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Chris Shockley
In this 1-hour long project-based course, you will learn how to build a Neural Network Model using Keras and the MNIST Data Set. By the end of the course you will have built a model that will recognize the digits of hand written numbers. You will also be...
Read more
In this 1-hour long project-based course, you will learn how to build a Neural Network Model using Keras and the MNIST Data Set. By the end of the course you will have built a model that will recognize the digits of hand written numbers. You will also be...
Read more
In this 1-hour long project-based course, you will learn how to build a Neural Network Model using Keras and the MNIST Data Set. By the end of the course you will have built a model that will recognize the digits of hand written numbers. You will also be exposed to One Hot Encoding, Neural Network Architecture, Loss Optimizers and Testing of the Model's performance.
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/22mt6m/machine
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Beginners who want to create their first neural network will find this introduction helpful
Teaches essential neural network concepts, including One Hot Encoding and Neural Network Architecture
Uses the popular Keras framework and the MNIST Data Set, widely used in machine learning and deep learning
Hands-on project-based approach allows learners to apply their learning immediately
Focuses on building a practical skill rather than theoretical knowledge
Requires basic programming and machine learning knowledge, making it suitable for intermediate learners
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Save Machine Learning: Create a Neural Network that Predicts whether an Image is a Car or Airplane. to your list so you can find it easily later:
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Reviews summary
Guided ml project
This 1-hour Machine Learning project, working best for students in North America, guides students in creating a Neural Network Model using Keras and the MNIST Data Set to recognize hand-written digits. Students will also learn about One Hot Encoding, Neural Network Architecture, Loss Optimizers, and Model Testing. Some students complained that the necessary files and packages were not available on the platform, but some students were able to find ways to work around this using their own computers.
Great instructor
"Very good tutor. "
Helpful project
"VERY USEFUL"
"Best project"
Missing files
"Sadly I couldn't do the practice. The necessary files and packages weren't available."
"El instructor guía adecuadamente a lo largo pero la consola virtual no cuenta con los packages ni los archivos necesarios para realizarlo impidiendo la practica a lo largo de la sesión"
"The required files for the project were not available, so I couldn't even try the project on my own. I wouldn't recommend any one to try this project unless they include the required files. It is waste of time."
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 Machine Learning: Create a Neural Network that Predicts whether an Image is a Car or Airplane. with these
activities:
Review linear algebra concepts
Show steps
Refresh your understanding of linear algebra, which forms the mathematical foundation for neural networks.
Browse courses on
Linear Algebra
Show steps
-
Review linear algebra notes or textbooks
-
Solve practice problems or exercises on linear algebra
Organize and review course materials
Show steps
Stay organized and prepare for success by reviewing and synthesizing course materials regularly.
Show steps
-
Create a dedicated study space and gather all relevant materials
-
Review lecture notes, readings, and assignments regularly
Review NumPy library concepts
Show steps
Reinforce your understanding of basic programming concepts and ensure a solid foundation for this course.
Browse courses on
NumPy
Show steps
-
Review documentation and tutorials on NumPy
-
Complete NumPy exercises or practice problems
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow Keras tutorials
Show steps
Familiarize yourself with the Keras library and its features to enhance your understanding of the course material.
Browse courses on
Keras
Show steps
-
Explore Keras documentation and tutorials
-
Build small neural network models with Keras
Practice coding neural network models
Show steps
Strengthen your programming skills and improve your ability to apply neural network concepts.
Browse courses on
Neural Networks
Show steps
-
Solve coding challenges related to neural networks
-
Build personal projects using Keras and neural networks
Develop a neural network model for a specific task
Show steps
Apply your knowledge by creating a tangible project that demonstrates your understanding of neural networks.
Browse courses on
Neural Networks
Show steps
-
Identify a problem or task suitable for a neural network
-
Design and implement the neural network model
-
Evaluate and refine the model's performance
Mentor junior learners in neural networks
Show steps
Deepen your understanding by sharing your knowledge and assisting others in their learning journey.
Browse courses on
Mentoring
Show steps
-
Join online forums or communities related to neural networks
-
Offer guidance and support to learners seeking help
Review linear algebra concepts
Show steps
Refresh your understanding of linear algebra, which forms the mathematical foundation for neural networks.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review linear algebra notes or textbooks
- Solve practice problems or exercises on linear algebra
Organize and review course materials
Show steps
Stay organized and prepare for success by reviewing and synthesizing course materials regularly.
Show steps
Show steps
Show steps
- Create a dedicated study space and gather all relevant materials
- Review lecture notes, readings, and assignments regularly
Review NumPy library concepts
Show steps
Reinforce your understanding of basic programming concepts and ensure a solid foundation for this course.
Browse courses on
NumPy
Show steps
Show steps
Show steps
- Review documentation and tutorials on NumPy
- Complete NumPy exercises or practice problems
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow Keras tutorials
Show steps
Familiarize yourself with the Keras library and its features to enhance your understanding of the course material.
