We may earn an affiliate commission when you visit our partners.
AWS Instructor
The Building a Machine Learning Ready Organization course provides components needed for a successful organizational adoption of machine learning (ML). This course focuses on business leaders and other decision-makers currently or potentially involved in ML projects.
Register for this course and see more details by visiting:
OpenCourser.com/course/w6plo2/building
Three deals to help you save
We found three deals and offers that may be relevant to this course.
Save money when you learn.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
Valid until December 12
Annual Subscription Savings
Unlock programs from Google, Microsoft, and IBM and join 77% of learners who reported positive career outcomes.
Only $239!
Valid for a limited time only
Future-proof your career
Access O'Reilly books, live events, courses, and more. Save with an annual subscription.
Take
15%
off
What's inside
Syllabus
Building a Machine Learning Ready Organization
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches skills, knowledge, and tools that are highly relevant in industry
Taught by AWS Instructors, who are recognized for their work in AWS
Develops skills and knowledge necessary for a successful organizational adoption of machine learning
Course is appropriate for business leaders and other decision-makers currently or potentially involved in ML projects
Save this course
Save Building a Machine Learning Ready Organization to your list so you can find it easily later:
Save
Save
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 Building a Machine Learning Ready Organization with these
activities:
Brush up on your business management skills
Show steps
Building a successful ML organization needs foundational business knowledge. Refresh your business acumen to be able to fluently understand the language and concerns of stakeholders.
Browse courses on
Business Management
Show steps
-
Review your notes or textbooks on core business concepts such as strategy, finance, and marketing
-
Read business publications and attend industry events to stay up-to-date with current trends
-
Talk to colleagues and business leaders to gain insights into real-world challenges
Seek guidance from an experienced machine learning professional
Show steps
Accelerate your learning by connecting with a mentor who can provide personalized guidance, share industry insights, and offer support.
Browse courses on
Mentorship
Show steps
-
Identify potential mentors through professional networks, industry events, or online platforms
-
Reach out and request mentorship, outlining your goals and areas where you seek guidance
-
Establish regular communication and seek advice on course-related topics and career development
Review statistics and machine learning concepts
Show steps
Refresh your understanding of statistics and machine learning fundamentals to establish a solid foundation for the course.
Browse courses on
Statistics
Show steps
-
Review statistical concepts such as probability, distributions, and hypothesis testing
-
Revisit machine learning algorithms like linear regression, decision trees, and clustering
Six other activities
Expand to see all activities and additional details
Show all nine activities
Follow online tutorials to supplement course material
Show steps
Expand your knowledge and understanding by exploring online tutorials that provide additional perspectives and examples on machine learning concepts covered in the course.
Browse courses on
Online Learning
Show steps
-
Search for reputable online tutorials on specific machine learning topics
-
Follow the tutorials, taking notes and applying the concepts to your own projects or assignments
Solve practice problems on machine learning techniques
Show steps
Reinforce your understanding of machine learning concepts by solving practice problems, testing your knowledge and identifying areas for improvement.
Show steps
-
Find practice problems online or in textbooks related to the course topics
-
Attempt to solve the problems independently
-
Review your solutions against provided answers or consult with peers for feedback
Engage in study sessions with peers
Show steps
Enhance your understanding through peer collaboration, discussing course concepts, working on assignments together, and providing mutual support.
Show steps
-
Form study groups with classmates
-
Meet regularly to discuss course materials, share insights, and work on projects
-
Provide feedback and support to each other
Develop an ML-driven application
Show steps
Applying ML concepts in a project will help solidify your understanding and prepare you for real-world scenarios.
Show steps
-
Identify a problem or opportunity that can be addressed with ML.
-
Gather and prepare data relevant to the problem.
-
Choose and implement an ML algorithm appropriate for the data.
-
Evaluate the performance of the ML model and make necessary adjustments.
-
Deploy the ML model and monitor its performance.
Attend a machine learning workshop or conference
Show steps
Gain exposure to cutting-edge research, industry best practices, and networking opportunities by attending specialized machine learning events.
Show steps
-
Research and identify relevant workshops or conferences
-
Register and attend the event
-
Actively participate in sessions, ask questions, and network with professionals
Develop a machine learning project or prototype
Show steps
Apply your knowledge by creating a tangible project or prototype, solidifying your understanding and building a portfolio of practical skills.
Browse courses on
Machine Learning Projects
Show steps
-
Define the project scope and objectives
-
Gather and prepare data
-
Choose and implement appropriate machine learning algorithms
-
Evaluate the results and iterate on the project
-
Document and present your project
Brush up on your business management skills
Show steps
Building a successful ML organization needs foundational business knowledge. Refresh your business acumen to be able to fluently understand the language and concerns of stakeholders.
Browse courses on
Business Management
Show steps
Show steps
Show steps
- Review your notes or textbooks on core business concepts such as strategy, finance, and marketing
- Read business publications and attend industry events to stay up-to-date with current trends
- Talk to colleagues and business leaders to gain insights into real-world challenges
Seek guidance from an experienced machine learning professional
Show steps
Accelerate your learning by connecting with a mentor who can provide personalized guidance, share industry insights, and offer support.
