little short because I love to understand the stuff under the hood."
Some learners experienced technical issues.
"The virtual environment is too slow."
"Course Demonstration was very good considering the scope."
"The cloud editing was continuously lagging extremely."
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 Image Data Augmentation with Keras with these
activities:
Gather Course Materials
Show steps
Familiarize yourself with the course by gathering any notes templates, code base, starter files, supplemental materials, or assignments in one organized location.
Show steps
-
Create a new digital folder on your computer.
-
Organize materials and rename files to reflect your own naming conventions so that they are easy to search and review later.
-
Locate any kickoff quizzes, assignments, or announcements and complete them as soon as possible.
Review your notes on image processing
Show steps
Refresh your memory on the fundamentals of image processing before starting the course.
Browse courses on
Image Processing
Show steps
-
Gather your notes on image processing
-
Review your notes
Read 'Deep Learning with Python'
Show steps
Understand the theoretical foundations of deep learning and gain practical experience with Keras.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
-
Read chapters 1-5
-
Complete the exercises in chapters 1-5
-
Read chapters 6-10
-
Complete the exercises in chapters 6-10
-
Complete the final project
13 other activities
Expand to see all activities and additional details
Show all 16 activities
Create a study guide for the course
Show steps
Improve your understanding and retention of the course material by creating a study guide.
Show steps
-
Gather your notes, assignments, and quizzes
-
Organize your materials into a logical order
-
Create a study guide
Complete the Keras ImageDataGenerator tutorial
Show steps
Learn how to use the Keras ImageDataGenerator class to augment your images for training.
Show steps
-
Read the Keras ImageDataGenerator documentation
-
Follow the Keras ImageDataGenerator tutorial
Practice applying data augmentation techniques
Show steps
Complete these drills to practice applying the data augmentation methods covered in the course.
Show steps
-
Find a dataset appropriate for your task
-
Choose the data augmentation techniques you want to practice
-
Apply the data augmentation techniques to the dataset
Practice Image Augmentation
Show steps
Build familiarity with the built-in parameters for image augmentation in Keras by working through several examples.
Show steps
-
Create a new Python script file.
-
Load the sample images and create a data generator using the ImageDataGenerator class.
-
Experiment by setting the rotation range, width shift range, height shift range, and zoom range to different values.
-
Generate augmented images and visualize them to see how they have been transformed.
-
Save your modified data generator for later use.
Practice using the Keras ImageDataGenerator class
Show steps
Reinforce your understanding of the Keras ImageDataGenerator class through hands-on practice.
Show steps
-
Create a dataset of images
-
Create an ImageDataGenerator object
-
Augment the images in the dataset
-
Train a model using the augmented images
Attend a meetup on image processing
Show steps
Connect with other professionals in the field of image processing and learn about the latest trends.
Show steps
-
Find a meetup on image processing
-
Attend the meetup
Volunteer at a local AI club
Show steps
Gain practical experience with image data augmentation by helping others learn about it.
Show steps
-
Find a local AI club
-
Contact the club and offer to volunteer
-
Help out at the club
Explore additional data augmentation libraries
Show steps
Following these tutorials will help you expand your knowledge of data augmentation techniques beyond the course material.
Browse courses on
Data Augmentation
Show steps
-
Identify a data augmentation library
-
Review the documentation and examples
-
Experiment with the library on a sample dataset
Create a blog post about image data augmentation
Show steps
Deepen your understanding of image data augmentation by explaining it to others.
Show steps
-
Choose a topic for your blog post
-
Research image data augmentation
-
Write your blog post
-
Publish your blog post
Follow Advanced Keras Tutorials
Show steps
Expand your knowledge by following additional tutorials on advanced Keras topics.
Browse courses on
Transfer Learning
Show steps
-
Search online for tutorials on topics like transfer learning with Keras, using pre-trained models, or building custom layers.
-
Follow the tutorials step-by-step, making notes and experimenting with the code.
-
Apply what you have learned to your own projects.
-
Share your findings and questions with the online community or in course discussion forums.
Mentor a junior developer on image data augmentation
Show steps
Reinforce your understanding of image data augmentation by teaching it to others.
Show steps
-
Find a junior developer to mentor
-
Create a mentoring plan
-
Meet with the mentee regularly
-
Provide feedback and support
Build a data augmentation pipeline
Show steps
This project will give you hands-on experience in implementing a data augmentation pipeline for a specific task.