Browse courses on
Keras
Show steps
Show steps
Show steps
- Explore Keras documentation and tutorials
- Build small neural network models with Keras
Practice coding neural network models
Show steps
Strengthen your programming skills and improve your ability to apply neural network concepts.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Solve coding challenges related to neural networks
- Build personal projects using Keras and neural networks
Develop a neural network model for a specific task
Show steps
Apply your knowledge by creating a tangible project that demonstrates your understanding of neural networks.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Identify a problem or task suitable for a neural network
- Design and implement the neural network model
- Evaluate and refine the model's performance
Mentor junior learners in neural networks
Show steps
Deepen your understanding by sharing your knowledge and assisting others in their learning journey.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Join online forums or communities related to neural networks
- Offer guidance and support to learners seeking help
Career center
Learners who complete Machine Learning: Create a Neural Network that Predicts whether an Image is a Car or Airplane. will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist uses their knowledge of data analysis and machine learning techniques to extract insights from data. They use these insights to make informed decisions and develop data-driven solutions. This Machine Learning course can help build a foundation for a Data Scientist by teaching you how to use Keras and the MNIST Data Set to recognize the digits of hand written numbers. This course will also expose you to One Hot Encoding, Neural Network Architecture, Loss Optimizers and Testing of the Model's performance.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and deploys machine learning models. They use their knowledge of machine learning algorithms and data analysis techniques to solve real-world problems. This Machine Learning course can help build a foundation for a Machine Learning Engineer by teaching you how to use Keras and the MNIST Data Set to recognize the digits of hand written numbers. This course will also expose you to One Hot Encoding, Neural Network Architecture, Loss Optimizers and Testing of the Model's performance.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and tests software applications. They use their knowledge of programming languages and software development tools to create software that meets the needs of users. This Machine Learning course can help build a foundation for a Software Engineer by teaching you how to use Keras and the MNIST Data Set to recognize the digits of hand written numbers. This course will also expose you to One Hot Encoding, Neural Network Architecture, Loss Optimizers and Testing of the Model's performance.
Data Analyst
Data Analyst
A Data Analyst uses their knowledge of data analysis tools to interpret large amounts of data. They are able to identify patterns and trends that can be used to make informed decisions. This Machine Learning course can help build a foundation for a Data Analyst by teaching you how to use Keras and the MNIST Data Set to recognize the digits of hand written numbers. This course will also expose you to One Hot Encoding, Neural Network Architecture, Loss Optimizers and Testing of the Model's performance.
Product Manager
Product Manager
A Product Manager uses their knowledge of customer needs and market trends to develop and launch new products. They work with engineers and other stakeholders to bring products to market that meet the needs of customers. This Machine Learning course may be useful for a Product Manager by teaching them how to use Keras and the MNIST Data Set to recognize the digits of hand written numbers. This course will also expose them to One Hot Encoding, Neural Network Architecture, Loss Optimizers and Testing of the Model's performance.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses their knowledge of mathematics and statistics to develop and implement financial models. They use these models to make investment decisions and manage risk. This Machine Learning course may be useful for a Quantitative Analyst by teaching them how to use Keras and the MNIST Data Set to recognize the digits of hand written numbers. This course will also expose them to One Hot Encoding, Neural Network Architecture, Loss Optimizers and Testing of the Model's performance.
Risk Manager
Risk Manager
A Risk Manager uses their knowledge of risk management techniques to identify, assess, and mitigate risks. They work with stakeholders to develop and implement risk management strategies that protect the organization from financial loss and reputational damage. This Machine Learning course may be useful for a Risk Manager by teaching them how to use Keras and the MNIST Data Set to recognize the digits of hand written numbers. This course will also expose them to One Hot Encoding, Neural Network Architecture, Loss Optimizers and Testing of the Model's performance.
Auditor
Auditor
An Auditor uses their knowledge of accounting and auditing standards to examine and evaluate financial records. They work with clients to ensure that financial statements are accurate and reliable. This Machine Learning course may be useful for an Auditor by teaching them how to use Keras and the MNIST Data Set to recognize the digits of hand written numbers. This course will also expose them to One Hot Encoding, Neural Network Architecture, Loss Optimizers and Testing of the Model's performance.
Tax Accountant
Tax Accountant
A Tax Accountant uses their knowledge of tax laws and regulations to prepare tax returns and advise clients on tax matters. They work with clients to minimize their tax liability and ensure that they are compliant with all applicable tax laws. This Machine Learning course may be useful for a Tax Accountant by teaching them how to use Keras and the MNIST Data Set to recognize the digits of hand written numbers. This course will also expose them to One Hot Encoding, Neural Network Architecture, Loss Optimizers and Testing of the Model's performance.
Market Researcher
Market Researcher
A Market Researcher uses their knowledge of research methods and data analysis techniques to understand the needs of customers. They use this information to develop and implement marketing strategies that increase sales. This Machine Learning course may be useful for a Market Researcher by teaching them how to use Keras and the MNIST Data Set to recognize the digits of hand written numbers. This course will also expose them to One Hot Encoding, Neural Network Architecture, Loss Optimizers and Testing of the Model's performance.
