Data Science 101: Machine Learning, Part 1

MACHINE LEARNING SERIES – PART 1

There has been a lot of talk about machine learning – most notably by digital marketers and others who see this technology as a boon to their business pursuits. But have you ever asked yourself “what is machine learning”, or wondered how it actually works? BloomReach engineer Srinath Sridhar walks through probability, Bayesian models and machine learning in this 5 part video series.

Machine learning, a branch of artificial intelligence, is about the construction and study of systems that can learn from data.”

This first lecture goes over some fundamental definitions of statistics that are needed for any rigorous analysis of machine learning algorithms. The presentation will define random variable, sample space, probability, expectation, standard deviation and variance and go over examples of discrete and continuous probability distributions. Time is also spent on uniform, binomial and normal (Gaussian) distributions along with other distributions such as Poisson, exponential, geometric and negative binomial.

 

 

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