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Hello, everyone, welcome to the program on Mathematics and Statistics for Machine Learning.

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If you really see machine learning or even deep learning is really at the intersection of domain knowledge,

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programming and mathematics.

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When I say mathematics, I'm not referring to theoretical mathematics, but rather the applied mathematics.

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That is how we apply the concepts of mathematics and statistics for the world of business.

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That's what is important in my experience.

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I have found that people wanting to learn machine learning straightaway jump into learning about algorithms

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and the associated programming.

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That is not the right way to go about learning machine learning.

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One must first understand the application of mathematics and statistics that are relevant for machine

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learning, and then one can learn about algorithms and the associated programming.

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So this program will cover the.

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Areas that I just mentioned, we will cover the concepts like central tendency and dispersion, dependent,

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independent variable hypothesis testing outliers, concepts that are relevant and useful for machine

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learning.

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We will also have a session on machine learning concepts and then we get into the math behind many of

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the algorithms like regression decision trees.

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And we will also have a session on how to measure the accuracy of algorithms and also gradient descent.

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So we have got an interesting mix of contents that will build a strong foundation for you to have a

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thriving career in artificial intelligence.

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I'm going and I come over two decades of experience managing technology operations and quality in both

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multinational companies as well as startups.

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I am currently the founder and CEO of an air venture called Spotify, and prior to that I have handled

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multiple leadership roles in Hewlett-Packard.

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I have launched products, machine learning and artificial intelligence based products in the areas

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of Industry 4.0 risk and customer experience.

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I'll be bringing these experiences and perspectives into this training program.

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So let's get started.