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Machine Learning for Business & Technical Decision Makers

In this course, you will learn about best practices and recommendations for machine learning (ML). The course explores how to roadmap for integrating ML into your business processes, explores requirements to determine if ML is the appropriate solution to a business problem, and describes what components are needed for a successful organizational adoption of ML.

We recommend that attendees of this course have:

• Basic knowledge of computers and computer systems

• Some basic knowledge of the concept of machine learning

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What's inside

Syllabus

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Taught by AWS instructors, who are recognized for their work in the field of machine learning

Explores various aspects and applications of machine learning across different industries and domains

Strong emphasis on best practices and recommendations for implementing machine learning solutions effectively

Teaches learners how to evaluate if machine learning is the right solution for specific business problems

Covers key considerations for successful organizational adoption of machine learning

Designed for learners with basic knowledge of computers and machine learning concepts

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Reviews summary

Strategic machine learning for business leaders

According to students, this course is a highly valuable resource for business and technical decision makers seeking to understand Machine Learning's strategic implications. Learners praise its ability to demystify complex ML concepts and bridge the gap between technical details and business application. The course is particularly strong in providing a high-level overview focused on strategic decision-making and organizational adoption, making it ideal for non-technical managers and leaders. While a significant majority found it incredibly beneficial, some with prior ML knowledge noted it could be too introductory or general, wishing for more in-depth practical examples or advanced frameworks. Overall, it's not a coding course, fulfilling its promise to equip leaders to speak intelligently about ML and guide strategic initiatives.

Best suited for those new to ML or in strategic business roles.

"A fantastic overview for non-technical managers. Highly recommend for business strategy."

"Good for absolute beginners, maybe."

"It demystifies the jargon and provides a clear framework for thinking about ML."

Successfully connects ML's technical aspects to practical business needs.

"As a technical manager, I found this course excellent for bridging the gap between deep technical ML concepts and their real-world business implications."

"It helped me frame conversations with our data science team."

"I found the modules on identifying ML opportunities and organizational adoption very helpful."

Emphasizes ML's impact on business strategy and decision-making.

"This course is incredibly valuable for anyone in a leadership role trying to understand how ML impacts business."

"It really clarified the 'why' behind ML investments. The case studies were very relevant."

"Perfect for a non-technical leader who needs to understand the landscape of ML. Provides a clear framework for thinking about ML in a business context."

Some practical examples felt generic, lacking industry-specific detail.

"Some parts felt a bit too general, and I wish there were more practical examples of ML project implementation challenges specific to different industries."

"The only minor critique is that some of the examples felt a bit generic, but still applicable."

"I didn't feel like I gained a lot of new, actionable insights."

May be too introductory for those with prior ML knowledge.

"While the course provides a decent overview, I felt it lacked depth in certain areas. For someone with a basic ML background, it felt a bit too introductory."

"Unfortunately, it was too superficial for my needs. It mostly covered basic definitions and concepts that I already knew."

"Not enough actionable advice for experienced professionals."

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 for Business & Technical Decision Makers with these activities:

Review Machine Learning Concepts

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Reviewing machine learning concepts will help you build a stronger foundation and better prepare you for the course.

Browse courses on Machine Learning

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Read introductory articles or blog posts about machine learning.
Watch videos or tutorials on machine learning basics.
Complete practice exercises or quizzes to test your understanding.

Join a Study Group or Discussion Forum

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Participating in a study group or discussion forum will allow you to connect with other students, share knowledge, and clarify concepts.

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Identify study groups or discussion forums related to machine learning.
Join the group or forum and actively participate in discussions.
Ask questions, share insights, and help others.

Follow Tutorials on Machine Learning Algorithms

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Following tutorials on machine learning algorithms will help you develop practical skills and deepen your understanding of different algorithms.

Browse courses on Machine Learning Algorithms

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Identify machine learning algorithms that are relevant to the course.
Find tutorials or online courses that cover these algorithms.
Follow the tutorials step-by-step and implement the algorithms.
Test the algorithms on sample datasets.

Three other activities

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Solve Machine Learning Practice Problems

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Solving machine learning practice problems will improve your problem-solving skills and reinforce your knowledge.

Browse courses on Coding Challenges

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Find practice problems or coding challenges related to machine learning.
Attempt to solve the problems independently.
Compare your solutions with others or seek help if needed.

Create a Machine Learning Project Presentation

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Creating a machine learning project presentation will help you synthesize your knowledge, develop communication skills, and showcase your understanding.

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Choose a machine learning project to work on.
Implement the project and collect results.
Create a presentation that explains the project, methods, and outcomes.
Present your project to your peers or instructors.

Start a Personal Machine Learning Project

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Starting a personal machine learning project will allow you to apply your skills, explore your interests, and build a portfolio.

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Identify a problem or domain you are interested in.
Gather data and explore different machine learning algorithms.
Implement and evaluate a machine learning model.
Deploy or share your project.

Career center

Learners who complete Machine Learning for Business & Technical Decision Makers will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 49 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 for Business & Technical Decision Makers.

Artificial Intelligence

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The definitive benchmark textbook of modern artificial intelligence, rigorously unifying search algorithms, probabilistic reasoning, knowledge representation, and machine learning into an elegant, coherent agent-based framework.

Artificial Intelligence: A Modern Approach

Hardcover

$$$$

Hands-On Machine Learning with Scikit-Learn, Keras,...

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The premier applied machine learning masterwork, taking practitioners through intuitive concept breakdowns, production-grade Scikit-Learn workflows, and deep neural architecture implementations.

Machine Learning for Business & Technical Decision Makers

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What's inside

Syllabus

Traffic lights

Save this course

Reviews summary

Strategic machine learning for business leaders

Activities

Career center

Reading list

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