Browse courses on
Data Augmentation
Show steps
-
Define the task you want to use data augmentation for
-
Choose the data augmentation techniques appropriate for the task
-
Implement the data augmentation pipeline
-
Evaluate the performance of the data augmentation pipeline
Image Augmentation Project
Show steps
Demonstrate your understanding of image augmentation by building a project that implements these techniques.
Show steps
-
Choose a dataset related to your interests.
-
Load the dataset and apply image augmentation techniques to it.
-
Build a model using Keras and train it on the augmented dataset.
-
Evaluate the performance of your model and compare it to a model trained on the original dataset.
-
Document your project and share your findings.
Gather Course Materials
Show steps
Familiarize yourself with the course by gathering any notes templates, code base, starter files, supplemental materials, or assignments in one organized location.
Show steps
Show steps
Show steps
- Create a new digital folder on your computer.
- Organize materials and rename files to reflect your own naming conventions so that they are easy to search and review later.
- Locate any kickoff quizzes, assignments, or announcements and complete them as soon as possible.
Review your notes on image processing
Show steps
Refresh your memory on the fundamentals of image processing before starting the course.
Browse courses on
Image Processing
Show steps
Show steps
Show steps
- Gather your notes on image processing
- Review your notes
Read 'Deep Learning with Python'
Show steps
Understand the theoretical foundations of deep learning and gain practical experience with Keras.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
Show steps
Show steps
- Read chapters 1-5
- Complete the exercises in chapters 1-5
- Read chapters 6-10
- Complete the exercises in chapters 6-10
- Complete the final project
13 other activities
Expand to see all activities and additional details
Show all 16 activities
Create a study guide for the course
Show steps
Improve your understanding and retention of the course material by creating a study guide.
Show steps
Show steps
Show steps
- Gather your notes, assignments, and quizzes
- Organize your materials into a logical order
- Create a study guide
Complete the Keras ImageDataGenerator tutorial
Show steps
Learn how to use the Keras ImageDataGenerator class to augment your images for training.
Show steps
Show steps
Show steps
- Read the Keras ImageDataGenerator documentation
- Follow the Keras ImageDataGenerator tutorial
Practice applying data augmentation techniques
Show steps
Complete these drills to practice applying the data augmentation methods covered in the course.
Show steps
Show steps
Show steps
- Find a dataset appropriate for your task
- Choose the data augmentation techniques you want to practice
- Apply the data augmentation techniques to the dataset
Practice Image Augmentation
Show steps
Build familiarity with the built-in parameters for image augmentation in Keras by working through several examples.
Show steps
Show steps
Show steps
- Create a new Python script file.
- Load the sample images and create a data generator using the ImageDataGenerator class.
- Experiment by setting the rotation range, width shift range, height shift range, and zoom range to different values.
- Generate augmented images and visualize them to see how they have been transformed.
- Save your modified data generator for later use.
Practice using the Keras ImageDataGenerator class
Show steps
Reinforce your understanding of the Keras ImageDataGenerator class through hands-on practice.
Show steps
Show steps
Show steps
- Create a dataset of images
- Create an ImageDataGenerator object
- Augment the images in the dataset
- Train a model using the augmented images
Attend a meetup on image processing
Show steps
Connect with other professionals in the field of image processing and learn about the latest trends.
Show steps
Show steps
Show steps
- Find a meetup on image processing
- Attend the meetup
Volunteer at a local AI club
Show steps
Gain practical experience with image data augmentation by helping others learn about it.
Show steps
Show steps
Show steps
- Find a local AI club
- Contact the club and offer to volunteer
- Help out at the club
Explore additional data augmentation libraries
Show steps
Following these tutorials will help you expand your knowledge of data augmentation techniques beyond the course material.
Browse courses on
Data Augmentation
Show steps
Show steps
Show steps
- Identify a data augmentation library
- Review the documentation and examples
- Experiment with the library on a sample dataset
Create a blog post about image data augmentation
Show steps
Deepen your understanding of image data augmentation by explaining it to others.
Show steps
Show steps
Show steps
- Choose a topic for your blog post
- Research image data augmentation
- Write your blog post
- Publish your blog post
Follow Advanced Keras Tutorials
Show steps
Expand your knowledge by following additional tutorials on advanced Keras topics.
