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!
This is a video tutorial for building beautiful data visualizations in R. You will learn about what Data Viz is, basic charting libraries and finally a full walk through for how I built the Miami Jail Interactive graphic you see in this article.
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
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.
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.
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.
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 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.
This is a question that comes up frequently. How do I get an internship, a job or anything at Microsoft as a student? Well there is a great program, the Microsoft Student Partner program. This is the foot in the door position. Students who do well here are more likely to get a full time job at Microsoft, but also anywhere. I have students who have started their own companies, work at Google, Lockheed Martin as well as Microsoft among several others. It is fairly easy to get into, but once you get in, you better work and treat it like a full time job. You can apply here: www.aka.ms/applyMSP2016
Now beyond this, I have some notes on my experience being a mentor for this program for 2 years. Continue reading →
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.
So I have been on a quest to find a great language/set of tools for data exploration and visualization. But not only that, deliver to modern app platforms. You can see I have been very active with R lately, as I liked those visualizations, but not the F# ones. But then along came this Xplot thing from fslab.org. Whats interesting about this is that I can write F# code to generate interactive charts similiar to how I did in R. The big difference though is that I can deliver those to a production environment. In R, you have shiny, which is “free”, until you want to run real workloads with security etc, then its $10k. That sucks, I just have a simple blog.
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.