About
Whether you’re training machine learning algorithms or performing a complex Analysis of data using statistical techniques the Quality and Quantity of your data determines the performance of your ML Model.
Today, organizations have a difficult time working with huge amount of datasets such as IoT, Click Stream, Mobile and Sensor Data etc.., In addition, big data processing and analyzing needs to be done in real time to gain valuable insights quickly. This is where Distributed Machine Learning comes in.
Understanding how to extract, process and analyze such huge amount of data will only become an ever more important skill for any data analyst / data scientist.
This training course is for you because…
- You are an aspiring or beginning data scientist/engineer.
- You have a comfortable intermediate-level knowledge of Python/Java/Scala and a very basic familiarity with statistics and linear algebra.
- You are a working programmer or student who is motivated to expand your skills to include machine learning and BigData.● You have some familiarity with the fundamentals of machine learning or have taken the Beginning Machine Learning.
Prerequisites
- A first course in Python and/or working experience as a programmer
- College level basic mathematics
- Recommended: Attend or view Beginning Machine Learning
Curriculum
- Fundamentals of Apache Spark and Machine Learning
- Analyzing massive amounts of data using Spark SQL
- Learning different Data Quality and Data Cleaning techniques
- Learning how to implement Spark ML pipelines
- Hands on experience with some Kaggle Projects
Schedule
Start learning the Distributed Machine Learning with Apache Spark with outside of business hours schedule!
The 12 hours of schedule is as follows:
April 12 – 19 – 26 and May 3
Sundays, from 9:00 am to 12:00 pm
Online
Distributed ML with Apache Spark Specialized Bootcamp is now also available online. Anyone who wants to attend this mini bootcamp can join online live webinars where the same course content will be taught. Online sessions will be distributed through zoom conferences. Students will have access to the screen of the instructor, external camera showing class atmosphere, whiteboard, and be able to ask questions through chat. You may attend this mini bootcamp no matter where you are.