Machine Learning Study Group Recap – Week 1

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So many of you who are here are probably part of the study group.  For those who are not or are perhaps referencing this at a later time, this is in regards to the following course on Coursera. If you would like to join our study group, please see one of the following meetup pages: Fort Lauderdale Machine Learning or Florida Dot Net.

Here in South Florida we have a strong Machine Learning and Data Science community and therefor it is easy to get a study group together.  This article is a recap from the first meeting of our study group.  Note that this first meeting is the week before the class started.  Therefor this article is a great introduction to machine learning, languages, commitments and more generally applicable questions and concerns.

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Linear Regression from Scratch

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So today we will do a quick conversion from mathematical notations of Algebra into a real algorithm that can be executed.  Note we will not be covering gradient descent, but rather only cost functions, errors and execution of these to provide the framework for gradient descent.  Gradient descent has so many flavors that it deserves its own article.

So to the mathematical representation.

LinearRegression

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Intro to R Data Structures

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This article is a video tutorial on introduction to the very bare basics of R.  Its a bit dry, but it is the underlying components of everything covered in the interesting stuff.  Can’t do cool stuff without understanding the basics first.

Part 1: Introduction to Microsoft R Open.

Part 2: Introduction to R Data Structures

Part 3: Data Manipulation with R

Part 4: Beautiful Visualizations with R

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Powering AzureML with Hadoop HBase

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Today is a freaking cool day.  Why do you ask?  Because today I am writing an article on how to use two of the coolest freaking big data/data science tools out there together to do epic shit!  Lets start with HBase.  HBase is a way to have a big data solution with query performance at an interactive level.  So many folks are starting to just dump data into HBase.  In the project teddy solution, we are dumping tweets, dialogue and dialogue annotations to power our open domain conversational api.  There really is no other way that is easy to use for us to do this.

The second part of project teddy is to predict based on an incoming conversational component, what sort of response the speaker is attempting to illicit from the teddy bear.  If we power our teddy bear with predictive analytics and big data, this would be perfect.  What better platform to do this quickly and easily than AzureML?

This is a follow up article to this one: http://indiedevspot.com/2015/06/30/writing-tweets-to-hbase-simply/

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Understanding AzureML – Part 2: Binary Classification

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Welcome to Part 2!  We will be discussing Binary Classification.  So I hope many of you have started using AzureML.  If not, you should definitely check it out.  Here is the link to the dev center for it.  This article series will focus on a few key points.

  • Understanding the Evaluation of each Model Type.
  • Understanding the published Web Service of each Model

If you are looking for how to build a simple how to get started, check out this article.

The series will be broken down into a three parts.

Part 1: Regression

Part 2: Binary Classification

 

So lets get started!

AzureML

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Understanding AzureML – Part 1: Regression

Hello World!

So I hope many of you have started using AzureML.  If not, you should definitely check it out.  Here is the link to the dev center for it.  This article will focus on a few key points.

  • Understanding the Evaluation of each Model Type.
  • Understanding the published Web Service of each Model

If you are looking for how to build a simple how to get started, check out this article.

The series will be broken down into a three parts.

Part 1: Regression

Part 2: Binary Classification

So lets get started!

AzureML

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