Classify Radio Signals from Space using Keras from Coursera

In this 1-hour long project-based course, you will learn the basics of using Keras with TensorFlow as its backend and use it to solve an image classification problem. The data we are going to use consists of 2D spectrograms of deep space radio signals collected by the Allen Telescope Array at the SETI Institute. We will treat the spectrograms as images to train an image classification model to classify the signals into one of four classes. By the end of the project, you will have built and trained a convolutional neural network from scratch using Keras to classify signals from space. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and Tensorflow pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - 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.

Good to know

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Builds a strong foundation for beginners interested in using Keras with TensorFlow as its backend to solve an image classification problem

Develops knowledge and skills in using Keras and TensorFlow, which are highly relevant to industry

Hands-on project-based course focuses on applying skills and knowledge

Reviews summary

Well-received course on using keras

This project-based course on using Keras is well-received by learners, with many positive reviews highlighting its strengths. The course provides a hands-on experience in building a convolutional neural network from scratch to classify radio signals from space. Learners appreciate the practical and engaging nature of the course, as well as the clear and concise explanations provided by the instructor.

Well-structured, clear explanations.

"The explanations were elaborate and insightful."

"Instructor knows the subject well and guides you through the material explaining each part."

Engaging, hands-on experience.

"I really learned a lot by doing the programming part live."

"It was little [difficult] but at the end I felt happy that I got to try out & learn something interesting from this Project."

"Creating a CNN model and seeing it work was amazing."

Some knowledge of CNNs and Python recommended.

"It's a nice course but it recommended that one must have knowledge of CNN and basic knowledge of python."

Could benefit from more theoretical explanations.

"It's to short and lack of theory to make us understand."

"I think it could add a section that give us a reading or other material that can strength our knowledge on the project."

Unstable Rhyme platform with technical difficulties.

"Rhyme experience is awful."

"Using the Rhyme platform is unstable."

"The cloud platform was lagging and slow."

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 Classify Radio Signals from Space using Keras with these activities:

Review linear algebra and calculus concepts

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This activity helps strengthen foundational knowledge in mathematics, which is essential for understanding the mathematical concepts used in deep learning.

Browse courses on Linear Algebra

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Go through your lecture notes or textbooks to revisit key concepts in linear algebra and calculus.
Practice solving problems related to these concepts.

Review Radio Astronomy by John Kraus

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This book gives additional depth on the topic of radio signals from space.

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Read chapters 1-3 to understand the fundamentals of radio astronomy.
Read chapter 6 to gain knowledge of the Allen Telescope Array.

Attend a conference or meetup on Machine Learning or Deep Learning

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Networking events provide opportunities to connect with professionals in the field, learn about industry trends, and gain insights that can enhance your learning

Browse courses on Machine Learning

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Identify and register for an upcoming conference or meetup focused on Machine Learning or Deep Learning.
Attend the event and actively engage in discussions and networking opportunities.

Three other activities

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Complete online exercises on Convolutional Neural Networks (CNNs) using TensorFlow

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These exercises will provide hands-on practice with CNNs and TensorFlow, which are used in the course project.

Browse courses on TensorFlow

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Enroll in an online course or tutorial that offers exercises on CNNs using TensorFlow.
Complete at least 10 exercises to gain proficiency in using these techniques.

Attend a workshop or tutorial on Deep Learning for Signal Processing

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This activity provides additional practical knowledge on applying deep learning techniques to signal processing, which is relevant to the course project.

Browse courses on Deep Learning

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Search for workshops or tutorials on Deep Learning for Signal Processing.
Attend the workshop or tutorial to learn about advanced techniques and best practices.

Build a simple CNN model from scratch using Keras

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This hands-on activity reinforces the concepts of CNNs and Keras covered in the course project by building a custom model.

Browse courses on Keras

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Follow a tutorial or documentation to set up a development environment with Keras and TensorFlow.
Create a simple dataset of images or spectrograms.
Design and build a CNN model using Keras layers.
Train and evaluate the model on your dataset.

Career center

Learners who complete Classify Radio Signals from Space using Keras will develop knowledge and skills that may be useful to these careers:

AI Engineer

An AI Engineer is responsible for designing, developing, and deploying AI systems. This course would be a great way for someone interested in becoming an AI Engineer to get started. The course provides a solid foundation in Keras and TensorFlow, which are essential tools for building AI systems. The course also covers important concepts such as natural language processing, computer vision, and reinforcement learning.

