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

Neural networks come in many flavors and varieties. Convolutional neural networks (ConvNets or CNN) are one of the most well known and important types of neural networks. They are mainly used in the context of computer vision tasks like smart tagging of your pictures, turning your old black and white family photos into colored images, and powering vision in self-driving cars. Recently, researchers have also started exploring them in the domain of Natural Language Processing. One other noteworthy application of CNN is drug discovery, as they allow us to predict interactions between molecules and biological proteins, which paves the way for discovering new groundbreaking medical treatments. Fascinating isn’t it?! So, let’s try to understand the building blocks of these “seeing” neural networks.

# How do we teach computers to “see”?
Our brains have the capacity to process visual information within as little as 13 milliseconds. To put this into perspective, it takes us 300-400 ms to blink our eyes! And all of this happens without us even realizing the complex mechanism going on in our brains. However, if we want machines to understand visual information, we need to understand how to represent images in a machine-readable format.

# Image representation

In this lesson, you will learn about convolutional neural networks (CNN). 


The Fundamentals

AI in Practice

Real Case Studies

Responsible AI

What's Next

Convolutional Neural Networks: What, When, How and Where