# Feature Scaling & Machine Learning

Hello World!

If you are practicing machine learning, you are likely going to run into this at some point.  Basically the reason we use feature scaling is to help our algorithms train faster and better.  Lets begin by taking a standard theta optimization equation to help better understand the problem.
$\theta_j = \theta_j - \alpha \cdot \frac{ \sum_i^m \left(H_{\theta}\left(x\right) - y\right) \cdot x_j } { m }$

# Linear Regression from Scratch using Linear Algebra

Hello World!

So I wrote an article earlier “Linear Regression From Scratch”.  Many folks have pointed out that this is in fact not the optimal approach.  Now being the perfectionist I decided to re-implement.  Not to mention it works great in my own libraries.  The following article discussing converting the original code into code that uses linear algebra.  Beyond this, it still works in PCL for xamarin,  Hoo-Rah Xamarin!

# Machine Learning Study Group Recap – Week 1

Hello World!

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.

# Linear Regression from Scratch

Hello World,

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.

# Intro to R Data Structures

Hello World,

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

# FTL Machine Learning User Group

Slides available here:

https://onedrive.live.com/redir?resid=BA8DC4B28555902A!3328&authkey=!AOYYzGQ8YSi_D-E&ithint=file%2cpptx

Channel9 Recorded version of this is here: https://channel9.msdn.com/Blogs/raw-tech/Predicting-Home-Values-with-Azure-Machine-Learning

# NSU – Intro to Machine Learning

Here is the slides:

https://onedrive.live.com/redir?resid=BA8DC4B28555902A!3267&authkey=!AMTeHOkanPEPjHM&ithint=file%2cpptx

GitHub repo for code:

https://github.com/drcrook1/ZillowAnalysis

# AzureML Talk Slides – Orlando SQL PASS

Here is the slide deck

https://onedrive.live.com/redir?resid=BA8DC4B28555902A!3026&authkey=!ACR2kzciAvbetak&ithint=file%2cpptx

# Powering AzureML with Hadoop HBase

Hello World!

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/