See salaries and explore the career path for AI Engineer

Machine Learning Engineer

A Machine Learning Engineer is responsible for designing, developing, and deploying machine learning models. This course would be a great starting point for someone interested in becoming a Machine Learning Engineer, as it provides a solid foundation in Keras and TensorFlow. The course also covers important concepts such as data preprocessing, model evaluation, and hyperparameter tuning.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

A Data Scientist is responsible for collecting, cleaning, and analyzing data to extract meaningful insights. This course would be beneficial for an aspiring Data Scientist, as it provides hands-on experience with using Keras and TensorFlow to solve an image classification problem. The skills learned in this course can be applied to a variety of data science tasks, such as building predictive models, detecting fraud, and identifying patterns in data.

See salaries and explore the career path for Data Scientist

Statistician

A Statistician is responsible for collecting, analyzing, and interpreting data. This course would be beneficial for an aspiring Statistician, as it provides hands-on experience with using Keras and TensorFlow to solve an image classification problem. The skills learned in this course can be applied to a variety of statistical tasks, such as hypothesis testing, regression analysis, and data visualization.

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Data Analyst

A Data Analyst is responsible for collecting, cleaning, and analyzing data to extract meaningful insights. This course would be beneficial for an aspiring Data Analyst, as it provides hands-on experience with using Keras and TensorFlow to solve an image classification problem. The skills learned in this course can be applied to a variety of data analysis tasks, such as building dashboards, identifying trends, and making predictions.

See salaries and explore the career path for Data Analyst

Data Engineer

A Data Engineer is responsible for designing, developing, and maintaining data pipelines. This course would be beneficial for an aspiring Data Engineer, as it provides hands-on experience with using Keras and TensorFlow to solve an image classification problem. The skills learned in this course can be applied to a variety of data engineering tasks, such as data integration, data cleaning, and data transformation.

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Software Engineer

A Software Engineer is responsible for designing, developing, and maintaining software systems. This course would be beneficial for an aspiring Software Engineer, as it provides hands-on experience with using Keras and TensorFlow to solve real-world problems. The skills learned in this course can be applied to a variety of software engineering tasks, such as building web applications, mobile applications, and data pipelines.

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Database Administrator

A Database Administrator is responsible for managing and maintaining databases. This course would be beneficial for an aspiring Database Administrator, as it provides hands-on experience with using Keras and TensorFlow to solve an image classification problem. The skills learned in this course can be applied to a variety of database administration tasks, such as data backup, data recovery, and performance tuning.

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Business Analyst

A Business Analyst is responsible for analyzing business data and processes to identify opportunities for improvement. This course would be beneficial for an aspiring Business Analyst, as it provides hands-on experience with using Keras and TensorFlow to solve an image classification problem. The skills learned in this course can be applied to a variety of business analysis tasks, such as data mining, process mapping, and financial modeling.

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Cloud Architect

A Cloud Architect is responsible for designing, developing, and deploying cloud computing solutions. This course would be beneficial for an aspiring Cloud Architect, as it provides hands-on experience with using Keras and TensorFlow to solve an image classification problem. The skills learned in this course can be applied to a variety of cloud architecture tasks, such as infrastructure design, security, and cost optimization.

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User Experience Researcher

A User Experience Researcher is responsible for studying how users interact with products and services. This course would be beneficial for an aspiring User Experience Researcher, as it provides hands-on experience with using Keras and TensorFlow to solve an image classification problem. The skills learned in this course can be applied to a variety of user experience research tasks, such as conducting user interviews, analyzing data, and making recommendations.

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Actuary

An Actuary is responsible for using mathematical and statistical models to assess risk and uncertainty. This course would be beneficial for an aspiring Actuary, as it provides hands-on experience with using Keras and TensorFlow to solve an image classification problem. The skills learned in this course can be applied to a variety of actuarial tasks, such as pricing insurance policies, managing risk, and developing financial models.

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Operations Research Analyst

An Operations Research Analyst is responsible for using mathematical and statistical models to optimize decision-making. This course would be beneficial for an aspiring Operations Research Analyst, as it provides hands-on experience with using Keras and TensorFlow to solve an image classification problem. The skills learned in this course can be applied to a variety of operations research tasks, such as scheduling, routing, and inventory management.

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Market Researcher

A Market Researcher is responsible for collecting, analyzing, and interpreting data about markets and customers. This course would be beneficial for an aspiring Market Researcher, as it provides hands-on experience with using Keras and TensorFlow to solve an image classification problem. The skills learned in this course can be applied to a variety of market research tasks, such as conducting surveys, analyzing data, and making recommendations.

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Quantitative Analyst

A Quantitative Analyst is responsible for using mathematical and statistical models to analyze financial data. This course would be beneficial for an aspiring Quantitative Analyst, as it provides hands-on experience with using Keras and TensorFlow to solve an image classification problem. The skills learned in this course can be applied to a variety of quantitative analysis tasks, such as building trading models, risk management, and portfolio optimization.

See salaries and explore the career path for Quantitative Analyst