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

# Unsupervised learning
In unsupervised learning, the data has no labels; the goal of the algorithm is to find relationships in the data. This system needs to learn without a teacher. For instance, say we have data about a website's visitors and we want to use it to find groupings of similar visitors. We don't know and can't tell the algorithm which group a visitor belongs to; it finds those connections without help based on some hidden patterns in the data. This type of ***customer segmentation*** is an example of what is known as **clustering** --- classification with no predefined classes and based on some unknown features. 

Another well-known use case is ***image compression***. When saving an image, if we set the palette, say, to 32 colors, clustering will find all the "blueish" pixels, calculate the "average blue," and set it for all the blue pixels. This helps us achieve a lower file size.

**Examples of unsupervised algorithms:**

- Clustering: k-Means
- Visualization and dimensionality reduction
- Principal component analysis (PCA), t-distributed 
- Stochastic neighbor embedding (t-SNE)
- Association rule learning: Apriori


This lesson introduces unsupervised learning. 

The Fundamentals

AI in Practice

Real Case Studies

Responsible AI

What's Next

Unsupervised Learning

Unsupervised learning