�ݺ�ߣshows by User: frankdevans

�ݺ�ߣshows by User: frankdevans / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: frankdevans / Thu, 03 Nov 2016 17:33:24 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: frankdevans Intro to Machine Learning: Thunderplains 2016 /slideshow/intro-to-machine-learning-thunderplains-2016/68136476 intromachinelearningthunderplains-161103173324
This is an introduction to Machine Learning and how it fits into Data Science as well as the larger tech space in general.]]>
This is an introduction to Machine Learning and how it fits into Data Science as well as the larger tech space in general.]]> Thu, 03 Nov 2016 17:33:24 GMT /slideshow/intro-to-machine-learning-thunderplains-2016/68136476 frankdevans@slideshare.net(frankdevans) Intro to Machine Learning: Thunderplains 2016 frankdevans This is an introduction to Machine Learning and how it fits into Data Science as well as the larger tech space in general. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/intromachinelearningthunderplains-161103173324-thumbnail.jpg?width=120&height=120&fit=bounds" /> This is an introduction to Machine Learning and how it fits into Data Science as well as the larger tech space in general.

Intro to Machine Learning: Thunderplains 2016 from Frank Evans

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0 How to get started in Data Science /frankdevans/how-to-get-started-in-data-science introdatascience-160809214713
This is a talk I have at Tulsa Tech Fest 2016.]]>
This is a talk I have at Tulsa Tech Fest 2016.]]> Tue, 09 Aug 2016 21:47:13 GMT /frankdevans/how-to-get-started-in-data-science frankdevans@slideshare.net(frankdevans) How to get started in Data Science frankdevans This is a talk I have at Tulsa Tech Fest 2016. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/introdatascience-160809214713-thumbnail.jpg?width=120&height=120&fit=bounds" /> This is a talk I have at Tulsa Tech Fest 2016.

How to get started in Data Science from Frank Evans

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0 Topic Modeling with Spark /slideshow/topic-modeling-with-spark/62480984 odscboston2016-160527213734
The world communicates in text. Our work lives have us treading waist-deep in email, our hobbies often have blogs, our complaints go to Yelp, and even our personal lives are lived out via tweets, Facebook updates, and texts. There is a massive amount of information that can be heard from text — as long as you know how to listen. Learning from large amounts of text data is less a question of will and more a question of feasibility. How do you “read” the equivalent of thousands or even millions of novels in an afternoon?]]>
The world communicates in text. Our work lives have us treading waist-deep in email, our hobbies often have blogs, our complaints go to Yelp, and even our personal lives are lived out via tweets, Facebook updates, and texts. There is a massive amount of information that can be heard from text — as long as you know how to listen. Learning from large amounts of text data is less a question of will and more a question of feasibility. How do you “read” the equivalent of thousands or even millions of novels in an afternoon?]]> Fri, 27 May 2016 21:37:34 GMT /slideshow/topic-modeling-with-spark/62480984 frankdevans@slideshare.net(frankdevans) Topic Modeling with Spark frankdevans The world communicates in text. Our work lives have us treading waist-deep in email, our hobbies often have blogs, our complaints go to Yelp, and even our personal lives are lived out via tweets, Facebook updates, and texts. There is a massive amount of information that can be heard from text — as long as you know how to listen. Learning from large amounts of text data is less a question of will and more a question of feasibility. How do you “read” the equivalent of thousands or even millions of novels in an afternoon? <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/odscboston2016-160527213734-thumbnail.jpg?width=120&height=120&fit=bounds" /> The world communicates in text. Our work lives have us treading waist-deep in email, our hobbies often have blogs, our complaints go to Yelp, and even our personal lives are lived out via tweets, Facebook updates, and texts. There is a massive amount of information that can be heard from text — as long as you know how to listen. Learning from large amounts of text data is less a question of will and more a question of feasibility. How do you “read” the equivalent of thousands or even millions of novels in an afternoon?

Topic Modeling with Spark from Frank Evans

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0 Text modeling with R, Python, and Spark /slideshow/text-modeling-with-r-python-and-spark/58065078 textmodeling-160209180248
This presentation was given to ODSC Boston in Feb. 2016. It outlines how to perform text clustering in R, as well Latent Dirichlet Allocation Topic Modeling on both small text data in Python and large text data using Apache Spark. Code from slides: https://github.com/frankdevans/odsc_meetup_text_processing]]>
This presentation was given to ODSC Boston in Feb. 2016. It outlines how to perform text clustering in R, as well Latent Dirichlet Allocation Topic Modeling on both small text data in Python and large text data using Apache Spark. Code from slides: https://github.com/frankdevans/odsc_meetup_text_processing]]> Tue, 09 Feb 2016 18:02:48 GMT /slideshow/text-modeling-with-r-python-and-spark/58065078 frankdevans@slideshare.net(frankdevans) Text modeling with R, Python, and Spark frankdevans This presentation was given to ODSC Boston in Feb. 2016. It outlines how to perform text clustering in R, as well Latent Dirichlet Allocation Topic Modeling on both small text data in Python and large text data using Apache Spark. Code from slides: https://github.com/frankdevans/odsc_meetup_text_processing <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/textmodeling-160209180248-thumbnail.jpg?width=120&height=120&fit=bounds" /> This presentation was given to ODSC Boston in Feb. 2016. It outlines how to perform text clustering in R, as well Latent Dirichlet Allocation Topic Modeling on both small text data in Python and large text data using Apache Spark. Code from slides: https://github.com/frankdevans/odsc_meetup_text_processing

