Applying Machine Learning to Create a Web-based Realtime Forecasting System

RMDS is an ecosystem service provider that empowers data science professionals and businesses worldwide to achieve successful, data-driven results. Our community members have access to tools and resources that enhance their research and increase the success-rate of their work. Our proprietary web platform uses AI-powered recommendations to connect members to online learning, datasets and workflows, and peers within a collaborative environment. Our community extends offline with hands-on training, conferences, and community events. Experience the RMDS ecosystem.

RMDS Lab

Learn from JPL data scientist on how to process satellite data and how to  organize  data for operational hurricane forecasting.

<p><strong>Note: This is a  4 week  online learning experience that you can purchase the first day of the course or the full 4 week course. Below you will find the course schedule and topic. You will receive the Zoom access link by email prior to the event.</strong></p><h3></h3><h3><strong>Overview</strong></h3><p>The workshop will cover a complete  workflow of data collection, feature extraction, machine learning model building, validation, prediction, and evaluation of hurricane forecast models. It will provide hands-on training in processing satellite data and organizing data for operational hurricane forecasting. The instructor will introduce methods for feature extraction and data dimensionality reduction, and multiple machine learning techniques.</p><p></p><h3><strong></strong></h3><h3><strong>Learning Outcomes</strong></h3><ul><li>A web-based real-time forecasting system </li><li>Knowledge of operational hurricane intensity forecast modeling</li><li>Exposure to the workflow of building predictive models using machine learning </li><li>Hands-on experience with spatiotemporal varying satellite data </li><li>Real-world impacts on improving hurricane forecasting and benefiting society </li></ul><h3></h3><h3><strong>Course Outline </strong></h3><p>Week 1: Data Collection and Feature Extraction </p><ul><li>Understanding data </li><li>Preprocessing techniques for spatiotemporal varying satellite data </li><li>Generating features from data</li><p></p></ul><p>Week 2:  Machine Learning Algorithms and Model Building </p><ul><li>Model building </li><li>Model validation </li><li>Model evaluation </li></ul><p></p><p>Week 3: Implementation and Solution Creation </p><ul><li>Script automation </li><li>A web-based real-time forecasting system </li></ul><p></p><p>Week 4: Innovative Techniques and Methods </p><ul><li>New findings and new methods from the latest research</li><li>Generating creative features to improve models </li></ul><h3></h3><h3><strong></strong></h3><h3><strong>Participants Description </strong></h3><p> Beginner and intermediate software developer, research fellow, student in data science and machine learning  </p><h3></h3><h3><strong>Highlights </strong></h3><ul><li>Understand the workflow of building predictive models using machine learning </li><li>Hands-on experience with spatiotemporal varying satellite data </li><li>Create a predictive model that has tremendous socioeconomic impacts</li></ul><h3></h3><h3><strong></strong></h3><h3><strong>Course  Structure</strong></h3><p><strong>TIme</strong>: 5pm – 7 PM PDT  every Wednesday  on  9/16, 9/23, 9/30, 10/7  </p><p><strong>Prerequisites</strong>: Basic Programming skills in Python or other languages</p><p><strong>Format</strong>: Online through Zoom</p><p><strong>Number of Sessions</strong>: 4</p><p><strong>Required Tools</strong>: Python</p><h3></h3><h3></h3><h3><strong>Instructors</strong></h3><h3></h3><h3>Dr. Hui Su </h3>

<p>Dr. Hui Su is a Principal Scientist in the Earth Science Department of Jet Propulsion Laboratory (JPL). Her research focuses on extreme weather and climate dynamics, including tropical cyclones, El Niño and cloud and water vapor feedback. She received the JPL Lew Allen Award for Excellence in 2008 and NASA Exceptional Scientific Achievement Medal in 2010 for “major advances in the understanding of water vapor and cloud feedback on climate change through quantitative analysis of observations from multiple NASA satellites.”  She is an Adjunct Professor of Dept. of Atmospheric and Oceanic Sciences and Assistant Director of the Joint Institute for Regional Earth System Science and Engineering (JIFRESSE) at UCLA. She is an Editor of Geophysical Research Letter and Vice Chair of American Meteorological Society (AMS) Committee on Tropical Meteorology and Tropical Cyclones.</p><p></p><h3>Dr.Longtao Wu </h3>

<p>Dr. Longtao Wu is a data scientist at Jet Propulsion Laboratory. He received B.S. and M.S degrees in Atmospheric Science from Peking University, and his Ph.D. in Atmospheric Science from the University of Wisconsin-Madison. He has extensive experience on numerical modeling and analysis of the Earth System science data, including tropical and extratropical cyclones, aerosol, cloud and water vapor. He received NASA Early Career Public Achievement Medal in 2017 for “excellence in developing customized regional weather and climate model simulations benefiting multiple earth science applications and improving communications networks”.</p><p></p><p></p><h3>About RMDS Lab</h3><p>RMDS Lab is an ecosystem service provider that empowers data science professionals and businesses worldwide to achieve successful, data-driven results. Our community members have access to tools and resources that enhance their research and increase the success-rate of their work. Our proprietary web platform uses AI-powered recommendations to connect members to online learning, datasets and workflows, and peers within a collaborative environment. Our community extends offline with hands-on training, conferences, and community events.</p><p>RMDS Lab was founded in 2009 by IBM Chief Data Scientist Alex Liu and is headquartered in Pasadena, CA with partners worldwide. It serves more than 35,000 members and affiliates globally. In December 2019, RMDS successfully held IM Data annual conference in the Pasadena Convention Center with 2000+ attendees from all over the world. For more information, visit www.grmds.org.</p>

Applying Machine Learning to Create a Web-based Realtime Forecasting System

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