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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IoT Hands on Lab – Azure Portion

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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.
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IoT Hands on Lab

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This article is for a hands on lab in IoT I am running.  You will find full documentation at the provided link.  Also, you may run into a scenario in which the MCP 3008 code does not work.  If this is the case, you are likely using an older version of the nugget package which contains it or the new version has not been pushed yet.  You can find the code for the MCP 3008 below.

Here is the link: https://onedrive.live.com/redir?resid=BA8DC4B28555902A!3406&authkey=!AGawE2hfolHvC8s&ithint=file%2cpdf

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