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

## What are recommendations?

How does Netflix know exactly what you might want to watch next? How does your Instagram feed know what type of content to show you to keep you hooked? This “*magic*” is powered by recommendation systems: algorithms that are able to provide the most relevant and accurate items to the user from a huge pool of content base. They are able to discover patterns in the data to provide a personalized user experience. When talking about creating personalized experiences, we mainly come across two types of recommendations:

- **Home feed**: recommendations personalized to a user based on their interests, e.g., your Netflix homepage 
- **"More Like This"/Related items**: recommendations similar to a particular item, e.g., movies like the one you watched last time or the news article you just read

# What are recommendations?

How does Netflix know exactly what you might want to watch next? How does your Instagram feed know what type of content to show you to keep you hooked? This “*magic*” is powered by recommendation systems: algorithms that are able to provide the most relevant and accurate items to the user from a huge pool of content base. They are able to discover patterns in the data to provide a personalized user experience. When talking about creating personalized experiences, we mainly come across two types of recommendations:

- **Home feed**: recommendations personalized to a user based on their interests, e.g., your Netflix homepage 
- **"More Like This"/Related items**: recommendations similar to a particular item, e.g., movies like the one you watched last time or the news article you just read

This lesson covers recommendation systems in detail.


The Fundamentals

AI in Practice

Real Case Studies

Responsible AI

What's Next

Recommendation Systems

What are recommendations?