Machine Learning

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Keywords: Python, JDBC, SQL, IRIS, Jupyter Notebook, Pandas, Numpy, and Machine Learning 

1. Purpose

This is another 5-minute simple note on invoking the IRIS JDBC driver via Python 3 within i.e. a Jupyter Notebook, to read from and write data  into an IRIS database instance via SQL syntax, for demo purpose. 

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i want to restrict certain IPs (ip for now and looking for more such parameters) from running my URL for security purposes...

i wanted to know how to access their ips and compare them with the list of restricted IPs, also if not IPs are there any other unique browser parameters which can be used for access control? and enhance security

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Keywords:  IRIS, IntegratedML, Machine Learning, Covid-19, Kaggle 

Purpose

Recently I noticed a Kaggle dataset  for the prediction of whether a Covid-19 patient will be admitted to ICU.  It is a spreadsheet of 1925 encounter records of 231 columns of vital signs and observations, with the last column of "ICU" being 1 for Yes or 0 for No. The task is to predict whether a patient will be admitted to ICU based on known data.

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Whats NLP Stands For?

NLP stands for Natural Language Processing which is a field of Artificial Intelligence with a lot of complexity and
techniques to in short words "understand what are you talking about".

And FHIR is...???

FHIR stands for Fast Healthcare Interoperability Resources and is a standard to data structures for healthcare. There are
some good articles here explainig better how FHIR interact with Intersystems IRIS.

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Keywords:  IRIS, IntegratedML, Machine Learning, Covid-19, Kaggle 

Continued from the previous Part I ... In part I, we walked through traditional ML approaches on this Covid-19 dataset on Kaggle. 

In this Part II, let's run the same data & task, in its simplest possible form, through IRIS integratedML which  is a nice & sleek SQL interface for backend AutoML options. It uses the same environment. 

 

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This is the third post of a series explaining how to create an end-to-end Machine Learning system.

Training a Machine Learning Model

When you work with machine learning is common to hear this work: training. Do you what training mean in a ML Pipeline?
Training could mean all the development process of a machine learning model OR the specific point in all development process
that uses training data and results in a machine learning model.

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Hi Community!

We're pleased to invite you to the Online Meetup with the Winners of the InterSystems IRIS AI Programming Contest!

Date & Time: Friday, July 24, 2020 – 11:00 EDT

What awaits you at this virtual Meetup? 

  • Our winners' bios.
  • Short demos on their applications.
  • A short interview with all the winners about the past contest. Plans for the next contests.

 

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Hey Developers!

This week is a voting week for the InterSystems IRIS AI Programming Contest!

So, it's time to give your vote to the best AI- and ML-enabled solution on InterSystems IRIS!

🔥 You decide: VOTING IS HERE 🔥

 

How to vote? This is easy: you will have one vote, and your vote goes either in Experts Nomination or in Community Nomination.

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Currently, the process of using machine learning is difficult and requires excessive consumption of data scientist services. AutoML technology was created to assist organizations in reducing this complexity and the dependence on specialized ML personnel.

AutoML allows the user to point to a data set, select the subject of interest (feature) and set the variables that affect the subject (labels). From there, the user informs the model name and then creates his predictive or data classification model based on machine learning.

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This is the second post of a series explaining how to create an end-to-end Machine Learning system.

Exploring Data

The Intersystem IRIS already has what we need to explore the data: an SQL Engine! For people who used to explore data in
csv or text files this could help to accelerate this step. Basically we explore all the data to understand the intersection
(joins) which should help to create a dataset prepared to be used by a machine learning algorithm.

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A few months ago, I read this interesting article from MIT Technology Review, explaing how COVID-19 pandemic are issuing challenges to IT teams worldwide regarding their machine learning (ML) systems.

Such article inspire me to think about how to deal with performance issues after a ML model was deployed.

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