Project Manager
Project Manager
A Project Manager uses their knowledge of project management techniques to plan, execute, and close projects. They work with stakeholders to ensure that projects are completed on time, within budget, and to the required quality. This Machine Learning course may be useful for a Project Manager by teaching them how to use Keras and the MNIST Data Set to recognize the digits of hand written numbers. This course will also expose them to One Hot Encoding, Neural Network Architecture, Loss Optimizers and Testing of the Model's performance.
Compliance Officer
Compliance Officer
A Compliance Officer uses their knowledge of laws and regulations to ensure that an organization is compliant with all applicable requirements. They work with stakeholders to develop and implement compliance programs that protect the organization from legal and regulatory risks. This Machine Learning course may be useful for a Compliance Officer by teaching them how to use Keras and the MNIST Data Set to recognize the digits of hand written numbers. This course will also expose them to One Hot Encoding, Neural Network Architecture, Loss Optimizers and Testing of the Model's performance.
Business Analyst
Business Analyst
A Business Analyst uses their knowledge of business processes and data analysis techniques to identify opportunities for improvement. They work with stakeholders to develop and implement solutions that meet the needs of the business. This Machine Learning course may be useful for a Business Analyst by teaching them how to use Keras and the MNIST Data Set to recognize the digits of hand written numbers. This course will also expose them to One Hot Encoding, Neural Network Architecture, Loss Optimizers and Testing of the Model's performance.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses their knowledge of mathematical models and optimization techniques to solve problems in a variety of industries. They work with stakeholders to develop and implement solutions that improve efficiency and productivity. This Machine Learning course may be useful for an Operations Research Analyst by teaching them how to use Keras and the MNIST Data Set to recognize the digits of hand written numbers. This course will also expose them to One Hot Encoding, Neural Network Architecture, Loss Optimizers and Testing of the Model's performance.
Financial Analyst
Financial Analyst
A Financial Analyst uses their knowledge of financial markets and analysis techniques to make investment recommendations. They work with clients to develop and implement investment strategies that meet their financial goals. This Machine Learning course may be useful for a Financial Analyst by teaching them how to use Keras and the MNIST Data Set to recognize the digits of hand written numbers. This course will also expose them to One Hot Encoding, Neural Network Architecture, Loss Optimizers and Testing of the Model's performance.
For more career information including salaries, visit:
OpenCourser.com/course/22mt6m/machine
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
Machine Learning: Create a Neural Network that Predicts whether an Image is a Car or Airplane..
Provides a comprehensive overview of machine learning concepts and techniques, with a focus on practical implementation using popular Python libraries such as Scikit-Learn, Keras, and TensorFlow. It covers a wide range of topics relevant to the course, including data preprocessing, feature engineering, model selection, and evaluation.
Written by the creator of Keras, this book offers a practical guide to deep learning, covering the fundamentals of neural networks, convolutional neural networks, recurrent neural networks, and more. It provides detailed explanations and code examples that can be easily applied to the course project.
This online book provides an intuitive and accessible introduction to neural networks and deep learning. It covers the basics of neural networks, backpropagation, convolutional neural networks, and recurrent neural networks. While it may not focus specifically on image classification, it offers a solid foundation for understanding the concepts used in the course.
Save
This textbook provides a comprehensive overview of computer vision algorithms and techniques. It covers topics such as image formation, feature detection, object recognition, and segmentation. While it may not focus specifically on neural networks, it offers a strong foundation in the underlying concepts that are relevant to the course project.
Save
This textbook provides a theoretical foundation for machine learning and pattern recognition. It covers topics such as probability theory, Bayesian inference, and statistical learning. While it may not contain specific instructions on building a neural network for image classification, it offers a deep understanding of the underlying mathematical principles.
This textbook provides a probabilistic perspective on machine learning. It covers topics such as Bayesian inference, graphical models, and reinforcement learning. While it may not focus specifically on neural networks, it offers valuable insights into the underlying principles of machine learning.
Provides a comprehensive overview of neural networks for image processing. It covers topics such as image enhancement, image restoration, and image recognition. While it may not focus specifically on the MNIST dataset, it offers valuable insights into the use of neural networks for image-related tasks.
Provides a comprehensive overview of computer vision principles and practices. It covers topics such as image formation, feature detection, object recognition, and segmentation. While it may not focus specifically on neural networks, it offers a strong foundation in the underlying concepts that are relevant to the course project.
Save
This textbook provides a comprehensive overview of pattern recognition techniques. It covers topics such as feature extraction, dimensionality reduction, and classification. While it may not focus specifically on neural networks or image classification, it offers a strong foundation in the underlying concepts that are relevant to the course project.
Provides a comprehensive overview of statistical learning with sparsity. It covers topics such as regularization, feature selection, and compressed sensing. While it may not focus specifically on neural networks or image classification, it offers valuable insights into the underlying principles of machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/22mt6m/machine
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Chris Shockley
Good to know
Beginners who want to create their first neural network will find this introduction helpful
Teaches essential neural network concepts, including One Hot Encoding and Neural Network Architecture
Uses the popular Keras framework and the MNIST Data Set, widely used in machine learning and deep learning
Hands-on project-based approach allows learners to apply their learning immediately
Focuses on building a practical skill rather than theoretical knowledge
Requires basic programming and machine learning knowledge, making it suitable for intermediate learners
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