Browse courses on
Mentorship
Show steps
Show steps
Show steps
- Identify potential mentors through professional networks, industry events, or online platforms
- Reach out and request mentorship, outlining your goals and areas where you seek guidance
- Establish regular communication and seek advice on course-related topics and career development
Review statistics and machine learning concepts
Show steps
Refresh your understanding of statistics and machine learning fundamentals to establish a solid foundation for the course.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review statistical concepts such as probability, distributions, and hypothesis testing
- Revisit machine learning algorithms like linear regression, decision trees, and clustering
Six other activities
Expand to see all activities and additional details
Show all nine activities
Follow online tutorials to supplement course material
Show steps
Expand your knowledge and understanding by exploring online tutorials that provide additional perspectives and examples on machine learning concepts covered in the course.
Browse courses on
Online Learning
Show steps
Show steps
Show steps
- Search for reputable online tutorials on specific machine learning topics
- Follow the tutorials, taking notes and applying the concepts to your own projects or assignments
Solve practice problems on machine learning techniques
Show steps
Reinforce your understanding of machine learning concepts by solving practice problems, testing your knowledge and identifying areas for improvement.
Show steps
Show steps
Show steps
- Find practice problems online or in textbooks related to the course topics
- Attempt to solve the problems independently
- Review your solutions against provided answers or consult with peers for feedback
Engage in study sessions with peers
Show steps
Enhance your understanding through peer collaboration, discussing course concepts, working on assignments together, and providing mutual support.
Show steps
Show steps
Show steps
- Form study groups with classmates
- Meet regularly to discuss course materials, share insights, and work on projects
- Provide feedback and support to each other
Develop an ML-driven application
Show steps
Applying ML concepts in a project will help solidify your understanding and prepare you for real-world scenarios.
Show steps
Show steps
Show steps
- Identify a problem or opportunity that can be addressed with ML.
- Gather and prepare data relevant to the problem.
- Choose and implement an ML algorithm appropriate for the data.
- Evaluate the performance of the ML model and make necessary adjustments.
- Deploy the ML model and monitor its performance.
Attend a machine learning workshop or conference
Show steps
Gain exposure to cutting-edge research, industry best practices, and networking opportunities by attending specialized machine learning events.
Show steps
Show steps
Show steps
- Research and identify relevant workshops or conferences
- Register and attend the event
- Actively participate in sessions, ask questions, and network with professionals
Develop a machine learning project or prototype
Show steps
Apply your knowledge by creating a tangible project or prototype, solidifying your understanding and building a portfolio of practical skills.
Browse courses on
Machine Learning Projects
Show steps
Show steps
Show steps
- Define the project scope and objectives
- Gather and prepare data
- Choose and implement appropriate machine learning algorithms
- Evaluate the results and iterate on the project
- Document and present your project
Career center
Learners who complete Building a Machine Learning Ready Organization will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/w6plo2/building
Reading list
We haven't picked any books for this reading list yet.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/w6plo2/building
Share
Similar courses
Here are nine courses similar to
Building a Machine Learning Ready Organization.
Applying Machine Learning to your Data with Google Cloud
OpenCourser.com/course/uqpsw1/applying
Applying Machine Learning to your Data with Google Cloud
Build Machine Learning Models with Azure Machine Learning Designer
OpenCourser.com/course/2fof3m/build
Build Machine Learning Models with Azure Machine Learning...
Machine Learning on AWS Deep Dive
OpenCourser.com/course/wq3grn/machine
Machine Learning on AWS Deep Dive
Google Cloud Certified Professional Machine Learning Engineer
OpenCourser.com/course/xsg6nf/google
Google Cloud Certified Professional Machine Learning...
Key Concepts Machine Learning
OpenCourser.com/course/klom90/key
Key Concepts Machine Learning
Introduction to Machine Learning: Art of the Possible
OpenCourser.com/course/fiakj8/introduction
Introduction to Machine Learning: Art of the Possible
How Google Does Machine Learning
OpenCourser.com/course/oq9e8v/how
How Google Does Machine Learning
Machine Learning Foundations for Product Managers
OpenCourser.com/course/ciz3vy/machine
Machine Learning Foundations for Product Managers
Applying Machine Learning to your Data with Google Cloud
OpenCourser.com/course/4on88h/applying
Applying Machine Learning to your Data with Google Cloud
Via
Coursera
Institution
Amazon Web Services
Instructor
AWS Instructor
Session
August 26, 2024
- September 9, 2024
Language
English
Good to know
Teaches skills, knowledge, and tools that are highly relevant in industry
Taught by AWS Instructors, who are recognized for their work in AWS
Develops skills and knowledge necessary for a successful organizational adoption of machine learning
Course is appropriate for business leaders and other decision-makers currently or potentially involved in ML projects
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser