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Products are filtered by different dates, depending on the combination of live and on-demand components that they contain, and on whether any live components are over or not.
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  • Module 10 - Now What?

    Contains 1 Component(s) Includes a Live Web Event on 12/17/2020 at 7:00 AM (PST)

    Developing and learning the ML skills to enable you to be part of an enterprise's AI transformation is a journey. This course's objective was to focus on introducing key concepts, helping you understand the use cases and key technologies that enable them. There are many next steps that depend on your background, knowledge, and objectives. In this module, we will discuss various paths and resources to be able to take your next step in your AI journey.

    Developing and learning the ML skills to enable you to be part of an enterprise's AI transformation is a journey. This course's objective was to focus on introducing key concepts, helping you understand the use cases and key technologies that enable them.  There are many next steps that depend on your background, knowledge, and objectives. In this module, we will discuss various paths and resources to be able to take your next step in your AI journey. 

    We will also discuss the steps to earn your H2O.ai ML Foundations badge.

  • Module 9 - Machine Learning Foundations Capstone

    Contains 2 Component(s) Includes Multiple Live Events. The next is on 12/15/2020 at 7:00 AM (PST)

    It’s time to put everything you’ve learned together to build an end-to-end machine learning solution.

    It’s time to put everything you’ve learned together to build an end-to-end machine learning solution. 

    Learning Outcomes
    • Successfully build an end to end machine learning solution
  • Machine Learning Foundations Course

    Contains 12 Product(s)

    Machine learning (ML) is one of the most active areas of artificial intelligence. Computers can learn new things without being programmed through the use of machine learning algorithms. The large amounts of data available can be understood through statistical methods to create new insights. These ML algorithms can help machines classify things we hear, images we see, videos consumed. Machine learning algorithms can also help discover new health remedies, can generate art or write songs, and can answer questions we ask. Over the course of the series, we will review the types of machine learning: supervised, unsupervised, reinforcement as well as how to boost predictions through ensembling or through the use of AutoML tools. We will also be hands-on with popular ML methods for different problem types and data on a massive scale.

    This course is designed to be a hands-on complement to the AI Fundamentals course offered by H2O.ai.  While a data science background is not required, successful learners should have some familiarity with Python and R.

    Prerequisites

    1. This course assumes you have some foundational AI knowledge
    2. This course assumes some basic familiarity with statistics.

    Series Description

    Machine learning (ML) is one of the most active areas of artificial intelligence. Computers can learn new things without being programmed through the use of machine learning algorithms. The large amounts of data available can be understood through statistical methods to create new insights. These ML algorithms can help machines classify things we hear, images we see, videos consumed.  Machine learning algorithms can also help discover new health remedies, can generate art or write songs, and can answer questions we ask.  Over the course of the series, we will review the types of machine learning: supervised, unsupervised, reinforcement as well as how to boost predictions through ensembling or through the use of AutoML tools. We will also be hands-on with popular ML methods for different problem types and data on a massive scale.  

    About the Speakers

    Chemere Davis

    Chemere is a passionate data science leader and educator with strong technical skills. Actively involved in the data science community outreach and volunteer opportunities. Experienced Financial Services data scientist, Chemere also leads many of our Customer Support engagements with these customers. Connect with her on LinkedIn.

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    Parul Pandey

    Parul is a Data Science Evangelist at H2O.ai and a Kaggle Kernels Grandmaster. She comes from an Engineering background and combines Data Science, evangelism, and community in her work. Her emphasis is to spread the information about H2O and Driverless AI to as many people as possible through meetups and writeups. Parul was one of Linkedin’s Top Voice in the Software Development category in 2019. She can be reached out at Linkedin: and Twitter.

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    Sanyam Bhutani

    Sanyam Bhutani is a Machine Learning Engineer and AI Content Creator at H2O.ai. He is also an active Kaggler, AI blogger on Medium & Hackernoon (Medium Blog link) with over 1 Million+ Views overall. Sanyam is also the host of Chai Time Data Science Podcast where he interviews top practitioners, researchers, and Kagglers. You can follow him on Twitter or subscribe to his podcast

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    Jo-Fai Chow

    Jo-fai (or Joe) has multiple roles (data scientist / evangelist / community manager) at H2O.ai. Since joining H2O.ai in 2016, Joe has delivered H2O talks/workshops in 40+ cities around Europe, US, and Asia. He is also the co-organiser of H2O’s EMEA meetup groups including London Artificial Intelligence & Deep Learning - one of the biggest data science communities in the world with more than 11,000 members. After years of non-stop #AroundTheWorldWithH2Oai action, he is now best known as the H2O #360Selfie guy. 

    LinkedIn

    Kaggle

    GitHub

    Twitter

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    Elena Boiarskaia

    As a Senior Solutions Engineer, Elena is passionate about helping H2O customers solve advanced data science problems while maximizing business value. With a background in Math and Economics, Elena loves to explore diverse applications of machine learning, earning her PhD from the University of Illinois with a dissertation focusing on predicting health outcomes using accelerometers. Previously, Elena worked with a variety of big data use cases on Spark while at Databricks, as well building machine learning models to identify manipulative activity in the US markets as the Lead Data Scientist at the Financial Industry Regulatory Authority (FINRA). LinkedIn

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    Benjamin Cox

    Ben Cox is a Director of Product Marketing at H2O.ai and leads Responsible AI research and thought leadership. Ben has held roles in leading teams of data scientists and machine learning engineers at Ernst & Young, Nike, and NTT Data. Ben holds a MBA from University of Chicago Booth School of Business with concentrations in Business Analytics, Economics, and Econometrics & Statistics, and a Bachelor of Science in Economics from the College of Charleston.

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    Dan Darnell

    Dan is an experienced product marketer with over twenty years of experience in leading technology companies. For the past nine years, he has been working on AI platforms and applications, including senior marketing roles at DataRobot, ParallelM, Talend, and Baynote. Before that, Dan was focused on analytics and optimization technologies at Adchemy, Interwoven, Oracle, and Siebel Systems. He holds an MBA from Carnegie Mellon University and a Bachelor's in engineering from The University of Colorado at Boulder.

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    David Engler 

    David Engler is a Senior Data Scientist and the Director of Customer Success at H2O. He has 15 years of experience leading data science teams in healthcare research and analytics and has over 20 publications in medical analytics as a primary author. He most recently built and led the analytics team for healthcare strategy at the University of Utah hospitals and clinics. David obtained his Ph.D. in Biostatistics from Harvard University.

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    Rafael Coss

    Rafael Coss is a Community Maker H2O.ai.  At H2O.ai he works on nourishing, supporting and growing the community(online, meetups, tutorials, ...).  He also works closely with prospects and customers helping them on their AI journeys.  And lastly, he also works on technical marketing (messaging, content, partners).

    Prior to joining H2O.ai, he was technical marketing and community Director and a developer advocate at Hortonworks. He was also the DataWorks Summit Program Co-Chair for the 3 years. Prior to Hortonworks, he was a Senior Solution Architect and Manager of IBM's WW Big Data Enablement team. At IBM he was responsible for the technical product enablement for BigInsights and Streams. Previously, he held several other positions in IBM, where he worked on tools, XML db, federated db and Object-Relational db.

    Twitter Linkedin

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  • Module 8 - Machine Learning Application Integration

    Contains 3 Component(s) Includes Multiple Live Events. The next is on 12/08/2020 at 7:00 AM (PST)

    Defining your problem, collecting your data, choosing your ML algorithm, training your model, and finally deploying your model are some of the major steps in your ML solution. However, the AI transformation is not going to occur until your Application and business process are coming consuming the results of the ML model in an application, and in a context a business can take an action.

    Defining your problem, collecting your data, choosing ML algorithm, training your model and finally deploying your model are some of the major steps in your ML solution. However, the AI transformation is not going to occur until your Application and business process are coming consuming the results of the ML model in an application and in a context a business can take an action.  

    Learning Outcomes

    • How can citizen data scientists train ML models quickly with AutoML?
    • What are the various options to consume the ML models in applications?
    • How to quickly build a decision making application that leverages ML models?

  • Module 7 - ML Ops with H2O.ai

    Contains 6 Component(s)

    Model operations is a critical component in creating value from machine learning models. After all, models are just experiments until they are deployed and used in business applications, to generate insights, or to make decisions. In this course, you will see how to use Model Ops to deploy, manage, and govern models in production environments.

    Model operations is a critical component in creating value from machine learning models. After all, models are just experiments until they are deployed and used in business applications, to generate insights, or to make decisions. In this course, you will see how to use Model Ops to deploy, manage, and govern models in production environments.

    Learning Outcomes
    • Describe how operators on the IT team can work together with data scientists to deploy models in production
    • Determine how to monitor models running in production and what to look for
    • Identify when to retrain models and how to put new versions of models into production without interrupting downstream services

  • H2O.ai en la Nube

    Contains 2 Component(s)

    En este Meetup nos enfocamos en las ofertas en la nube de H2O.ai y cómo puede comenzar su viaje de transformación de IA hoy.

    Detalles

    Hola makers!

    H2O.ai es una empresa visionaria de software del Silicon Valley que introdujeron al mercado nuevas plataformas y tecnologías para impulsar el movimiento de inteligencia artificial. Somos los creadores de H2O-3, la principal plataforma de aprendizaje de ciencia de datos de código abierto y de aprendizaje automático utilizada por casi la mitad de Fortune 500 y en la que confían más de 20,000 organizaciones y cientos de miles de científicos de datos de todo el mundo.

    Nuestra visión es democratizar la Inteligencia Artificial para todos, no solo una porción selecta. Creemos que podemos hacer de cada empresa una empresa de IA, proporcionando acceso a tecnologías innovadoras para que se embarquen en sus transformaciones digitales.

