.NET Software Quality Software Management Software Architecture Angular Project Management JavaScript TypeScript REST Azure Software Development C# F# Vue.js SQL Data Science Machine Learning Python Teaching Teaching Code Jupyter Notebook
Columbus, Ohio, United States
After several decades as a software engineer and engineering manager, Matt now serves as a software engineering instructor at Tech Elevator where he gets to raise up future developers and unleash them upon the world to build awesome things.
Matt is an Azure Data Scientist Associate, a current organizer for the Central Ohio .NET Developer Group, runs a data science blog and YouTube channel, and is currently pursuing a master's degree in data analytics.
In his copious amounts of spare time, Matt continues to build nerdy things and looks for ways to share them with the community.
In this session we'll explore Azure Machine Learning Studio and how it can help novices, journeymen, and experts on their machine learning journey.
Specifically we'll explore AutomatedML and machine learning without deep data science knowledge, the machine learning designer for intermediate users, and the Python SDK for those who prefer to code in Python but take advantage of Azure's cloud offerings.
We'll explore data and compute management as well as integration with other aspects of Azure such as databricks and synapse analytics and close with a discussion of Azure Cognitive Services for those interested in consuming pre-built machine learning models.
By the time we're done, you'll see how Azure is a frighteningly powerful platform for all flavors of data scientists - including those without a deep data science background.
Ever had life happen? One minute you're cruising along and the next disaster strikes: layoffs, medical calamities, death or serious illness in the family, global pandemics... the list goes on. Sometimes Life Happens and it just isn't fair.
But how do we move through it and get back to being happy and functional adults?
In this session Matt offers anecdotes and lessons learned from his own life in overcoming paralysis as well as lessons learned from helping students overcome life to complete a challenging technical software development bootcamp and transform their careers.
We'll discuss how to cope with difficulty or help others, where to find help, and how to eventually "hit play" on your life and return to normal.
Machine learning works best with big data. And what science fiction show is bigger than Doctor Who?
In this beginner-friendly talk we'll use C# and ML.NET's AutoML features to build predictive machine learning models around a dataset of Doctor Who episodes to determine what factors make a Doctor Who episode truly fantastic. We'll then use our predictive models to come up with some tongue-in-cheek recommendations to help Doctor Who get back to what made it great in the first place.
Whether you love, hate, or are ignorant of Doctor Who, you'll leave this talk knowing more about machine learning and how to get started with ML.NET without needing to be The Doctor (or hold a doctorate).
Did you know you can train and use machine learning inside of your .NET applications without needing detailed knowledge of machine learning algorithms? In this talk we'll explore the ML.NET AutoML API capabilities and how accessible machine learning in C# really is as we write C# code to solve machine learning problems.
We'll focus heavily on the automatic algorithm selection features of AutoML in ML.NET and talk about the various tasks it can achieve before drilling deeper to apply AutoML to solve a multi-class classification problem. We'll train a machine learning model and have it predict video game ESRB ratings for a few hypothetical games provided by the audience, then host this new model in a .NET 6 minimal Web API project in ASP .NET Core.
We'll also explore evaluating model performance, ideal training times, and how trained models can be saved and loaded for use in production applications, as well as some places you can go to learn more about ML.NET and machine learning in general.
NOTE: This is different than the AutoML CLI or Model Builder. Our core focus is on C# code for Machine Learning using the AutoML as baby steps into Machine Learning
This talk is a mixture of slides and code.
This talk can be performed in person without an internet connection
Curious about data science and its relation to software engineering? Want to know how to dabble in artificial intelligence or machine learning side projects before taking the plunge? Come check out this session.
In this session I'll highlight my own journey in layering data science skills on top of a software engineering background. I'll teach you the terms, roles, languages, libraries, and technologies you'll encounter and help you understand what aspects of math and programming are helpful in setting down this journey.
You'll discover easy ways to get started with Python, R, and get connected to the data science community. I'll show you how to discover public datasets and visualizations to help inspire your own journey. By the time the session is finished, you'll know how to find out if data science is a good fit for you and how to take it to the next level if you discover you like it.
When it comes to popular Christmas movies there's a recurring debate as to whether or not that list of movies should include the 1988 film Die Hard. Thankfully, machine learning has been applied to this problem and we have an answer.
We'll start off by using Python, Pandas, and Jupyter Notebooks to analyze and clean movie data and then prepare it for model training while avoiding factors that might introduce bias.
After that we'll explore using the beginner-friendly features of Azure Machine Learning Studio's Automated ML to train and evaluate a classification model.
Finally, we'll deploy the trained model to Azure as a web service and see what it has to say about Die Hard.
By the time we're done you'll know the truth about Die Hard and have a deeper understanding of machine learning experiments and some common beginner-friendly tools involved in machine learning.
Like many terrier owners, I have a problem. My dog is overworked from the constant need to monitor multiple streets to bark at squirrels or passers by. I'd like to free up more of his time and energy for snuggling and play but the outdoors must still be monitored. Thankfully, it turns out that much of what my dog does, Azure Cognitive Services can help with.
In this talk we'll use this absurd premise to explore progressively enhancing applications through the Azure Cognitive Services speech, vision, and text APIs. We'll look at object detection, facial APIs, text to speech, speech to text, and language understanding.
By the end of this session you'll have more of an understanding of what Azure Cognitive Services can do and the basics of how to interact with them from code so that you, like my dog, can take advantage of pre-trained machine learning models to enhance devote more of your energy to other areas.
