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Priya Jha
In this 1 hour long project-based course, you will learn to build and train a convolutional neural network in Keras with TensorFlow as backend from scratch to classify patients as infected with COVID or not using their chest x-ray images. Our goal is to create an image classifier with Tensorflow by implementing a CNN to differentiate between chest x rays images with a COVID 19 infections versus without. The dataset contains the lungs X-ray images of both groups.We will be carrying out the entire project on the Google Colab environment.
Read more
In this 1 hour long project-based course, you will learn to build and train a convolutional neural network in Keras with TensorFlow as backend from scratch to classify patients as infected with COVID or not using their chest x-ray images. Our goal is to create an image classifier with Tensorflow by implementing a CNN to differentiate between chest x rays images with a COVID 19 infections versus without. The dataset contains the lungs X-ray images of both groups.We will be carrying out the entire project on the Google Colab environment.
Read more
In this 1 hour long project-based course, you will learn to build and train a convolutional neural network in Keras with TensorFlow as backend from scratch to classify patients as infected with COVID or not using their chest x-ray images. Our goal is to create an image classifier with Tensorflow by implementing a CNN to differentiate between chest x rays images with a COVID 19 infections versus without. The dataset contains the lungs X-ray images of both groups.We will be carrying out the entire project on the Google Colab environment.
Please be aware of the fact that the dataset and the model in this project, can not be used in the real-life. We are only using this data for educational purposes.
By the end of this project, you will be able to build and train the convolutional neural network using Keras with TensorFlow as a backend. You will also be able to perform data visualization. Additionally, you will also be able to use the model to make predictions on new data.
You should be familiar with the Python Programming language and you should have a theoretical understanding of Convolutional Neural Networks. You will need a free Gmail account to complete this project.
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/c44vef/classification
What's inside
Syllabus
Project Overview
In this 1 hour long project-based course, you will learn to build and train a convolutional neural network in Keras with TensorFlow as backend from scratch to classify patients as infected with COVID or not using their chest x-ray images. Our goal is to create an image classifier with Tensorflow by implementing a CNN to differentiate between chest x rays images with a COVID 19 infections versus without. The dataset contains the lungs X-ray images of both groups.We will be carrying out the entire project on the Google Colab environment.
Please be aware of the fact that the dataset and the model in this project, can not be used in the real-life. We are only using this data for educational purposes.
By the end of this project, you will be able to build and train the convolutional neural network using Keras with TensorFlow as a backend. You will also be able to perform data visualization. Additionally, you will also be able to use the model to make predictions on new data.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops skills which are useful for personal growth and development
Well-suited for learners interested in Data Science
Provides hands-on experience through interactive materials
Teaches skills, knowledge, and tools that are highly relevant in an academic setting
Explores a unique perspective on COVID-19 detection
Taught by instructors with expertise in the subject
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Reviews summary
Practical covid-19 chest x-ray classification
Learners say this is a well-received course that provides engaging assignments and that the subject is particularly relevant. The course uses convolutional neural networks to predict COVID-19 from chest X-ray images. While some learners find the lectures helpful, others have reported that the instructor speaks too fast or with an accent that is difficult to understand.
Provide practical experience.
"best guided project I have taken at Coursera so far"
"very good and practical project"
Relevant to current events.
"was completely related to the current situation and I really liked this project"
"informative"
May be difficult to understand.
"instructor speaks too fast with an accent and I couldn't catch up with her explanations."
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 Classification of COVID19 using Chest X-ray Images in Keras with these
activities:
Review of Convolutional Neural Networks Theory
Show steps
Refresh your understanding of the theoretical foundations of Convolutional Neural Networks (CNNs) to enhance your comprehension of the course material.
Browse courses on
Convolutional Neural Networks
Show steps
-
Read online articles or textbooks on CNNs.
-
Review lecture notes or slides from previous courses on CNNs.
Seeking Guidance from Experienced CNN Practitioners
Show steps
Enhance your learning journey by seeking guidance from experienced CNN practitioners. This can provide valuable insights and support as you progress through the course.
