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Jared Heywood
In this course, you will discover how to solve business problems with machine learning, no coding required. You will explore Amazon SageMaker Canvas, a visual point-and-click interface that allows you to generate accurate ML predictions without requiring any machine learning experience or having to write a single line of code. At the end of the course, you will walk away understanding how to make better business decisions using no-code machine learning.
Register for this course and see more details by visiting:
OpenCourser.com/course/blir8m/practical
What's inside
Syllabus
Practical decision making using no-code ML on AWS
Discover how to solve business problems with machine learning. No coding required using Amazon SageMaker Canvas.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Designed for beginners in machine learning and those without coding expertise
No-code learning: Allows for quick and easy implementation of machine learning solutions without the need for complex coding
Familiarises learners with Amazon SageMaker Canvas for practical application of machine learning
Provides a clear understanding of how machine learning can enhance business decisions
Save this course
Save Practical Decision-Making Using No-code ML on AWS to your list so you can find it easily later:
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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 Practical Decision-Making Using No-code ML on AWS with these
activities:
Connect with ML Professionals on LinkedIn
Show steps
Expand your network and gain insights by connecting with experienced ML professionals on LinkedIn.
Browse courses on
Mentoring
Show steps
-
Search for and identify potential mentors on LinkedIn
-
Send personalized connection requests and messages
-
Build relationships and seek guidance on your ML journey
Read 'Machine Learning for Dummies'
Show steps
Gain a comprehensive foundation in ML concepts and applications by reading this beginner-friendly book.
View
Data Analytics & Visualization All-in-One For...
on Amazon
Show steps
-
Read through the chapters and understand the fundamental principles of ML
-
Focus on understanding the no-code ML approach
Join a Study Group for Amazon SageMaker Canvas
Show steps
Enhance your learning by collaborating with peers in a study group dedicated to Amazon SageMaker Canvas.
Show steps
-
Find or create a study group with fellow learners
-
Meet regularly to discuss course concepts and work on problems together
-
Share knowledge, ask questions, and provide support to each other
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow ML Training Tutorials on AWS
Show steps
Enhance your understanding of machine learning concepts and the Amazon SageMaker Canvas platform by working through guided tutorials.
Browse courses on
Machine Learning
Show steps
-
Identify relevant tutorials on the AWS website or Coursera platform
-
Follow the step-by-step instructions provided in the tutorials
-
Experiment with different parameters and scenarios to gain practical experience
Practice ML Problem-Solving on Kaggle
Show steps
Reinforce your ML problem-solving abilities by engaging in hands-on practice on the Kaggle platform.
Browse courses on
Machine Learning
Show steps
-
Identify suitable ML challenges or competitions on Kaggle
-
Read and understand the problem statement and data provided
-
Develop and implement ML models to address the challenge
-
Evaluate and refine your models to improve performance
Attend Amazon SageMaker Canvas Workshop
Show steps
Deepen your understanding of Amazon SageMaker Canvas and best practices for no-code ML by attending an official workshop.
Show steps
-
Register for an Amazon SageMaker Canvas workshop
-
Attend the workshop and actively participate in hands-on exercises
-
Network with experts and fellow learners
Develop an ML Model for a Business Case
Show steps
Apply your ML skills to a real-world scenario by developing an ML model to solve a business problem.
Browse courses on
Machine Learning
Show steps
-
Identify a business problem that can be addressed with ML
-
Gather and prepare relevant data
-
Develop and train an ML model using Amazon SageMaker Canvas
-
Evaluate and refine the model's performance
-
Present the model and its insights to stakeholders
Participate in a No-Code ML Hackathon
Show steps
Challenge yourself and showcase your skills by participating in a no-code ML hackathon.
Browse courses on
Machine Learning
Show steps
-
Find and register for a no-code ML hackathon
-
Form a team or work individually on the challenge
-
Develop a creative ML solution to the problem statement
-
Present your solution to a panel of judges
Connect with ML Professionals on LinkedIn
Show steps
Expand your network and gain insights by connecting with experienced ML professionals on LinkedIn.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Search for and identify potential mentors on LinkedIn
- Send personalized connection requests and messages
- Build relationships and seek guidance on your ML journey
Read 'Machine Learning for Dummies'
Show steps
Gain a comprehensive foundation in ML concepts and applications by reading this beginner-friendly book.
View
Data Analytics & Visualization All-in-One For...
on Amazon
Show steps
Show steps
Show steps
- Read through the chapters and understand the fundamental principles of ML
- Focus on understanding the no-code ML approach
Join a Study Group for Amazon SageMaker Canvas
Show steps
Enhance your learning by collaborating with peers in a study group dedicated to Amazon SageMaker Canvas.
Show steps
Show steps
Show steps
- Find or create a study group with fellow learners
- Meet regularly to discuss course concepts and work on problems together
- Share knowledge, ask questions, and provide support to each other
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow ML Training Tutorials on AWS
Show steps
Enhance your understanding of machine learning concepts and the Amazon SageMaker Canvas platform by working through guided tutorials.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify relevant tutorials on the AWS website or Coursera platform
- Follow the step-by-step instructions provided in the tutorials
- Experiment with different parameters and scenarios to gain practical experience
Practice ML Problem-Solving on Kaggle
Show steps
Reinforce your ML problem-solving abilities by engaging in hands-on practice on the Kaggle platform.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify suitable ML challenges or competitions on Kaggle
- Read and understand the problem statement and data provided
- Develop and implement ML models to address the challenge
- Evaluate and refine your models to improve performance
Attend Amazon SageMaker Canvas Workshop
Show steps
Deepen your understanding of Amazon SageMaker Canvas and best practices for no-code ML by attending an official workshop.
