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

## Introduction
>"***Deploying AI requires careful management to prevent unintentional but significant damage, not only to brand reputation but, more importantly, to workers, individuals, and society as a whole.***"

Though AI can supercharge businesses and society, **with great power comes great responsibility**. AI bias, discrimination, and privacy violations are concerns that can damage, often unintentionally, both brand reputation and society. Given the value at stake, in this section, we are going to talk about responsible AI.

Whether you are a Fortune 500 or a startup in a garage, all companies dealing with data and smart insights need to grapple with ethical considerations. Misuse of data, privacy violations, and ethical concerns (e.g., Cambridge Analytica scandal) have given news anchors much to talk about over the past few years, and rightly so. 


The value that AI brings to the table is too high for organizations to play it safe and not harness its power. Therefore, as a leader, **it is your responsibility to take the appropriate measures to embrace AI in a responsible manner**; it is your responsibility to prevent grave mistakes as your team deploys AI into your applications, even (or especially) when the use case seems innocuous. There have been too many cases of well-intentioned teams ending up with negative press for their smart work!

As a result, **leaders need to ensure that AI outputs are fair, that AI does not result in discrimination, data acquisition is not coming at the expense of users’ privacy, and your team is not driving up performance at the cost of lost transparency.**

Let’s dive into the key areas you should be challenging your teams to ensure that you are being a responsible “AI citizen.” Namely, in the next lessons, you will learn about **fairness, interpretability, privacy, and security**.



# Introduction
>"***Deploying AI requires careful management to prevent unintentional but significant damage, not only to brand reputation but, more importantly, to workers, individuals, and society as a whole.***"

Though AI can supercharge businesses and society, **with great power comes great responsibility**. AI bias, discrimination, and privacy violations are concerns that can damage, often unintentionally, both brand reputation and society. Given the value at stake, in this section, we are going to talk about responsible AI.

Whether you are a Fortune 500 or a startup in a garage, all companies dealing with data and smart insights need to grapple with ethical considerations. Misuse of data, privacy violations, and ethical concerns (e.g., Cambridge Analytica scandal) have given news anchors much to talk about over the past few years, and rightly so. 


The value that AI brings to the table is too high for organizations to play it safe and not harness its power. Therefore, as a leader, **it is your responsibility to take the appropriate measures to embrace AI in a responsible manner**; it is your responsibility to prevent grave mistakes as your team deploys AI into your applications, even (or especially) when the use case seems innocuous. There have been too many cases of well-intentioned teams ending up with negative press for their smart work!

As a result, **leaders need to ensure that AI outputs are fair, that AI does not result in discrimination, data acquisition is not coming at the expense of users’ privacy, and your team is not driving up performance at the cost of lost transparency.**

Let’s dive into the key areas you should be challenging your teams to ensure that you are being a responsible “AI citizen.” Namely, in the next lessons, you will learn about **fairness, interpretability, privacy, and security**.



In this lesson, you will learn about creating responsible AI. 

The Fundamentals

AI in Practice

Real Case Studies

Responsible AI

What's Next

Responsible AI Practices: Introduction

Introduction