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Apache Spark is one of the most widely used and supported open-source tools for machine learning and big data. In this repo, discover how to work with this powerful platform for machine learning. This repo discusses MLlib—the Spark machine learning library—which provides tools for data scientists and analysts who would rather find solutions to business problems than code, test, and maintain their own machine learning libraries. Repo shows how to use DataFrames to organize data structure, and covers data preparation and the most commonly used types of machine learning algorithms: clustering, classification, regression, and recommendations. You will have experience loading data into Spark, preprocessing data as needed to apply MLlib algorithms, and applying those algorithms to a variety of machine learning problems.

Python 100.00%
spark-mllib spark-ml apache-spark python3

spark-mllib's Introduction

Hi there, I'm Ankit Desai 👋

I am a Chief Data Product Officer at Genuin Inc. responsible for building an AI based consumer app platform. With over 15 years of industry, research, and academic experience, I specialize in areas like Machine Learning, Data Mining, Distributed Systems, Data Engineering, and Deep Learning across Supply Chain & Logistics, EdTech, AdTech and Academic Research industries.

Key Skills

  • Architecting the data science projects from the scratch to production
  • Product-driven Data Science
  • KRA/KPI driven deliveries
  • Leading a team of Data Scientists & Data Engineers (Execution & Delivery)
  • Team Building and People management (Hire, Keep and coach people to be their best)
  • Data Analytics
  • Mentoring
  • Research and Development
  • Applied Research
  • Stakeholder Management (Product, Business, Client)

Academic Qualification

Experience

  • Chief Data Product Officer at Genuin Inc. (Apr 2023 - Present)
  • Director of Data Science at Locus.sh (Jul 2021 - Mar 2023)
  • Principal Data Scientist and Head of Applied Data Science at Embibe (Jul 2018 - Jul 2012)
  • Data Scientist at IQM Co. (Nov 2016 - Jun 2018)
  • Academic Research and Assistant Professor (Jul 2007 - Oct 2016)

Research Interests

  • Graph Mining
  • Mining Massive Data sets
  • Cost-sensitive Data Mining
  • Architecting the data science projects from scratch to production level

Achievements

  • Published multiple research papers in international conferences and journals
  • Served as a review committee member for conferences of ACM and IEEE

Values

I am a strong follower of values like Intellectually fierce, empathetically strong, vision-led, and closely knit.

Connect with me

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