Show steps
-
Identify potential mentors within your network or through online platforms.
-
Reach out and request mentorship, outlining your goals and areas where you seek guidance.
-
Regularly connect with your mentor for discussions and feedback.
Review of 'Deep Learning with Python'
Show steps
Gain a broader perspective by reviewing a comprehensive book on deep learning, such as 'Deep Learning with Python' by François Chollet. This will supplement your understanding of CNNs within the wider context of deep learning.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
-
Read selected chapters or sections relevant to CNNs.
-
Reflect on how the concepts presented in the book align with the course material.
-
Identify areas where you can deepen your understanding.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Solving CNN Practice Problems
Show steps
Engage in targeted practice by solving CNN-related problems to reinforce your understanding of the concepts covered in the course.
Show steps
-
Work through practice problems and exercises provided in the course materials.
-
Search for additional practice problems online or in textbooks.
-
Join online forums or discussion groups to engage with other learners and solve problems collaboratively.
Attending a CNN Workshop
Show steps
Deepen your understanding by attending a workshop focused on CNNs. This will provide an immersive learning experience and allow you to engage with experts in the field.
Show steps
-
Research and identify relevant CNN workshops.
-
Register for the workshop and attend all sessions.
-
Actively participate in discussions and Q&A sessions.
Building a Mini CNN Project
Show steps
Solidify your understanding by implementing a small-scale CNN project. This will provide practical experience and enhance your comprehension of the course content.
Show steps
-
Choose a simple image classification task, such as recognizing handwritten digits or classifying images of clothing.
-
Design and implement a CNN architecture using Keras with TensorFlow backend.
-
Train and evaluate your CNN model.
Following Online Tutorials on Advanced CNN Techniques
Show steps
Expand your knowledge by exploring online tutorials that delve into advanced CNN techniques, such as transfer learning or data augmentation.
Show steps
-
Identify reputable sources and online platforms offering tutorials on advanced CNN techniques.
-
Follow step-by-step tutorials to implement these techniques in your own projects.
-
Experiment with different techniques to observe their impact on model performance.
Mentoring Junior Students in CNN Concepts
Show steps
Reinforce your understanding by mentoring junior students or peers who are learning about CNNs. This will solidify your knowledge and enhance your communication skills.
Show steps
-
Identify opportunities to assist others in understanding CNN concepts.
-
Provide clear explanations, answer questions, and offer guidance.
-
Encourage active participation and foster a supportive learning environment.
Contributing to Open-Source CNN Projects
Show steps
Enhance your understanding and stay up-to-date with the latest advancements by contributing to open-source CNN projects.
Show steps
-
Identify reputable open-source CNN projects.
-
Review the project documentation and codebase.
-
Contribute bug fixes, feature enhancements, or documentation improvements.
Review of Convolutional Neural Networks Theory
Show steps
Refresh your understanding of the theoretical foundations of Convolutional Neural Networks (CNNs) to enhance your comprehension of the course material.
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Read online articles or textbooks on CNNs.
- Review lecture notes or slides from previous courses on CNNs.
Seeking Guidance from Experienced CNN Practitioners
Show steps
Enhance your learning journey by seeking guidance from experienced CNN practitioners. This can provide valuable insights and support as you progress through the course.
Show steps
Show steps
Show steps
- Identify potential mentors within your network or through online platforms.
- Reach out and request mentorship, outlining your goals and areas where you seek guidance.
- Regularly connect with your mentor for discussions and feedback.
Review of 'Deep Learning with Python'
Show steps
Gain a broader perspective by reviewing a comprehensive book on deep learning, such as 'Deep Learning with Python' by François Chollet. This will supplement your understanding of CNNs within the wider context of deep learning.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
Show steps
Show steps
- Read selected chapters or sections relevant to CNNs.
- Reflect on how the concepts presented in the book align with the course material.
- Identify areas where you can deepen your understanding.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Solving CNN Practice Problems
Show steps
Engage in targeted practice by solving CNN-related problems to reinforce your understanding of the concepts covered in the course.