    Con nuestra plataforma de machine learning automatizado, H2O Driverless AI, creemos que hemos establecido el estándar en torno al aprendizaje automático, ofreciendo a los desarrolladores de software una forma sencilla de implementar sus modelos agnósticamente en plataformas en la nube con un enfoque en explicabilidad, visualización, “NLP” , “time series” y un equipo de atención al cliente ejemplar.

    Mira nuestro meetup donde nos enfocamos en las ofertas en la nube de H2O.ai y cómo puede comenzar su viaje de transformación de IA hoy.

    Rafael Coss

    Community and Technical Maker, H2O.ai

    Rafael es un creador de comunidad H2O.ai y Product Marketing. Antes de unirse a H2O.ai, fue director técnico de marketing y comunidad y advocate de desarrolladores en Hortonworks. También fue el co-presidente del programa de la DataWorks Summit durante 3 años. Antes de Hortonworks, fue Senior Solution Architect y Manager del equipo WW Big Data Enablement de IBM. En IBM fue responsable de la habilitación técnica del producto para BigInsights y Streams.

    Jorge Luis Hernandez Villapol

    Software Engineer and Data Scientist

    Jorge es ingeniero de software y científico de datos en H2O. Proviene de una experiencia en Ingeniería Electrónica donde la pasión y el entusiasmo del campo de la Mecatrónica y la Robótica lo llevaron a la IA y los Sistemas Inteligentes.
    Tiene un M.S. en Ingeniería Eléctrica de la UNT y una Licenciatura en Ingeniería Electrónica de la Universidad Simón Bolívar (USB) en Caracas, Venezuela

  • Module 6 - Responsible AI with H2O-3 and Driverless AI

    Contains 6 Component(s)

    In this hands-on session we will lead you in practical applications of the Machine Learning Interpretability methods to explain a models’ predictions. We will discuss methods such as building surrogate models, utilizing interpretability techniques like K-LIME, variable and feature importance for a machine learning model. We will also demonstrate the use of explainable techniques like partial dependence plots and Shapley values to to provide exact contributions of a feature to a prediction. Additionally we will examine fairness in a model through disparate impact analysis and use sensitivity analysis to debug our model and probe it for security and fairness.

    In this hands-on session we will lead you in practical applications of the Machine Learning Interpretability methods to explain a models’ predictions. We will discuss methods such as building surrogate models, utilizing interpretability techniques like K-LIME, variable and feature importance for a machine learning model. We will also demonstrate the use of explainable techniques like partial dependence plots and Shapley values to  to provide exact contributions of a feature to a prediction.  Additionally we will examine fairness in a model through disparate impact analysis and use sensitivity analysis to debug our model and probe it for security and fairness.

    Learning Outcomes
    • Build an explainable surrogate model 
    • Apply & interpret the K-LIME method for a ML model 
    • Apply & interpret the Variable/Feature Importance for a ML model
    • Apply & interpret a Decision Tree Surrogate Model for a ML model
    • Apply & interpret the Partial Dependence & ICE Plots for a ML model
    • Generate Shapley Values for a ML model
    • Examine a model for bias using Disparate Impact Analysis
    • Run Sensitivity/What-if Analysis for a ML model

  • Module 4 - Hands-On Deep Learning

    Contains 5 Component(s)

    Deep learning (DL) is a driving force for many of the artificial intelligence applications changing the world today in areas like image recognition and self-driving cars. In this hands-on module you will apply neural networks to practical examples.

    Deep learning (DL) is a driving force for many of the artificial intelligence applications changing the world today in areas like image recognition and self-driving cars. In this hands-on module you will apply neural networks to practical examples.

    Learning Outcomes
    • Select the appropriate deep learning task for a real-world application
    • Use a dataset to fit a new model  
    • Build a deep learning model based on business application
    • Assess the model performance in terms by error metrics for the DL task.

  • Tutorial 5B: Risk Assessment Tools in the World of AI, the Social Sciences, and Humanities

    Contains 14 Component(s)

    Learn how a global versus local behavior analysis can help you understand if your model behavior creates a disparate impact. Also, learn how the social sciences, humanities, and AI should work together to create a more fair integration of AI into our society.

    Learn how AI and risk assessment tools can be used in the Social Sciences

  • Introducción al Aprendizaje Automático (Machine Learning) con H2O-3

    Contains 2 Component(s)

    Te damos una introducción a la plataforma de aprendizaje automático de código abierto número 1, H2O-3 y te mostramos cómo puedes usarla para desarrollar modelos para resolver diferentes casos de uso.

    H2O-3 te permite aplicar el aprendizaje automático y realizar análisis predictivos para resolver tus desafíos comerciales. Aqui exploramos algunas aplicaciones de uso y también estaremos identificando las características y capacidades de H2O-3 para flujos de trabajo típicos de ciencia de datos.

    Franklin Velasquez

    Technical Marketing Engineer

    Franklin es un Ingeniero Técnico de Marketing en H2O.ai. Como parte de la familia de H2O.ai, Franklin trabaja creando tutoriales para la plataforma de H2O-3 usando Python y R. Franklin también es encargado del Programa Académico de H2O.ai y uno de los administradores del Centro de Aprendizaje.