Introduction to Machine Learning with Andreas Mueller, core developer of scikit-learn
scikit-learn provides easy-to-use interfaces in Python to perform advanced analysis and build powerful predictive models.
Bio
Curriculum
Prerequisites

Introduction to Deep Learning with Tensorflow Contributor and Kaggle Winner Dan Becker
This workshop will introduce participants to the basic concepts of deep learning and its most promising applications. Participants will get a taste of this exciting field at the intersection of AI and machine learning. The workshop will utilize popular open source deep learning tools combined with practical exercises and programming assignments.
Bio
Curriculum
Prerequisites

Developing and Deploying Intelligent Chat Bots with Micheleen Harris
Bio
Curriculum
- Cognitive services overview
- What are Cognitive APIs
- Demos
- Introduction for Bot Framework Part
- Syllabus
- Learning objectives
- Bot Framework Overview
- What a bot is and is not
- The major components of the Bot Framework
- Deploying and working with channels
- Your arsenal or toolbox
- Developer’s Introduction and Building an intelligent bot with Bot Builder Node.js SDK
- Toolbox – Go over prereqs
- Setup project in VSCode (and set up debugger)
- Get code from course website with Git
- Update with Vision API key from Cognitive Services “My Account”
- Test with emulator
- Create more bots! Follow along or create your own
- Summary
Prerequisites
Please bring a laptop with internet connectivity.
- Node.js with npm installed locally – get the latest at:
- Visual Studio Code [recommended] or equivalent code editing and debugging environment with IntelliSense.
- Bot Framework Emulator (Windows and Unix-compatible) installed locally – information and links at
- GitHub Account – a code repository and collaboration tool we’ll use
- Git Bash – included in git download
- [Recommended]Azure account – use the one you have, sign up for a free trial at https://azure.microsoft.com/en-us/free/, or, if you have an MSDN account and Azure as a benefit, link your Microsoft Account or Work/School Account to MSDN and activate the Azure benefit by following this guide
We will assume you have already have the following background:
- Basic knowledge around using and navigating in a unix-style command line or terminal (for using Git Bash) (good basic guide at http://linuxcommand.org/lc3_learning_the_shell.php)
- Familiarity with Git and GitHub as a tools for software development, versioning and collaboration. (great book on Git at https://git-scm.com/book/en/v2)
- Have learned about debugging bots with VSCode in https://docs.botframework.com/en-us/node/builder/guides/debug-locally-with-vscode/ docs.
- If you are new to Node, here’s a good video tutorial series at https://www.youtube.com/playlist?list=PL6gx4Cwl9DGBMdkKFn3HasZnnAqVjzHn_

The full cycle of model development in R and Python and utilizing Shiny and Bokeh with Ali Marami
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Prerequisites

Modeling in R with Jared Lander
Bio
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Prerequisites

Getting Started with Deep Learning with Charlie Killam
Bio
Curriculum
- Learn how to leverage deep neural networks (DNN) within the deep learning workflow
- Solve a real-world image classification problem using NVIDIA DIGITS
- Walk through the process of data preparation, model definition, model training and troubleshooting
- Use validation data to test and try different strategies for improving model performance using GPUs
- Use NVIDIA DIGITS to train a DNN on your own image classification application
Prerequisites

Approaches to Object Detection with Charlie Killam
Bio
Curriculum
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Learn three approaches to identify a specific feature within an image
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Compare each in relation to: model training time, model accuracy and speed of detection during deployment
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Understand the merits of each approach
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Learn how to detect objects using neural networks trained neural networks
Prerequisites

Deep Learning for Image Segmentation with Charlie Killam
Bio
Curriculum
- Learn how to train and evaluate an image segmentation network
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Neural Network Deployment with Charlie Killam
Bio
Curriculum
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Learn three approaches for deployment – directly use inference functionality within a deep learning framework, integrate inference within a custom application , use the NVIDIA TensorRT™,
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Learn about the role of batch size in inference performance
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Learn about various optimizations that can be made in the inference process
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Explore inference for a variety of different DNN architectures
Prerequisites

Deploying Python Models As an API with Jed Dougherty
Bio
Curriculum
Prerequisites
- Basic knowledge of Python and Scikit-Learn
- We will be using Flask, Celery, and Docker for performing Predictions with an API. Basic knowledge of these tools is helpful but not required.
- The following python packages installed:
- numpy
- scipy
- scikit-learn
- joblib
- Flask
- gunicorn
- celery[redis]
- gevent

Create a Codeless Data Pipeline using Dataiku with Jed Dougherty
Bio
Curriculum
Prerequisites
- Attendees should have the free version of DSS installed on their local machines.
- We will be discussing feature selection, model optimization, and model choice so some background in machine learning is recommended.
- We will be using models from the Scikit-learn package and the XGBoost package.

Data Science with Spark: Beyond the Basics with Adam Breindel
Bio
Curriculum
Prerequisites

Advanced scikit-learn with Andreas Mueller
scikit-learn provides easy-to-use interfaces in Python to perform advanced analysis and build powerful predictive models.
Bio
Curriculum
Prerequisites

Machine Learning in R with Jared Lander
Bio
Curriculum
Prerequisites

Open Geospatial Machine Learning with Kevin Stofan
Bio
Curriculum
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Natural Language Processing and Text Mining in Python with Michael Galvin
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Machine Learning with H2O Open Platform (Morning Session) with Jo-fai (Joe) Chow
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Deploying and Scaling Spark ML and Tensorflow AI Models with Chris Fregly, Research Scientist, Contributor, Author and Trainer
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Deep Learning with H2O Open Platform with Jo-fai (Joe) Chow
Bio
Curriculum
- Gridlines
- Pipe Search
- Deep Water
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Intro to Text Analytics with Ted Kwartler
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Apache Drill with Charles Givre
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Programming with Data: Python and Pandas with Accomplished Data Scientist Daniel Gerlanc
Bio
- Daniel Gerlanc is a highly respected former hedge fund quant and much sought after data scientist. He has a well earned reputation of helping companies improve their modeling techniques and unblocking critical issues. His workshop is a shortened version he has delivered internally to top hedge funds and fortune 100 companies.
- Daniel Gerlanc has worked as a data scientist for over 10 years. He spent 5 years as a quantitative analyst with two Boston hedge funds before starting Enplus Advisors Inc, a predictive analytics consultancy, in 2011. At Enplus, he works with clients in different industries to improve existing analytic processes and develop new ones. He has coauthored several open source R packages, published in peer-reviewed journals, and is active in local predictive analytics groups. He is a graduate of Williams College.
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