Browse courses on
Transfer Learning
Show steps
Show steps
Show steps
- Search online for tutorials on topics like transfer learning with Keras, using pre-trained models, or building custom layers.
- Follow the tutorials step-by-step, making notes and experimenting with the code.
- Apply what you have learned to your own projects.
- Share your findings and questions with the online community or in course discussion forums.
Mentor a junior developer on image data augmentation
Show steps
Reinforce your understanding of image data augmentation by teaching it to others.
Show steps
Show steps
Show steps
- Find a junior developer to mentor
- Create a mentoring plan
- Meet with the mentee regularly
- Provide feedback and support
Build a data augmentation pipeline
Show steps
This project will give you hands-on experience in implementing a data augmentation pipeline for a specific task.
Browse courses on
Data Augmentation
Show steps
Show steps
Show steps
- Define the task you want to use data augmentation for
- Choose the data augmentation techniques appropriate for the task
- Implement the data augmentation pipeline
- Evaluate the performance of the data augmentation pipeline
Image Augmentation Project
Show steps
Demonstrate your understanding of image augmentation by building a project that implements these techniques.
Show steps
Show steps
Show steps
- Choose a dataset related to your interests.
- Load the dataset and apply image augmentation techniques to it.
- Build a model using Keras and train it on the augmented dataset.
- Evaluate the performance of your model and compare it to a model trained on the original dataset.
- Document your project and share your findings.
Career center
Learners who complete Image Data Augmentation with Keras will develop knowledge and skills
that may be useful to these careers:
Computer Vision Engineer
Computer Vision Engineer
A Computer Vision Engineer develops and implements computer vision algorithms and systems. They work with a variety of image and video data to develop applications such as facial recognition, object detection, and medical imaging. **Image Data Augmentation with Keras** may be useful to a Computer Vision Engineer interested in working with image data. The course covers a variety of data augmentation techniques that can be used to improve the performance of image-based machine learning models.
Data Scientist
Data Scientist
A Data Scientist uses machine learning and statistical techniques to gather, analyze, and interpret data from a variety of sources. They use this data to develop predictive models and uncover hidden patterns. **Image Data Augmentation with Keras** may be useful to a Data Scientist interested in working with image data. The course covers a variety of data augmentation techniques that can be used to improve the performance of image-based machine learning models.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and deploys machine learning models. They work with data scientists and other engineers to ensure that machine learning models are scalable, efficient, and accurate. **Image Data Augmentation with Keras** may be useful to a Machine Learning Engineer interested in working with image data. The course covers a variety of data augmentation techniques that can be used to improve the performance of image-based machine learning models.
Data Analyst
Data Analyst
A Data Analyst collects, cleans, and analyzes data to identify trends and patterns. They use this information to make recommendations and inform decision-making. **Image Data Augmentation with Keras** may be useful to a Data Analyst interested in working with image data. The course covers a variety of data augmentation techniques that can be used to improve the performance of image-based machine learning models.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. They work with a variety of programming languages and technologies to create applications that meet the needs of users. **Image Data Augmentation with Keras** may be useful to a Software Engineer interested in working with image data. The course covers a variety of data augmentation techniques that can be used to improve the performance of image-based machine learning models.
Product Manager
Product Manager
A Product Manager is responsible for the development and launch of new products. They work with engineers, designers, and marketers to ensure that products meet the needs of users. **Image Data Augmentation with Keras** may be useful to a Product Manager interested in working with image-based products. The course covers a variety of data augmentation techniques that can be used to improve the performance of image-based machine learning models.
Business Analyst
Business Analyst
A Business Analyst helps organizations improve their performance by analyzing data and identifying opportunities for improvement. They work with a variety of stakeholders to develop and implement solutions that meet the needs of the business. **Image Data Augmentation with Keras** may be useful to a Business Analyst interested in working with image data. The course covers a variety of data augmentation techniques that can be used to improve the performance of image-based machine learning models.
Project Manager
Project Manager
A Project Manager is responsible for planning, executing, and closing projects. They work with a variety of stakeholders to ensure that projects are completed on time, within budget, and to the desired quality. **Image Data Augmentation with Keras** may be useful to a Project Manager interested in working with image data. The course covers a variety of data augmentation techniques that can be used to improve the performance of image-based machine learning models.