Show steps
Show steps
Show steps
- Register for an Amazon SageMaker Canvas workshop
- Attend the workshop and actively participate in hands-on exercises
- Network with experts and fellow learners
Develop an ML Model for a Business Case
Show steps
Apply your ML skills to a real-world scenario by developing an ML model to solve a business problem.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify a business problem that can be addressed with ML
- Gather and prepare relevant data
- Develop and train an ML model using Amazon SageMaker Canvas
- Evaluate and refine the model's performance
- Present the model and its insights to stakeholders
Participate in a No-Code ML Hackathon
Show steps
Challenge yourself and showcase your skills by participating in a no-code ML hackathon.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find and register for a no-code ML hackathon
- Form a team or work individually on the challenge
- Develop a creative ML solution to the problem statement
- Present your solution to a panel of judges
Career center
Learners who complete Practical Decision-Making Using No-code ML on AWS will develop knowledge and skills
that may be useful to these careers:
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course may be useful for Software Engineers as it provides a foundation in ML and teaches how to use no-code ML to solve business problems and make better decisions.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers develop and deploy ML models. This course may be useful for Machine Learning Engineers as it teaches how to use no-code ML to solve business problems and make better decisions.
Data Scientist
Data Scientist
Data Scientists use data to build models and solve business problems. This course may be useful for Data Scientists as it provides a foundation in ML and teaches how to use no-code ML to solve business problems and make better decisions.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design and develop AI systems. This course may be useful for Artificial Intelligence Engineers as it provides a foundation in ML and teaches how to use no-code ML to solve business problems and make better decisions.
Marketing Manager
Marketing Manager
Marketing Managers develop and execute marketing campaigns to promote products and services. This course may be useful for Marketing Managers as it teaches how to use no-code ML to solve business problems and make better decisions.
Product Manager
Product Manager
Product Managers develop and manage products from ideation to launch. This course may be useful for Product Managers as it teaches how to use no-code ML to solve business problems and make better decisions.
Operations Manager
Operations Manager
Operations Managers oversee the day-to-day operations of a business. This course may be useful for Operations Managers as it teaches how to use no-code ML to solve business problems and make better decisions.
Consultant
Consultant
Consultants provide advice to businesses on a variety of topics, including strategy, operations, and marketing. This course may be useful for Consultants as it teaches how to use no-code ML to solve business problems and make better decisions.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. This course may be useful for Actuaries as it teaches how to use no-code ML to solve business problems and make better decisions.
Market Research Analyst
Market Research Analyst
Market Research Analysts conduct research on market conditions, customer demographics, and buying trends to help businesses make informed decisions. This course may be useful for Market Research Analysts as it teaches how to use no-code ML to solve business problems and make better decisions.
Entrepreneur
Entrepreneur
Entrepreneurs start and run their own businesses. This course may be useful for Entrepreneurs as it teaches how to use no-code ML to solve business problems and make better decisions.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data to make recommendations on investments and other financial decisions. This course may be useful for Financial Analysts as it teaches how to use no-code ML to solve business problems and make better decisions.
Sales Manager
Sales Manager
Sales Managers lead and manage sales teams to achieve sales targets. This course may be useful for Sales Managers as it teaches how to use no-code ML to solve business problems and make better decisions.
Business Analyst
Business Analyst
Business Analysts help businesses improve their performance by analyzing business processes and identifying areas for improvement. This course may be useful for Business Analysts as it teaches how to use no-code ML to solve business problems and make better decisions.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. This course may be useful for Data Analysts as it teaches how to use no-code ML to solve business problems and make better decisions.
For more career information including salaries, visit:
OpenCourser.com/course/blir8m/practical
Reading list
We've selected 12 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
Practical Decision-Making Using No-code ML on AWS.
Save
Provides a comprehensive overview of deep learning, covering the latest advances in the field. This book valuable resource for learners who want to stay up-to-date on the latest developments in deep learning.
Provides a practical guide to machine learning using popular libraries such as Scikit-Learn, Keras, and TensorFlow. It great resource for learners who want to apply machine learning to real-world problems and projects.
Provides a comprehensive overview of pattern recognition and machine learning. It great resource for learners who want to understand the mathematical foundations of machine learning.
Provides a comprehensive overview of statistical learning. It great resource for learners who want to understand the mathematical foundations of machine learning.
Provides a practical guide to machine learning using a variety of programming languages. It great resource for learners who want to learn how to use machine learning for real-world projects.
Provides a practical guide to machine learning for hackers. It great resource for learners who want to learn how to use machine learning to solve real-world problems.
Provides a comprehensive introduction to machine learning using the Python programming language. It great resource for learners who want to learn the fundamentals of machine learning.
Provides a practical guide to deep learning using the Fastai and PyTorch libraries. It great resource for learners who want to learn how to use deep learning for real-world projects.
Provides a practical guide to machine learning using the R programming language. It great resource for learners who want to learn how to use R for machine learning projects.
Provides a comprehensive introduction to machine learning, covering the fundamental concepts and algorithms. It great resource for beginners who want to learn more about the field.
Provides a comprehensive overview of interpretable machine learning. It great resource for learners who want to understand how to make machine learning models more interpretable.
Provides a more theoretical and mathematical treatment of machine learning. It great resource for learners who want to understand the underlying principles of machine learning and how to apply them to different types of problems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/blir8m/practical
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Amazon Web Services
Instructor
Jared Heywood
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Good to know
Designed for beginners in machine learning and those without coding expertise
No-code learning: Allows for quick and easy implementation of machine learning solutions without the need for complex coding
Familiarises learners with Amazon SageMaker Canvas for practical application of machine learning
Provides a clear understanding of how machine learning can enhance business decisions
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