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I came to the platform with no experience and now I am knowledgeable in Machine Learning with Python. No easy thing I must say, the sessions are challenging and go to the depths. I looked at graduate... Read More
I have 11 years of experience and work with IBM. My domain is Travel, Hospitality and Banking - both sectors process lots of data. The way the projects were set up and the mentors' explanation was... Read More
According to Wikipedia, Statistics is a branch of mathematics dealing with data collection, organization, analysis, interpretation and presentation. It is about building from collected data, a model that can enable humans to describe, analyze and infer event happening around. Statistics is in itself a conduit to the field of Machine Learning and AI.
In this Hackerday, we will go through the basis of statistics and see how Spark enables us to perform statistical operations like descriptive and inferential statistics over the very large dataset.
No knowledge of statistics is assumed in this session. Every concept will be discussed ground up and put to practice on the airline on-time performance dataset. We will conclude the session by introducing a number of machine learning algorithms available in MLlib.
In this NoSQL project, we will use two NoSQL databases(HBase and MongoDB) to store Yelp business attributes and learn how to retrieve this data for processing or query.
In this hive project, you will design a data warehouse for e-commerce environments.
The goal of this Spark project is to analyze business reviews from Yelp dataset and ingest the final output of data processing in Elastic Search.Also, use the visualisation tool in the ELK stack to visualize various kinds of ad-hoc reports from the data.