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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 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/he23nk/machine
What's inside
Syllabus
Project Overview
Here you will describe what the project is about. It should give an overview of what the learner will achieve by completing this project.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops neural network modeling skills, which are highly relevant in research, academia, and industry
Uses the MNIST data set, which is a standard in machine learning for handwritten digit recognition
Builds a foundation in neural network architecture, loss optimization, and model testing
Taught by Chris Shockley, a recognized expert in deep learning
Hands-on project-based learning approach to reinforce the concepts taught
May require additional resources or software that learners may not have readily available
Save this course
Save Machine Learning: Predict Numbers from Handwritten Digits using a Neural Network, Keras, and R to your list so you can find it easily later:
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Reviews summary
Beginner-friendly neural network
According to students, Machine Learning: Predict Numbers from Handwritten Digits using a Neural Network, Keras, and R is a beginner-friendly option for those looking to learn about neural networks. Students largely rate this course positively, citing its very practical sessions explained through workbook examples located in the cloud, helping to provide them with hands-on experience. While one student remarked that the course could be more detailed, another said they considered it to be very helpful.
Practical Sessions with Workbooks
"Very practical session explained with work book in cloud"
"very useful in hands on experience"
Appropriate for Beginners
"good"
"very useful course"
"it's very helpful"
Potential Cloud Desktop Access Issues
"Rhyme won't let me access cloud desktop, I did not even watch one video and when I went to external tool for the first time, it directly said that you've almost earned your certificate. What stupidity is that?"
Could Include More Detailed Explanations
"average course, I. guess it should be little more detailed explanation is required"
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: Predict Numbers from Handwritten Digits using a Neural Network, Keras, and R with these
activities:
Compile your notes, assignments, quizzes, and exams
Show steps
Compiling your materials will help you stay organized and prepare for the course.
Show steps
-
Gather your notes, assignments, quizzes, and exams.
-
Organize your materials into folders.
-
Create a study schedule.
Review the basics of Python programming
Show steps
Refreshing your Python programming skills will help you be better prepared for the course.
Browse courses on
Python
Show steps
-
Review the basics of Python syntax.
-
Complete some Python coding exercises.
Review an introductory textbook on Keras
Show steps
Reviewing an introductory textbook on Keras will help you familiarize yourself with the basics of the library and prepare you for the course.
View
Deep Learning with TensorFlow and Keras - Third...
on Amazon
Show steps
-
Read the first few chapters of the book.
-
Try out the code examples in the book.
-
Create a small Keras project of your own.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice building Neural Network Models on different datasets
Show steps
Practicing building Neural Network Models on different datasets will help you develop your skills and improve your understanding of the concepts.
Show steps
-
Find a dataset that you are interested in.
-
Load the dataset into Keras.
-
Build a Neural Network Model for the dataset.
-
Train and evaluate the Neural Network Model.
-
Repeat steps 1-4 for different datasets.
Create a presentation on your Neural Network Model
Show steps
Creating a presentation on your Neural Network Model will help you organize your thoughts and communicate your findings to others.
Show steps
-
Choose a topic for your presentation.
-
Create an outline for your presentation.
-
Gather your data and create your slides.
-
Rehearse your presentation.
-
Deliver your presentation.
Write a blog post about your experience building a Neural Network Model
Show steps
Writing a blog post about your experience will help you solidify your understanding of the concepts and share your knowledge with others.
Show steps
-
Choose a topic for your blog post.
-
Write an outline for your blog post.
-
Write the first draft of your blog post.
-
Edit and revise your blog post.
-
Publish your blog post.
Build a Neural Network Model for a real-world problem
Show steps
Building a Neural Network Model for a real-world problem will give you a chance to apply your skills and knowledge to a practical problem.
Browse courses on
Neural Networks
Show steps
-
Identify a real-world problem that you would like to solve.
-
Gather data for your problem.
-
Build a Neural Network Model for your problem.
-
Train and evaluate your Neural Network Model.
-
Deploy your Neural Network Model.
Compile your notes, assignments, quizzes, and exams
Show steps
Compiling your materials will help you stay organized and prepare for the course.
Show steps
Show steps
Show steps
- Gather your notes, assignments, quizzes, and exams.
- Organize your materials into folders.
- Create a study schedule.