This will require an internet connection
Azure Machine Learning Studio has something for all experience levels of data scientist. In this talk we'll explore what Azure Machine Learning Studio is and how it can help novice, intermediate, and advanced data scientists empower their data science experiments.
We'll start by exploring Automated ML and how it helps data scientists focus on the task they're trying to accomplish while Azure discovers the optimal algorithms and hyperparameters for their experiments - without requiring any code on their part.
Next, we'll explore the Machine Learning Studio designer and how it supports more advanced no-code or low-code approaches to build repeatable machine learning pipelines.
After that we'll discuss the Azure ML Python SDK and how it allows advanced users to customize their experiments, use their own compute resources, and fine-tune and automate the tasks they're trying to accomplish.
By the end of this talk you'll see how Azure Machine Learning Studio reduces barriers to entry and propels experiments further by helping novice, intermediate, and advanced data scientists train, evaluate, manage, and deploy their machine learning models and related datasets.
TechBash 2022 |
8 Nov 2022 - 11 Nov 2022
Mount Pocono, Pennsylvania, United States
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Momentum 2022 |
20 Oct 2022
Cincinnati, Ohio, United States
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Indy.Code()
Automating Machine Learning with Python and Azure
Is Die Hard a Christmas Movie? Let's ask Azure! |
19 Oct 2022
Indianapolis, Indiana, United States
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PyOhio 2022
Introducing Automated Machine Learning with Python and Azure
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30 Jul 2022
Columbus, Ohio, United States
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Franklin University 2nd Annual Doctoral Student Association Conference
Empowering Machine Learning with Azure Machine Learning Studio
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25 Jun 2022
Columbus, Ohio, United States
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MemPy
Automating Machine Learning with Python and Azure
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20 Jun 2022
Memphis, Tennessee, United States
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SciFiDevConMayTheFourthEvent |
1 May 2022 - 31 May 2022
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Stir Trek 2022 |
6 May 2022
Columbus, Ohio, United States
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Central Ohio .NET Developer Group
Using ML.NET to Predict Video Game ESRB Ratings with C#
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24 Mar 2022
Columbus, Ohio, United States
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Cincinnati Machine Learning Meetup
Using ML.NET to Predict Video Game ESRB Ratings with C#
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17 Mar 2022
Cincinnati, Ohio, United States
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Technology and Friends Podcast
Spoke on how humans learn and how that relates to programming and some aspects of machine learning
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25 Feb 2022
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Global App Dev User Group
Stand Back; I'm going to try Data Science!
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21 Feb 2022
Columbus, Ohio, United States
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CodeMash 2022 |
11 Jan 2022 - 14 Jan 2022
Sandusky, Ohio, United States
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Festive Tech Calendar 2021 |
1 Dec 2021 - 31 Dec 2021
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Central Ohio Azure
Is Die Hard a Christmas Movie? Let's ask Azure!
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13 Dec 2021
Columbus, Ohio, United States
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Cincinnati Software Craftsmanship
Is Die Hard a Christmas Movie? Let's ask Azure!
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1 Dec 2021
Cincinnati, Ohio, United States
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Columbus App Dev User Group
Introduction to Application Architecture and Scalability
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25 Oct 2021
Columbus, Ohio, United States
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Momentum 2021 |
15 Oct 2021
Cincinnati, Ohio, United States
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Stir Trek 2021 Virtual Edition |
7 May 2021
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GLUGNet
Expanding your .NET Testing Toolbox
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15 Apr 2021
Lansing, Michigan, United States
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Cincinnati Software Craftsmanship
Intro to Application Architecture and Scalability
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7 Apr 2021
Cincinnati, Ohio, United States
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LOPSA
Intro to Application Architecture and Scalability
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25 Mar 2021
Columbus, Ohio, United States
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GLUGNet
Intro to Application Architecture and Scalability
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18 Mar 2021
Lansing, Michigan, United States
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JavaScript and Friends
Intro to Application Architecture and Scalability
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9 Mar 2021
Columbus, Ohio, United States
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CinJuG
Intro to Application Architecture and Scalability
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17 Feb 2021
Cincinnati, Ohio, United States
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Tea & Tech with Michael
Casual talk about software development, bootcamps, getting into coding, side projects, etc.
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11 Feb 2021
Cincinnati, Ohio, United States
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Women Who Code Philly
Intro to Application Architecture and Scalability
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9 Feb 2021
Philadelphia, Pennsylvania, United States
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Momentum 2020 |
16 Oct 2020
Cincinnati, Ohio, United States
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Momentum Conf Interview
Discussing Functional Programming in C#
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15 Oct 2020
Cincinnati, Ohio, United States
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SciFiDevCon |
29 Jul 2020 - 31 Jul 2020
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Stir Trek 2020 |
1 May 2020
Columbus, Ohio, United States
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DotNet Open Source Days
Stand Back; I'm Going to Try Scientist!
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17 Apr 2020
Columbus, Ohio, United States
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CodeMash 2020 |
6 Jan 2020 - 10 Jan 2020
Sandusky, Ohio, United States
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Central Ohio .NET Developer Group
Expanding Your .NET Testing Toolbox
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22 Aug 2019
Columbus, Ohio, United States
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Columbus App Dev User Group
Accelerating Angular Application Development
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8 Jul 2018
Columbus, Ohio, United States
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Central Ohio Windows Phone User Group (COWPUG)
Prototyping and Building Windows Phone Applications
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21 Nov 2011
Columbus, Ohio, United States
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