Marketing Manager
Marketing Manager
A Marketing Manager is responsible for developing and implementing marketing campaigns. They work with a variety of stakeholders to ensure that marketing campaigns are effective and meet the goals of the organization. **Image Data Augmentation with Keras** may be useful to a Marketing Manager interested in using image data in their marketing campaigns. The course covers a variety of data augmentation techniques that can be used to improve the performance of image-based machine learning models.
Sales Manager
Sales Manager
A Sales Manager is responsible for leading and managing a sales team. They work with a variety of stakeholders to ensure that the sales team is meeting its goals and objectives. **Image Data Augmentation with Keras** may be useful to a Sales Manager interested in using image data in their sales presentations. The course covers a variety of data augmentation techniques that can be used to improve the performance of image-based machine learning models.
Customer Success Manager
Customer Success Manager
A Customer Success Manager is responsible for ensuring that customers are satisfied with their products or services. They work with a variety of stakeholders to identify and resolve customer issues. **Image Data Augmentation with Keras** may be useful to a Customer Success Manager interested in using image data to improve customer support. The course covers a variety of data augmentation techniques that can be used to improve the performance of image-based machine learning models.
Technical Writer
Technical Writer
A Technical Writer creates and maintains technical documentation, such as user manuals, white papers, and training materials. They work with a variety of stakeholders to ensure that technical documentation is clear, concise, and accurate. **Image Data Augmentation with Keras** may be useful to a Technical Writer interested in writing documentation for image-based machine learning models. The course covers a variety of data augmentation techniques that can be used to improve the performance of image-based machine learning models.
UX Designer
UX Designer
A UX Designer creates and maintains user interfaces for websites and applications. They work with a variety of stakeholders to ensure that user interfaces are user-friendly and meet the needs of users. **Image Data Augmentation with Keras** may be useful to a UX Designer interested in using image data to improve the user experience. The course covers a variety of data augmentation techniques that can be used to improve the performance of image-based machine learning models.
Graphic designer
Graphic designer
A Graphic Designer creates and maintains visual content, such as logos, brochures, and website graphics. They work with a variety of stakeholders to ensure that visual content is visually appealing and meets the needs of the organization. **Image Data Augmentation with Keras** may be useful to a Graphic Designer interested in using image data to create more visually appealing content. The course covers a variety of data augmentation techniques that can be used to improve the performance of image-based machine learning models.
Web Developer
Web Developer
A Web Developer creates and maintains websites and web applications. They work with a variety of stakeholders to ensure that websites and web applications are functional and meet the needs of users. **Image Data Augmentation with Keras** may be useful to a Web Developer interested in using image data in their web applications. The course covers a variety of data augmentation techniques that can be used to improve the performance of image-based machine learning models.
For more career information including salaries, visit:
OpenCourser.com/course/8v78h0/image
Reading list
We've selected nine 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
Image Data Augmentation with Keras.
Provides a comprehensive overview of deep learning, with a focus on Python implementations using the Keras library.
Provides a comprehensive overview of data augmentation techniques for deep learning. Covers theoretical concepts, best practices, and implementation details. Discusses the benefits and limitations of different augmentation methods and offers practical guidance for choosing the most suitable ones for specific tasks.
Provides a comprehensive overview of machine learning using Python, Scikit-Learn, Keras, and TensorFlow. Covers a wide range of machine learning algorithms, including supervised learning, unsupervised learning, and deep learning. Offers practical examples and exercises to illustrate the use of these libraries for real-world machine learning tasks.
Save
Provides a comprehensive overview of generative adversarial networks, which can be used for image generation and data augmentation.
Provides a comprehensive overview of deep learning for computer vision, including image data augmentation techniques.
Save
Provides a comprehensive overview of computer vision algorithms, including image data augmentation techniques.
Covers the basics of Python programming for data analysis. Introduces data structures, data manipulation techniques, and data visualization libraries. Provides a solid foundation for working with data in Python.
Covers the fundamentals of Pandas, a popular library for data analysis and manipulation in Python. Introduces dataframes, data cleaning techniques, and data aggregation functions. Provides practical examples and exercises to illustrate the use of Pandas for data analysis tasks.
Provides a comprehensive overview of machine learning algorithms, including data augmentation techniques.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/8v78h0/image
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Effort
1.5 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Amit Yadav
Language
English
Good to know
Intended for students who want to augment their ability to build complex models in Keras for computer vision tasks
Not recommended for those with no experience in computer vision or data augmentation
May not align with beginners who look to learn image data augmentation from scratch
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