Hello, everyone, welcome to the program on Mathematics and Statistics for Machine Learning.
If you really see machine learning or even deep learning is really at the intersection of domain knowledge,
programming and mathematics.
When I say mathematics, I'm not referring to theoretical mathematics, but rather the applied mathematics.
That is how we apply the concepts of mathematics and statistics for the world of business.
That's what is important in my experience.
I have found that people wanting to learn machine learning straightaway jump into learning about algorithms
and the associated programming.
That is not the right way to go about learning machine learning.
One must first understand the application of mathematics and statistics that are relevant for machine
learning, and then one can learn about algorithms and the associated programming.
So this program will cover the.
Areas that I just mentioned, we will cover the concepts like central tendency and dispersion, dependent,
independent variable hypothesis testing outliers, concepts that are relevant and useful for machine
learning.
We will also have a session on machine learning concepts and then we get into the math behind many of
the algorithms like regression decision trees.
And we will also have a session on how to measure the accuracy of algorithms and also gradient descent.
So we have got an interesting mix of contents that will build a strong foundation for you to have a
thriving career in artificial intelligence.
I'm going and I come over two decades of experience managing technology operations and quality in both
multinational companies as well as startups.
I am currently the founder and CEO of an air venture called Spotify, and prior to that I have handled
multiple leadership roles in Hewlett-Packard.
I have launched products, machine learning and artificial intelligence based products in the areas
of Industry 4.0 risk and customer experience.
I'll be bringing these experiences and perspectives into this training program.
So let's get started.