Text modeling with R, Python, and Spark from Frank Evans

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0 PitchFX Hackathon - Team R /slideshow/pitchfx-hackathon-team-r/51347298 pitchfxhackathon-150806134249-lva1-app6892
Presentation for the PitchFX data hackathon on using pitching data from the MLB, building a machine learning model around it, and building the foundation for a better pitcher scouting report.]]>
Presentation for the PitchFX data hackathon on using pitching data from the MLB, building a machine learning model around it, and building the foundation for a better pitcher scouting report.]]> Thu, 06 Aug 2015 13:42:49 GMT /slideshow/pitchfx-hackathon-team-r/51347298 frankdevans@slideshare.net(frankdevans) PitchFX Hackathon - Team R frankdevans Presentation for the PitchFX data hackathon on using pitching data from the MLB, building a machine learning model around it, and building the foundation for a better pitcher scouting report. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pitchfxhackathon-150806134249-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> Presentation for the PitchFX data hackathon on using pitching data from the MLB, building a machine learning model around it, and building the foundation for a better pitcher scouting report.

PitchFX Hackathon - Team R from Frank Evans

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0 Intro to Sentiment Analysis /slideshow/intro-to-sentiment-analysis-47868546/47868546 sentimentanalysis-150507145109-lva1-app6891
This is a very basic intro to Sentiment Analysis, that is primarily visual cues and does not contain a large amount of the content in text on the slides themselves.]]>
This is a very basic intro to Sentiment Analysis, that is primarily visual cues and does not contain a large amount of the content in text on the slides themselves.]]> Thu, 07 May 2015 14:51:09 GMT /slideshow/intro-to-sentiment-analysis-47868546/47868546 frankdevans@slideshare.net(frankdevans) Intro to Sentiment Analysis frankdevans This is a very basic intro to Sentiment Analysis, that is primarily visual cues and does not contain a large amount of the content in text on the slides themselves. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sentimentanalysis-150507145109-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> This is a very basic intro to Sentiment Analysis, that is primarily visual cues and does not contain a large amount of the content in text on the slides themselves.

Intro to Sentiment Analysis from Frank Evans

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0 Basics of Machine Learning /frankdevans/intro-to-machine-learning introtomachinelearning-150203210004-conversion-gate01
This is a very basic 10-15 min Basics of Machine Learning deck that I used to give a talk to the Oklahoma City Data Science/Big Data user group in Feb 2015.]]>
This is a very basic 10-15 min Basics of Machine Learning deck that I used to give a talk to the Oklahoma City Data Science/Big Data user group in Feb 2015.]]> Tue, 03 Feb 2015 21:00:04 GMT /frankdevans/intro-to-machine-learning frankdevans@slideshare.net(frankdevans) Basics of Machine Learning frankdevans This is a very basic 10-15 min Basics of Machine Learning deck that I used to give a talk to the Oklahoma City Data Science/Big Data user group in Feb 2015. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/introtomachinelearning-150203210004-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> This is a very basic 10-15 min Basics of Machine Learning deck that I used to give a talk to the Oklahoma City Data Science/Big Data user group in Feb 2015.

Basics of Machine Learning from Frank Evans

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0 https://cdn.slidesharecdn.com/profile-photo-frankdevans-48x48.jpg?cb=1523475730 Frank is a data scientist for a data analysis applications company. He primarily works with machine learning and feature engineering using big data systems, specializing in unstructured and semi-structured data. He has a BS in Quantitative Social Science from St. Gregory’s University and a Master’s Specialization in Data Science from Johns Hopkins University. He is a co-founder and organizer of the Oklahoma City Big Data User Group. www.exaptive.com https://cdn.slidesharecdn.com/ss_thumbnails/intromachinelearningthunderplains-161103173324-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/intro-to-machine-learning-thunderplains-2016/68136476 Intro to Machine Learn... https://cdn.slidesharecdn.com/ss_thumbnails/introdatascience-160809214713-thumbnail.jpg?width=320&height=320&fit=bounds frankdevans/how-to-get-started-in-data-science How to get started in ... https://cdn.slidesharecdn.com/ss_thumbnails/odscboston2016-160527213734-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/topic-modeling-with-spark/62480984 Topic Modeling with Spark