Machine learning is more popular than ever because there are several dataset available and we can use several tools at our disposal to learn an insight from this data.

In this session I shall show how F# can be used for several machine learning tasks and I will be using industry standard APIs

During this session participants will be solving several machine learning challenges from Kaggle like handwritten digit recognizer (https://www.kaggle.com/c/digit-recognizer)

During this session participants will write code in F# to solve real challenges like this one

https://gist.github.com/sudipto80/72e6e56d07110baf4d4d

and they will get to understand the process of machine learning system design pipeline.

 
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Outline/structure of the Session

In this you shall learn how to use

https://upload.wikimedia.org/wikipedia/commons/0/07/Weka_(software)_logo.png

 

In this Workshop you shall learn how to implement several algorithms to solve real world challenges from 

 

People will learn the following

1. F# Fundamentals (F# List comprehension)

2. Basics of Machine Learning

  • Understanding Training/Test Corpus

3. Classification Algorithms implemented to solve real problems

  •  K Nearest Neighbor
  •  Logistic Regression
  •  Multi-class Logistic Regression
  •  Decision Trees
  •  Similarity Measurements

4. Linear Regression with F# to predict real values

  • Simple Regression
  • Multiple Regression
  • Weighted Linear Regression
  • Ridge Regression
  • Multivariate Multiple Regression

5. Building Recommendation Systems

  •   Collaborative Filtering
  •   Item based filtering and recommendation

6. Clustering

  •  K Means Clustering to identify market segments

7. Ensemble Methods

  •  Random Forests
  •  AdaBoost
  •  Bagging

In general I shall show audience how to break a problem statement to a machine learning problem and how to solve it with existing APIs like WekaSharpAccord.NET and F#. All the visualization will be implemented using FsPlot  FsPlot helps draw the charts using HighCharts and is a very natural choice for using in F#. You can see the plot generated using FsPlot in action at http://recordit.co/SMNa4i7S8r

Learning Outcome

After attending this session

  • People will be able to identify a problem as a regression/classification/recommendation problem
  • Be able to find a solution using F# and other APIs (WekaSharp, FsPlot .NET, Accord.NET)

Target Audience

Software Developers, Architects, Data Scientists , Data Engineers

schedule Submitted 2 years ago

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comment Comment on this Proposal
  • Naresh Jain
    By Naresh Jain  ~  1 year ago
    reply Reply

    Hi Sudipta,

    Can this be condensed to a 90 mins demo? Unfortunately we won't be able to accommodate 1 day workshop.

  • Debasish Ghosh
    By Debasish Ghosh  ~  2 years ago
    reply Reply

    Regarding "Classification Algorithms implemented to solve real problems", can you please elaborate a bit on what algorithms will you cover ? And also will u discuss their implementations in F# ?

     

    Thanks.

    • Sudipta Mukherjee
      By Sudipta Mukherjee  ~  1 year ago
      reply Reply

      I shall cover the following algorithms

      • KNN
      • Logistic Regression
      • Decision Trees
      • SVM with several Kernels

      And yes. I shall discuss the implementations of all of these in F#


  • Sudipta Mukherjee
    Sudipta Mukherjee
    schedule 2 years ago
    Sold Out!
    90 mins
    Demonstration
    Intermediate

    This is a demo session about Squirrel framework. Squirrel in an open source data analytics framework for .NET

    https://github.com/sudipto80/Squirrel

     

    Here are some of the case studies that will be demoed. These questions will be posed and then the solution will be provided using Squirrel

    Hope that audience will be able to appreciate the elegance and simplicity of the solution that Squirrel provides.

     

    1. Do women pay more tip than men  ?

    2. How many accidents happen becasue of bird strikes ?

    3. Which country is famous for which sport in Olympics ?

     

    Please visit Squirrel github page https://github.com/sudipto80/Squirrel to see some of these examples

    At the end of this session, data from Squirrel will be taken and then some infographics will be generated that can make life lot simpler for data journalists.

    In other words, it will be the attempt to make each developer realize that they can be a data journalist.

     

    We shall have few T-Shirts to give away with the nice Squirrel logo and our motto. But I hope you shall have other motivation to come to this session :)