Review the basics of Python programming
Show steps
Refreshing your Python programming skills will help you be better prepared for the course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review the basics of Python syntax.
- Complete some Python coding exercises.
Review an introductory textbook on Keras
Show steps
Reviewing an introductory textbook on Keras will help you familiarize yourself with the basics of the library and prepare you for the course.
View
Deep Learning with TensorFlow and Keras - Third...
on Amazon
Show steps
Show steps
Show steps
- Read the first few chapters of the book.
- Try out the code examples in the book.
- Create a small Keras project of your own.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice building Neural Network Models on different datasets
Show steps
Practicing building Neural Network Models on different datasets will help you develop your skills and improve your understanding of the concepts.
Show steps
Show steps
Show steps
- Find a dataset that you are interested in.
- Load the dataset into Keras.
- Build a Neural Network Model for the dataset.
- Train and evaluate the Neural Network Model.
- Repeat steps 1-4 for different datasets.
Create a presentation on your Neural Network Model
Show steps
Creating a presentation on your Neural Network Model will help you organize your thoughts and communicate your findings to others.
Show steps
Show steps
Show steps
- Choose a topic for your presentation.
- Create an outline for your presentation.
- Gather your data and create your slides.
- Rehearse your presentation.
- Deliver your presentation.
Write a blog post about your experience building a Neural Network Model
Show steps
Writing a blog post about your experience will help you solidify your understanding of the concepts and share your knowledge with others.
Show steps
Show steps
Show steps
- Choose a topic for your blog post.
- Write an outline for your blog post.
- Write the first draft of your blog post.
- Edit and revise your blog post.
- Publish your blog post.
Build a Neural Network Model for a real-world problem
Show steps
Building a Neural Network Model for a real-world problem will give you a chance to apply your skills and knowledge to a practical problem.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Identify a real-world problem that you would like to solve.
- Gather data for your problem.
- Build a Neural Network Model for your problem.
- Train and evaluate your Neural Network Model.
- Deploy your Neural Network Model.
Career center
Learners who complete Machine Learning: Predict Numbers from Handwritten Digits using a Neural Network, Keras, and R will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying Machine Learning models. They work closely with Data Scientists and Data Analysts to identify the most appropriate algorithms and techniques for solving specific business problems. This course provides a strong foundation in the fundamentals of Machine Learning, including neural networks, optimization techniques, and model evaluation. By completing this course, you will be well-equipped to enter the field of Machine Learning Engineering and build a successful career in this rapidly growing field.
Data Analyst
Data Analyst
Data analysts play a vital role in the development and implementation of Machine Learning algorithms and models. They are responsible for collecting, cleaning, and analyzing data to identify patterns and trends that can be used to make informed decisions. A strong foundation in Machine Learning, such as that provided by this course, is essential for success in this field. By completing this course, you will gain the skills necessary to extract insights from data, build predictive models, and communicate your findings effectively. This will give you a competitive edge in the job market and help you excel in your career as a Data Analyst.
Data Scientist
Data Scientist
Data Scientists use Machine Learning algorithms and techniques to solve complex business problems. They are responsible for extracting insights from data, building predictive models, and communicating their findings to stakeholders. This course provides a comprehensive introduction to Machine Learning, including neural networks, deep learning, and natural language processing. By completing this course, you will gain the skills and knowledge necessary to become a successful Data Scientist and contribute to the field of data-driven decision-making.
Research Scientist
Research Scientist
Research Scientists conduct research in various fields, including Machine Learning. This course can provide you with the necessary foundation in Machine Learning to pursue a career in research. You will learn about the latest Machine Learning algorithms and techniques, and you will gain the skills necessary to design and conduct your own research projects.
University Professor
University Professor
University Professors teach and conduct research in various fields, including Machine Learning. This course can provide you with the necessary foundation in Machine Learning to pursue a career in academia. You will learn about the latest Machine Learning algorithms and techniques, and you will gain the skills necessary to design and conduct your own research projects.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain the infrastructure that stores and processes data. Machine Learning requires large amounts of data, and Data Engineers are responsible for ensuring that this data is available and accessible to Machine Learning models. This course can provide you with the necessary knowledge to build and manage data pipelines for Machine Learning.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts leverage mathematical and statistical models to assess the risk and return of financial investments. Implementing knowledge of neural networks and model evaluation techniques gained from this course can enhance your ability to build predictive models for financial data.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make investment recommendations and assess the performance of companies. Machine Learning is increasingly used in finance for tasks like stock price prediction, risk management, and fraud detection. This course can provide you with the necessary knowledge to apply Machine Learning techniques to financial data.