Show steps
Show steps
Show steps
- Work through practice problems and exercises provided in the course materials.
- Search for additional practice problems online or in textbooks.
- Join online forums or discussion groups to engage with other learners and solve problems collaboratively.
Attending a CNN Workshop
Show steps
Deepen your understanding by attending a workshop focused on CNNs. This will provide an immersive learning experience and allow you to engage with experts in the field.
Show steps
Show steps
Show steps
- Research and identify relevant CNN workshops.
- Register for the workshop and attend all sessions.
- Actively participate in discussions and Q&A sessions.
Building a Mini CNN Project
Show steps
Solidify your understanding by implementing a small-scale CNN project. This will provide practical experience and enhance your comprehension of the course content.
Show steps
Show steps
Show steps
- Choose a simple image classification task, such as recognizing handwritten digits or classifying images of clothing.
- Design and implement a CNN architecture using Keras with TensorFlow backend.
- Train and evaluate your CNN model.
Following Online Tutorials on Advanced CNN Techniques
Show steps
Expand your knowledge by exploring online tutorials that delve into advanced CNN techniques, such as transfer learning or data augmentation.
Show steps
Show steps
Show steps
- Identify reputable sources and online platforms offering tutorials on advanced CNN techniques.
- Follow step-by-step tutorials to implement these techniques in your own projects.
- Experiment with different techniques to observe their impact on model performance.
Mentoring Junior Students in CNN Concepts
Show steps
Reinforce your understanding by mentoring junior students or peers who are learning about CNNs. This will solidify your knowledge and enhance your communication skills.
Show steps
Show steps
Show steps
- Identify opportunities to assist others in understanding CNN concepts.
- Provide clear explanations, answer questions, and offer guidance.
- Encourage active participation and foster a supportive learning environment.
Contributing to Open-Source CNN Projects
Show steps
Enhance your understanding and stay up-to-date with the latest advancements by contributing to open-source CNN projects.
Show steps
Show steps
Show steps
- Identify reputable open-source CNN projects.
- Review the project documentation and codebase.
- Contribute bug fixes, feature enhancements, or documentation improvements.
Career center
Learners who complete Classification of COVID19 using Chest X-ray Images in Keras will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists often use machine learning to make data accessible and easier to understand, which requires strong programming skills in Python and an ability to interpret data using statistical methods. Through this course, you'll build a foundation in using Keras with TensorFlow to train a convolutional neural network model. Additionally, you'll develop skills in data visualization and making predictions on new data, all of which are applicable to a role as a Data Scientist.
Healthcare Data Analyst
Healthcare Data Analyst
Healthcare Data Analysts use data to improve patient care. This often requires skills in machine learning and data visualization. This course will help you build a foundation in using machine learning models to make predictions on new data. It will also help you develop your data visualization skills. Both of these skills are valuable for aspiring Healthcare Data Analysts.
Business Analyst
Business Analyst
Business Analysts help businesses understand and solve problems using data. They often use machine learning to analyze data and make recommendations. This course will provide you with a foundation in using machine learning models to make predictions on new data. It will also help you develop your data visualization skills. These skills are valuable for aspiring Business Analysts.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications, which often requires skills in machine learning and data analysis. This course will introduce you to the fundamentals of convolutional neural networks and data visualization, both of which are becoming increasingly important in software development. The practical experience in building a machine learning model will also be beneficial for aspiring Software Engineers.
Data Analyst
Data Analyst
Data Analysts collect, clean, and interpret data to help businesses make informed decisions. To do this, they need skills in data visualization, data analysis, and machine learning. This course will help you build a foundation in data visualization and using machine learning models to make predictions on new data. These skills are valuable for aspiring Data Analysts.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and maintain machine learning systems, which requires an ability to build, train, and evaluate models. This course provides hands-on experience in building and training a convolutional neural network model using Keras with TensorFlow as a backend. As a result, this course may be useful for aspiring Machine Learning Engineers.
