INTUITIVE UNDERSTANDING OF MACHINE LEARNING

Aditya, Head of Technology at Aritha, breaks down the ML terminology used, explains regression in algorithms using simple examples, walks you through the steps of creating an ML algorithm right from Data acquisition, Feature Engineering, Fitting the model, to Evaluation. You will get to know the pre-requisites to learning ML like learning programming languages such as Python/Scala, Functional programming, basic understanding of Maths/Statistics and other ML areas like prediction and classification. You will also get to know the other skills and knowledge needed to implement ML algorithms like Platform knowledge, Spark, tools for data ingestion etc.

At the end of this video you will have a good understanding on how an ML Algorithm is continuously learning with each data set you feed it, its business value, what makes it a green field implementation and how all these justify Gartner’s prediction of a 3.9 trillion dollar industry by 2022 for ML.