Business Analyst
Business Analyst
Business Analysts leverage data to identify and solve business problems. This course provides a foundation in Machine Learning, which is increasingly used in business analysis for tasks like customer segmentation, demand forecasting, and fraud detection. By completing this course, you'll be able to effectively collaborate with technical teams and develop data-driven solutions that drive business value.
Software Engineer
Software Engineer
As a software engineer, understanding Machine Learning concepts and techniques help in developing robust and efficient software applications. Machine Learning is integrated into various software systems like recommendation engines, fraud detection, and natural language processing. Acquiring skills in neural networks, loss optimizers and model testing through this course can complement your software engineering abilities, making you a more valuable asset to potential employers.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex problems in various industries. Machine Learning is increasingly used in operations research for tasks like supply chain optimization, inventory management, and scheduling. This course can provide you with the necessary knowledge to apply Machine Learning techniques to real-world problems.
Marketing Analyst
Marketing Analyst
Marketing Analysts use data to understand consumer behavior and develop effective marketing campaigns. Machine Learning is increasingly used in marketing for tasks like customer segmentation, campaign optimization, and social media analysis. This course will provide you with the foundation in Machine Learning necessary to succeed in this field.
Consultant
Consultant
Consultants provide advice and expertise to businesses and organizations. Machine Learning is increasingly used in consulting for tasks like data analysis, process optimization, and risk management. This course can provide you with the necessary knowledge to apply Machine Learning techniques to solve real-world problems and help businesses make better decisions.
Entrepreneur
Entrepreneur
Entrepreneurs start and run their own businesses. Machine Learning is increasingly used in entrepreneurship for tasks like product development, customer segmentation, and marketing automation. This course can provide you with the necessary knowledge to apply Machine Learning techniques to your own business and gain a competitive advantage.
Product Manager
Product Manager
Product Managers are responsible for developing and launching new products or features. Machine Learning is increasingly used in product development for tasks like personalized recommendations, user segmentation, and churn prediction. This course can provide you with the necessary knowledge to understand the potential of Machine Learning and its applications in product development.
For more career information including salaries, visit:
OpenCourser.com/course/he23nk/machine
Reading list
We've selected 15 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: Predict Numbers from Handwritten Digits using a Neural Network, Keras, and R.
Save
Provides a comprehensive overview of pattern recognition and machine learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning.
Provides a comprehensive overview of machine learning concepts and techniques using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and evaluation.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It covers a wide range of topics, including Bayesian inference, graphical models, and reinforcement learning.
Comprehensive guide to deep learning using Python and the Keras library. It covers the fundamentals of deep learning, including neural networks, convolutional neural networks, and recurrent neural networks.
Save
Provides a comprehensive overview of machine learning. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and evaluation.
Provides a comprehensive overview of neural networks and deep learning. It is written in a clear and engaging style, making it a great resource for beginners and experts alike.
Provides a practical introduction to deep learning using Fastai and PyTorch. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and evaluation.
Provides a comprehensive overview of neural networks and deep learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning.
Provides a comprehensive overview of machine learning using R. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and evaluation.
Provides a comprehensive overview of deep learning using R. It covers a wide range of topics, including neural networks, convolutional neural networks, and recurrent neural networks.
Provides a comprehensive overview of machine learning design patterns. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning.
Save
Provides a practical introduction to machine learning for business. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and evaluation.
Provides a practical introduction to machine learning. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and evaluation.
Provides a practical introduction to machine learning for hackers. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and evaluation.
Provides a comprehensive overview of machine learning for beginners. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and evaluation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/he23nk/machine
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Effort
2 Hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Chris Shockley
Language
English
Good to know
Develops neural network modeling skills, which are highly relevant in research, academia, and industry
Uses the MNIST data set, which is a standard in machine learning for handwritten digit recognition
Builds a foundation in neural network architecture, loss optimization, and model testing
Taught by Chris Shockley, a recognized expert in deep learning
Hands-on project-based learning approach to reinforce the concepts taught
May require additional resources or software that learners may not have readily available
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