Lead the GenAI revolution. Future-proof your skills by understanding AI basics, machine learning, algorithms, ethical practices, and real-world applications. Stay ahead in tech leadership.

In one form or the other, AI is going to be infused in all the tech products. Meaning, that in order to stay a relevant leader, it has become essential to have a solid, broad understanding of AI. While you may not be implementing the solution, you need to speak the language of AI.
 
Whether you’re leading a team on creating AI solutions, or you just want a better understanding of AI, then this course is for you. In this course, I have deconstructed the concepts most relevant for you.
 
You will start by learning the basics of AI, its connection with machine learning, and the different types of algorithms you should be familiar with. You’ll then learn the rules, best practices, and infrastructure for creating great AI products that users trust. Then, you will go through some real case studies of AI application and do a deep dive into Ethical AI. You'll end by getting an outlook into the current status and future of AI. In a nutshell, this course is your no-fuss, comprehensive guide to the essentials of AI.

Grokking AI for Engineering & Product Managers

# Interpretability 

***AI systems should be understandable.***

We touched upon this topic when talking about creating AI solutions that users trust. Interpretability, or explainability, plays a crucial role when it comes to trusting AI systems. 

Automated predictions and decision making are becoming integrated into our lives more and more. In some scenarios, it can be acceptable to have AI systems that make decisions we do not fully understand. For example, if a healthcare application classifies an image as cancerous, the patient wouldn’t likely be interested in knowing how the algorithm is able to make those predictions; whether the deep learning model was trained using biomarker levels or skin color of the patient, wouldn’t be their primary concern. However, if there is a loan application that denies legit requests or a surveillance system that shows bias in classifying people, then in order to establish the human-AI trust, we want to have a better understanding of how those decisions are made. In the latter scenarios, we want to be able to explain the model outputs to stakeholders, and we cannot get away with black-box or opaque models.



In addition to being fair, AI should be understandable. 


The Fundamentals

AI in Practice

Real Case Studies

Responsible AI

What's Next

Responsible AI Practices: Interpretability