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Most FutureLearn courses run multiple times. Every run of a course has a set start date but you can join it and work through it after it starts. Find out more This course is designed for individuals interested in artificial intelligence (AI) and its use in business. Please note that the individuals detailed in the ‘Who will you learn with?’ section below, are current staff members and may be subject to change.
Topics Covered
Read more
Most FutureLearn courses run multiple times. Every run of a course has a set start date but you can join it and work through it after it starts. Find out more This course is designed for individuals interested in artificial intelligence (AI) and its use in business. Please note that the individuals detailed in the ‘Who will you learn with?’ section below, are current staff members and may be subject to change.
Topics Covered
Read more
Most FutureLearn courses run multiple times. Every run of a course has a set start date but you can join it and work through it after it starts. Find out more This course is designed for individuals interested in artificial intelligence (AI) and its use in business. Please note that the individuals detailed in the ‘Who will you learn with?’ section below, are current staff members and may be subject to change.
Topics Covered
- Data sources
- Knowledge acquisition
- Knowledge representation
- Types of machine learning algorithms
- Decision-making processes
- Value creation
Register for this course and see more details by visiting:
OpenCourser.com/course/r8ulsf/using
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Reviews summary
Ai for business: strategic overview & decision-making
According to students, this course offers a solid introduction to integrating AI into business strategy and decision-making. Learners consistently praise its ability to demystify AI concepts for non-technical professionals, focusing on strategic implications and value creation. The explanations of machine learning types and decision-making processes are often highlighted as incredibly clear and helpful. While many find it perfect for understanding AI's impact, some intermediate learners suggest it remains too high-level, desiring deeper technical dives or more hands-on examples. Overall, it's seen as a valuable resource for grasping the essentials of AI in a business context.
Strong on strategic implications, less on hands-on implementation.
"I found it more theoretical than practical, which might disappoint if you're looking for hands-on business AI tools."
"The case studies were particularly helpful in showing real-world applications, which I appreciated."
"I would have loved more interactive exercises or templates for practical application."
Complex AI ideas are made accessible and easy to understand.
"The instructor made complex ideas accessible for someone without a technical background."
"I found the explanations of machine learning types and decision-making processes incredibly clear and actionable."
"This course provided me with a very solid introduction to using AI for business decisions."
Ideal for strategic application, not deep technical detail.
"As a business manager, I needed to understand how AI can genuinely impact our strategy, and this course delivered."
"This course is perfect for non-technical business professionals like me to grasp AI's strategic implications."
"I found this course demystified AI concepts and connected them directly to business planning."
Some examples may not reflect the very latest advancements.
"Some content felt slightly dated, and I was looking for cutting-edge applications."
"My only minor critique is that some technical parts could have been explained a bit deeper, perhaps with more current examples."
"I didn't find the practical applications as innovative or current as I had hoped."
Offers a broad introduction, but lacks advanced technical depth.
"It's good for absolute beginners like me, but not for those looking to deepen existing knowledge."
"The course provided a good overview, but I felt it stayed too high-level for my needs."
"I was hoping for more actionable insights or hands-on examples for implementation."
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Learners who complete Using Artificial Intelligence (AI) Technologies for Business Planning and Decision-making will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
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Reading list
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Provides a comprehensive overview of machine learning algorithms, covering a wide range of topics from linear regression to neural networks. It is written in a clear and concise style, making it accessible to readers with a variety of backgrounds.
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Addresses the learning strategies by describing the models of a number of different algorithms used in machine learning. Every chapter addresses a different learning algorithm and contains detailed diagrams and real-world applications.
Covers the topic of machine learning for data streams. Data streams are continuous flows of data that are too large to be stored in memory. This book shows how to use machine learning algorithms to process data streams in real time.
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Offers a unique perspective on machine learning by presenting it from a probabilistic standpoint. It covers a wide range of topics from Bayesian inference to Gaussian processes.
Provides a hands-on introduction to machine learning, covering a wide range of topics from data preparation to model evaluation. It is written by Andrew Ng, one of the leading researchers in the field of machine learning.
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Presents a unified view of machine learning from a Bayesian and optimization perspective. It covers a wide range of topics from supervised learning to reinforcement learning.
Provides a comprehensive overview of reinforcement learning, a subfield of machine learning that deals with how agents can learn to make decisions in complex environments. It is written by two of the leading researchers in the field.
Covers the topic of machine learning for text data. Text data special type of data that has unique characteristics. This book shows how to use machine learning algorithms to process text data.
Covers the topic of sparsity in machine learning. Sparsity property of data that has many zeros. This book shows how to use sparsity to improve the performance of machine learning algorithms.
Covers the topic of machine learning for audio, image and video analysis. This book shows how to use machine learning algorithms to process audio, images, and videos.
Provides a comprehensive overview of statistical learning, a subfield of machine learning that deals with supervised learning. It is written by three of the leading researchers in the field.
Provides a framework for evaluating the quality of strategies. Rumelt argues that good strategies are clear, focused, and aligned with an organization's capabilities.
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Draws on the author's experience as a professional poker player to provide insights into how to make better decisions under uncertainty. Duke emphasizes the importance of probabilistic thinking and risk management.
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Explores the science of habit formation and how we can use it to change our behavior. Duhigg provides practical advice on how to break bad habits and create good ones, making it relevant for personal decision-making and self-improvement.
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Explores the paradox of choice, arguing that having too many options can lead to indecision and regret. Iyengar provides practical advice on how to navigate choices effectively and make better decisions.
Provides a comprehensive and engaging exploration of the two systems of thinking that drive our behavior, System 1 (fast, intuitive) and System 2 (slow, rational). It examines how these systems interact and how we can use this knowledge to make better decisions.
Explores the systematic ways in which people defy rational economic predictions, offering numerous examples of irrational behavior in decision-making. It's excellent for gaining a broad understanding of behavioral economics and its impact on choices. It serves as valuable additional reading to complement theoretical concepts.
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Introduces the concept of 'nudges,' which are small changes to our surroundings that can influence our decisions. Thaler and Sunstein argue that nudges can be used to promote positive behavior change without restricting individual freedom.
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Presents a collection of 50 decision-making models that can be applied to a wide range of situations. Mauboussin provides clear and concise explanations of each model, along with real-world examples of how they can be used.
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Provides guidance on how to build trust and credibility as a professional. It covers topics such as communication, active listening, and problem-solving, which are essential skills for effective decision-making.
For more information about how these books relate to this course, visit:
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