Battle of the Programming Languages

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So this article is to help provide some guidance around which programming language to use.  Note that this article is specifically geared towards delivering code in which intelligence and information is the soul of the product.  In this day and age, that should be every product.

I want to preface this article with a few things

  1. This is an excerpt from a paper I wrote for internal use of my own volition.  As this is the case, I was able to remove all confidential information and publish my findings.
  2. I only analyzed F#, C#, R and Python.  I know there are more, but I picked the top dogs, but F# had some special circumstances that I felt it belonged.

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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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Miami’s top 10 Jail Bookings

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So I’ve been working on building some interesting visualizations with open data.  Today I get to show off a really interesting one, not only will we discuss the visualization in depth, but also dive into how I built it.  And here it is, the top 10 bookings in Miami where the legend is in descending order for most common bookings holistically.

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Intro to Data Manipulation with R

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Here is a recorded version of an in-person training I have been doing.  Enjoy.  I end up coming back to this myself even for reference.

This episode is all about performing data manipulation to derive raw insights from your data using the R programming language.  Data manipulation is the core to anything and everything you do in business intelligence and machine learning.  This episode sets the base for all R based intelligence sessions from here on out.

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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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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Introduction to Microsoft R Open

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Ever wonder the difference between R and Microsoft R?  Considering learning R as a programming language?  You should probably watch this video.  It is the first in a 4 part series to give you the jump start you need to becoming a professional data scientist with R.

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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Data Analytics Architectural Blueprint

Here is a video show casing a sample architecture for doing Data Analytics on Azure.  Enjoy 🙂

Exploratory IoT Analysis with R

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These days I need to make videos instead of written articles, so I am going to post a few of those here.

In this video we will do an initial exploratory analysis on a water flow data set that came from a prototype that I built. The prototype consists of a water pump, a valve and a flow meter. The data set exists in SQL Azure. We will use R and R Studio to perform the analysis from an Azure virtual machine.

The code is:

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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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