Marketing Analyst
Marketing Analyst
Marketing Analysts collect, analyze, and interpret data to help businesses make marketing decisions. To do this, they often use machine learning and data visualization. This course will provide you with a foundation in using machine learning models to make predictions on new data. It will also help you develop your data visualization skills. These skills will enable you to develop effective marketing campaigns for your clients.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical modeling to analyze data and make investment decisions. To do this, they often use machine learning and data visualization. This course will provide you with experience using Keras to train a convolutional neural network model and develop your data visualization skills. As a result, this course may be useful for aspiring Quantitative Analysts.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data infrastructure. To understand the needs and flow of data, it is helpful to have machine learning and data visualization skills. This course will introduce you to the fundamentals of convolutional neural networks and data visualization. As a result, this course may be useful for aspiring Data Engineers.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. To do this, they often use machine learning and data visualization. This course will build on your existing knowledge of statistics to provide a foundation in using convolutional neural networks and data visualization. These skills will enable you to make more informed decisions and communicate your findings more effectively.
User Experience Researcher
User Experience Researcher
User Experience Researchers conduct research to understand how users interact with products. To do this, they often use machine learning and data visualization. This course will provide you with foundational knowledge in using machine learning and data visualization. The hands-on experience in building a machine learning model will be particularly beneficial for aspiring User Experience Researchers.
Product Manager
Product Manager
Product Managers develop and manage products. To understand the needs of users, it is helpful to have machine learning and data visualization skills. This course will introduce you to the fundamentals of convolutional neural networks and data visualization. As a result, this course may be useful for aspiring Product Managers.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers design and develop systems that enable computers to interpret and understand images and videos. To do this, they often use convolutional neural networks. This course will provide you with hands-on experience in building and training a convolutional neural network model. As a result, this course may be useful for aspiring Computer Vision Engineers.
Information Architect
Information Architect
Information Architects design and organize websites and other digital products. To do this, they often use machine learning and data visualization. This course will provide you with foundational knowledge in using machine learning and data visualization. As a result, this course may be useful for aspiring Information Architects.
Research Scientist
Research Scientist
Research Scientists conduct research to advance knowledge in various fields. Many Research Scientists use machine learning and data visualization to analyze data and make discoveries. This course will provide you with foundational knowledge in using machine learning and data visualization. As a result, this course may be useful for aspiring Research Scientists.
For more career information including salaries, visit:
OpenCourser.com/course/c44vef/classification
Reading list
We've selected seven 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
Classification of COVID19 using Chest X-ray Images in Keras.
Provides a comprehensive overview of deep learning, including the fundamentals of neural networks, convolutional neural networks, and recurrent neural networks. It also covers practical aspects of deep learning, such as data preprocessing, model training, and evaluation.
Provides a comprehensive overview of deep learning for medical image analysis, with a focus on chest x-ray images. It covers the essential concepts, techniques, and applications.
Comprehensive guide to deep learning with Python, and it covers all the essential concepts and techniques. It is an excellent resource for learners who want to gain a deep understanding of deep learning.
Provides a practical introduction to machine learning, using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It covers a wide range of machine learning topics, including data preprocessing, model selection, and evaluation.
Practical guide to TensorFlow, the popular open-source machine learning library. It covers the essential concepts and techniques, as well as practical tips and advice.
Provides a comprehensive overview of pattern recognition and machine learning, covering a wide range of topics, including statistical models, neural networks, and support vector machines. It good resource for learners who want to gain a deeper understanding of the theoretical foundations of machine learning.
Save
Provides a comprehensive overview of artificial intelligence in healthcare, covering a wide range of topics, including machine learning, natural language processing, and computer vision. It good resource for learners who want to gain a deeper understanding of the role of artificial intelligence in healthcare.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/c44vef/classification
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Priya Jha
Language
English
Good to know
Develops skills which are useful for personal growth and development
Well-suited for learners interested in Data Science
Provides hands-on experience through interactive materials
Teaches skills, knowledge, and tools that are highly relevant in an academic setting
Explores a unique perspective on COVID-19 detection
Taught by instructors with expertise in the subject
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