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

Remember in high school when you had to plot data points on a graph with an X-axis and a Y-axis and then find the line of best fit? 

That is a very simple ML algorithm, linear regression. In technical terms, linear regression attempts to represent the relationship between one or more independent variables (points on X-axis) and a numeric outcome, or dependent variable (value on Y-axis), by fitting the equation of a line to the data. For example, you might want to relate the weights (Y) of individuals to their heights (X) using linear regression.
Linear regression models are widely used because of their simplicity.


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**Business use cases**
- **Predictive analytics**: demand and sales volume analysis e.g., number of items that a customer is likely to purchase
- **Operation efficiency**: optimize business processes, e.g., understand the impact of oven temperature on the shelf life of cookies or predict traffic jams.
- **Supporting decisions**: aid business management, e.g., given data on finances, operations, and customer purchases; make data-driven decisions instead of following your intuition alone; understand product-sales drivers such as competitive prices, distribution, advertisement, etc. 
- **New insights**: Over time businesses gather a large volume of unorganized data that has the potential to yield valuable new insights, e.g., analysis of data from point of sales systems and purchase accounts may highlight unexpected market patterns. 

For example, in the image below, we can see how linear regression aids traders in the selection and analysis of stocks: deviations above strong and established linear regression uptrend are opportunities to sell out positions, while deviations below the line indicate buying opportunities. 



In this lesson, we discuss examples of supervised learning algorithms in greater detail. 

The Fundamentals

AI in Practice

Real Case Studies

Responsible AI

What's Next

Supervised Learning: Algorithms and Business Use Cases I

Linear regression