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.
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.
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.
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.
Due to my time in the games industry, I still occasionally receive questions around it. Today I received an email from a college student with some questions I feel are more broadly applicable and am therefor writing an article which can be shared to that broader audience.
For this portion of the HoL, we will be bypassing the Raspberry Pi portion all together and go straight to provisioning Resources and using a local app to simulate the telemetry data. You can alternatively still use a Raspberry Pi as this session was intended, but for time purposes, that can be skipped and the app below can be used for simulating data for a live dashboard. Continue reading →