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The Planning a Machine Learning Project course introduces requirements to determine if ML is the appropriate solution to a business problem. 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/e23h7a/planning
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What's inside
Syllabus
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Tailored for business leaders and decision-makers involved or considering ML projects
Provides guidance on determining if ML is a suitable solution for business problems
Developed by AWS Instructors, recognized experts in cloud computing and ML
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Reviews summary
Strategic ml project planning for leaders
According to learners, this course is a highly valuable resource for business leaders and decision-makers. It provides a clear and concise overview of how to approach machine learning projects strategically, focusing on determining ML applicability to business problems rather than technical implementation. Students particularly appreciate the practical frameworks and insights that help bridge the gap between business objectives and technical teams. It's considered ideal for those seeking to understand the 'why' and 'what' of ML projects, but not the 'how' in a technical sense.
Provides a comprehensive, non-technical strategic perspective.
"I appreciated the high-level strategic guidance; it's exactly what managers need to plan effectively."
"The course offers a solid conceptual foundation for understanding ML project lifecycle from a strategic viewpoint."
"It's great for gaining a broad understanding of the planning phase without diving into complex algorithms."
Helps determine ML use for business challenges.
"I now have a much better framework for identifying business problems where ML could genuinely add value."
"The course excelled at showing me how to translate business needs into potential ML project requirements."
"It provided clear criteria to assess if ML is truly the appropriate solution for a given business case."
Perfectly tailored for non-technical professionals in leadership roles.
"This course hit the mark for me as a business leader looking to understand ML strategy without getting bogged down in code."
"As a decision-maker, I found the content incredibly relevant for planning and evaluating potential ML initiatives."
"It’s precisely what I needed to grasp the business perspective of machine learning, not the technical."
Does not cover coding, algorithms, or deep technical implementation.
"If you're an engineer or data scientist seeking technical depth, this course isn't for you. It's very high-level."
"I was not expecting coding, and indeed, it provides none—it's purely about the planning and strategic side."
"This course clearly states its target, so don't enroll if you want to learn specific ML models or programming."
Activities
Be better prepared
before
your course. Deepen your understanding
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it. Supplement your coursework and achieve mastery of the topics covered
in Planning a Machine Learning Project with these
activities:
Review prerequisite materials
Show steps
Review foundational concepts and materials to ensure a solid understanding of the fundamentals of machine learning.
Show steps
-
Review lecture notes and textbooks from previous courses
-
Complete practice problems and exercises
-
Take practice quizzes and exams
Compile a list of machine learning resources
Show steps
Create a valuable resource for future reference by gathering and organizing a comprehensive list of machine learning materials.
Show steps
-
Identify different types of machine learning resources
-
Search for and evaluate resources
-
Organize resources into a structured format
Follow online tutorials on machine learning algorithms
Show steps
Enhance understanding of specific machine learning algorithms and their applications by following guided tutorials.
Show steps
-
Identify specific algorithms to focus on
-
Find reputable online tutorials or courses
-
Follow the tutorials step-by-step
-
Implement the algorithms in a programming environment
Three other activities
Expand to see all activities and additional details
Show all six activities
Volunteer at a machine learning organization
Show steps
Gain practical experience and connect with professionals in the field of machine learning.
Show steps
-
Identify machine learning organizations that offer volunteer opportunities
-
Apply for a volunteer position
-
Participate in volunteer activities and learn from experienced professionals
Participate in a machine learning hackathon
Show steps
Challenge oneself to solve real-world problems using machine learning in a collaborative and competitive environment.
Show steps
-
Find a machine learning hackathon to participate in
-
Form a team or work individually
-
Develop a solution to the hackathon challenge
-
Submit the solution and compete for prizes or recognition
Write a report on machine learning best practices
Show steps
Enhance understanding of responsible and effective use of machine learning by researching and documenting best practices.
Browse courses on
Machine Learning Ethics
Show steps
-
Research different aspects of machine learning best practices
-
Summarize key findings in a written report
-
Identify areas for further exploration
Review prerequisite materials
Show steps
Review foundational concepts and materials to ensure a solid understanding of the fundamentals of machine learning.
Show steps
Show steps
Show steps
- Review lecture notes and textbooks from previous courses
- Complete practice problems and exercises
- Take practice quizzes and exams
Compile a list of machine learning resources
Show steps
Create a valuable resource for future reference by gathering and organizing a comprehensive list of machine learning materials.
Show steps
Show steps
Show steps
- Identify different types of machine learning resources
- Search for and evaluate resources
- Organize resources into a structured format
Follow online tutorials on machine learning algorithms
Show steps
Enhance understanding of specific machine learning algorithms and their applications by following guided tutorials.
Show steps
Show steps
Show steps
- Identify specific algorithms to focus on
- Find reputable online tutorials or courses
- Follow the tutorials step-by-step
- Implement the algorithms in a programming environment
Three other activities
Expand to see all activities and additional details
Show all six activities
Volunteer at a machine learning organization
Show steps
Gain practical experience and connect with professionals in the field of machine learning.
Show steps
Show steps
Show steps
- Identify machine learning organizations that offer volunteer opportunities
- Apply for a volunteer position
- Participate in volunteer activities and learn from experienced professionals
Participate in a machine learning hackathon
Show steps
Challenge oneself to solve real-world problems using machine learning in a collaborative and competitive environment.
Show steps
Show steps
Show steps
- Find a machine learning hackathon to participate in
- Form a team or work individually
- Develop a solution to the hackathon challenge
- Submit the solution and compete for prizes or recognition
Write a report on machine learning best practices
Show steps
Enhance understanding of responsible and effective use of machine learning by researching and documenting best practices.
Browse courses on
Machine Learning Ethics
Show steps
Show steps
Show steps
- Research different aspects of machine learning best practices
- Summarize key findings in a written report
- Identify areas for further exploration
Career center
Learners who complete Planning a Machine Learning Project will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/e23h7a/planning
Reading list
We haven't picked any books for this reading list yet.
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OpenCourser.com/course/e23h7a/planning
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