About

Abhishek is a software architect, developer and Pluralsight author. He is very passionate about working with data especially in the field of machine learning. He has authored several courses on machine learning which are available on Pluralsight. He has been involved in several software development projects, which involves various machine learning techniques. His work focuses on architecting and developing applications especially in the area of monitoring, optimization, pattern recognition, and fault detection. His professional interests include software design patterns, agile practices, and various technologies such as WCF, WF, WPF, Silverlight, SQL Server, Entity Framework and ASP.NET MVC. He is also a Microsoft Certified Professional (HTML5, Javascript, CSS3).
My Pluralsight Courses

Course 1 : Introduction to Machine learning with ENCOG

Course 2 : Advanced Machine Learning with ENCOG

MCP(rgb)Course 3: Advanced Machine Learing with ENCOG – Part 2

Course 4 : R Programming Fundamentals

 

PS

 

 

 

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10 comments on “About
  1. Rafat Sarosh says:

    Liked your Machine learning course on PluralSight. Thanks for putting it.

  2. Ali Umair says:

    Thanks alot for Machine Learning Course, its really nice, Can i use ENCOG for Particle Swarm Optimization PSO Implementation
    ?

    • Thanks a lot for your appreciation. Yes, you can surely used encog for PSO implementation. You can take the existing implementation and tweak it as per your need. Infact, I will be covering PSO in third part of Advanced level course. I am currently working on second part , which will be available on pluralsight soon.

      Regards,
      Abhishek

  3. Daniel Rusch says:

    Greatly enjoyed the R programming course on PluralSight, you mentioned in it that you are working on an advanced course, any guess as to when it will be released? do you need a Guinea pig to give feedback on your draft?

  4. Kiran Randhawa says:

    Hi Abhishek,

    I really enjoyed your machine learning courses on pluralsight.

    One thing I noticed is that you tend to normalise after segregation. I was thinking that it would be better to normalise before segregation for the following reasons:

    1) If the data ranges happened to differ after the shuffle process. The normalisation ranges could differ in each file.

    2) You’re having to normalise three separate files as opposed to one operation.

    I wonder was there a specific reason that you chose to normalise after segregation?

    Would you advise that I normalise after segregation as opposed to before it?

    Also, I would really like you to do a course on Hidden Markov Models using Encog!

    Many Thanks,
    Kiran

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About Me


Software Architect, Developer & Pluralsight Author

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Disclaimer
The opinions expressed herein are my own personal opinions and do not represent my current or previous employer's view in anyway.
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