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Building a Machine Learning Ready Organization

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.

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What's inside

Syllabus

Building a Machine Learning Ready Organization

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

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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 Building a Machine Learning Ready Organization with these activities:
Brush up on your business management skills
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
Accelerate your learning by connecting with a mentor who can provide personalized guidance, share industry insights, and offer support.
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  • 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
Refresh your understanding of statistics and machine learning fundamentals to establish a solid foundation for the course.
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  • Review statistical concepts such as probability, distributions, and hypothesis testing
  • Revisit machine learning algorithms like linear regression, decision trees, and clustering
Six other activities
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Follow online tutorials to supplement course material
Expand your knowledge and understanding by exploring online tutorials that provide additional perspectives and examples on machine learning concepts covered in the course.
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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
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
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
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
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
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

Career center

Learners who complete Building a Machine Learning Ready Organization will develop knowledge and skills that may be useful to these careers:

Reading list

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Course image
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
Share and help others discover this course
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