A code-free intro to artificial intelligence, ML, & data science for professionals, marketers, managers, & executives
What you will learn
This course provides students with a broad introduction to AI, and a foundational understanding of what AI is, what it is not, and why it matters.
The main differences between building a prediction engine using human-crafted rules and machine learning – and why this difference is central to AI.
Three key capabilities that AI makes possible, why they matter, and what AI applications cannot yet do.
The types of data that AI applications feed on, where that data comes from, and how AI applications – with the help of ML – turn this data into ‘intelligence’.
The main principles behind the machine learning and deep learning approaches that power the current wave of AI applications.
Artificial neural networks and deep learning: the reality behind the hype.
Three main drivers of risks which are characteristic of AI, why they arise, and their potential consequences in a workplace environment.
An overview of how AI applications are built – and who builds them (with the help of extended analogy).
Why one of the biggest problems the AI industry faces today – a pronounced skills gap – represents an opportunity for students.
How to use their own knowledge, skills and expertise to provide valuable contributions to AI projects.
Students will learn how to build upon the foundations they learned upon in this course, to make the move from informed observer to valuable contributor.
Why take this course?
🌟 Unlock the Power of AI in Your Business with “AI Foundations for Business Professionals” 🌟
Course Description:
Embark on a journey through the world of Artificial Intelligence (AI), Machine Learning (ML), and Data Science without writing a single line of code. This course is meticulously designed for business professionals, marketers, managers, and executives who aim to understand how these technologies can transform decision-making processes, strategies, and operations within their organizations.
What You’ll Learn:
Module 1: Demystifying AI 🧐
- Lecture 1: Unpack the various definitions of AI, understand its objectives, and explore why it sparks both excitement and skepticism in the business world.
- Lecture 2: Introduce prediction engines and explore the basics of machine learning – your gateway to understanding how AI systems learn from data.
- Lecture 3: Dispelling myths about AI and setting realistic expectations for what these technologies can achieve.
Module 2: Building a Prediction Engine 🔧
- Lecture 4: Dive into the anatomy of AI – inputs, the model itself, and outputs.
- Lecture 5: Compare different approaches to building prediction engines through a gentle introduction. Plus, a hands-on example with a jacket sales prediction engine.
- Lecture 6: Weigh the pros and cons of human-crafted rules versus machine learning for automation tasks.
Module 3: New Capabilities… and Limitations 📈
- Lecture 7: Discover how AI can automate an ever-growing range of tasks, enhance decision-making with new insights, and offer granular personalization at an affordable price.
- Lecture 8: Acknowledge the limitations of current AI applications and understand what they can’t do well.
Module 4: From Data to ‘Intelligence’ 📊
- Lecture 9: Learn what constitutes data, the difference between structured and unstructured data, and how ML unlocks new insights across various data types.
- Lecture 10: Understand the practical applications of AI, including how it can make predictions and provide automated instructions, and when to trust a machine’s decisions.
Module 5: Machine Learning Approaches 🧠
- Lecture 11: Get familiar with three fundamental definitions in ML that set the stage for understanding its applications.
- Lecture 12: Break down what an algorithm is, differentiate traditional algorithms from ML ones, and understand the role of a model in machine learning.
- Lecture 13: Explore various machine learning approaches, including supervised, unsupervised learning, and touch on neural networks and deep learning.
Module 6: Risks and Consequences ✊
- Lecture 17: Learn how data is at the heart of AI systems and why it’s similar to oil in its importance.
- Lecture 18: Get an insider’s view of the anatomy of an AI project, the mission of a data scientist, and the critical role of domain expertise.
Module 7: The Importance of Domain Expertise 🔑
- Lecture 19: Identify the skills gap in AI projects and understand how you can bridge it by marrying technical skills with your own domain expertise.
- Lecture 20: Discover what you know as a professional that data scientists might not and learn how to leverage your knowledge for AI initiatives.
Bonus Module: From Observer to Contributor 🚀
- Lecture 21: Transition from being an observer of AI trends to actively contributing to AI projects within your organization, leveraging your unique insights and skills.
Why Enroll?
- Comprehensive Coverage: This course offers a holistic view of AI, ML, and Data Science, tailored for professionals who are not developers.
- Real-World Relevance: Learn how to apply AI concepts directly to your business problems and strategies.
- Expert Insights: Gain knowledge from an experienced educator who brings the subject to life with practical examples and real-world applications.
- Empowerment: Equip yourself with the tools and understanding necessary to confidently engage in AI discussions and projects within your industry.
Join us on this transformative learning journey and become a leader who can harness the full potential of AI in your business endeavors. 🚀💡
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