AI Foundations Course

Artificial Intelligence is one of the biggest transformational business opportunities in modern time but many organizations are challenged to successfully adopt AI within and across the enterprise. AI is not magic or one-size-fits-all, but the use of AI can unlock tremendous value from data assets, derive real actionable outcomes, and drive business innovation through automation and personalization. Building a world-class, scalable analytic strategy requires recognition of several factors like identifying where your organization falls in the AI journey, addressing business constraints when it comes to time and talent, building trust in the use of AI within an organization, and for its customers. This series will walk you through the strategic aspects of determining what is needed to build effective predictive solutions using AI concepts at scale. The instructors in this series will review each stage in the AI transformation journey including deployment--providing learners with foundational knowledge, and new insights and perspectives.

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.

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  • Survey

    Contains 1 Component(s)

    Please tell us more about yourself

    Please choose the answer that best describes you

  • Module 1 - An AI Transformation Journey

    Contains 5 Component(s)

    The chances of successfully implementing AI strategies within an organization significantly improve when you can recognize where your organization is on the maturity scale. Over this course, you will learn the keys to unlocking value with AI which include asking the right questions about the problems you are solving and ensuring you have the right cross-section of talent, tools, and resources. By the end of this module, you should be able to recognize where your organization is on the AI transformation spectrum and identify some strategies that can get you to the next stage in your journey.

    The chances of successfully implementing AI strategies within an organization significantly improve when you can recognize where your organization is on the maturity scale.  Over this course, you will learn the keys to unlocking value with AI which include asking the right questions about the problems you are solving and ensuring you have the right cross-section of talent, tools, and resources. By the end of this module, you should be able to recognize where your organization is on the AI transformation spectrum and identify some strategies that can get you to the next stage in your journey. 

    Learning Outcomes
    • Outline the AI Transformation Journey
    • Recognize the correct type of analytic capability to use based on a defined business need
    • Categorize roles and responsibilities of key AI talent in the AI journey

  • Module 2 - Demystifying Artificial Intelligence

    Contains 4 Component(s)

    Artificial intelligence is not magic but it can be extremely transformative. This module will pull back the curtain on what artificial intelligence is (and isn’t), identify where in the world you can spot AI in use and dive into a few common examples of AI in action across various industries.

    Artificial intelligence is not magic but it can be extremely transformative.  This module will pull back the curtain on what artificial intelligence is (and isn’t), identify where in the world you can spot AI in use and dive into a few common examples of AI in action across various industries.

    Learning Outcomes
    • Correctly identify sub-specialties of AI
    • Apply use cases to the appropriate AI sub-specialty
    • Classify a real-world business problem as an AI problem

  • Module 3 - Introduction to Machine Learning

    Contains 8 Component(s)

    Machine learning is one of the most active areas of artificial intelligence, powered by data. In this module, you will learn the essential building blocks of machine learning through the use of case studies. You will be introduced to the data science workflow and frameworks to help you turn business problems into machine learning problems. By the end of this module, you will be able to apply machine learning methods to a wide variety of domains and applications.

    Machine learning is one of the most active areas of artificial intelligence, powered by data. In this module, you will learn the essential building blocks of machine learning through the use of case studies. You will be introduced to the data science workflow and frameworks to help you turn business problems into machine learning problems. By the end of this module, you will be able to apply machine learning methods to a wide variety of domains and applications.

    Learning Outcomes
    • Identify applications of machine learning in practice
    • Describe the core differences between regression, classification, and clustering, and reinforcement learning
    • Select the appropriate machine learning task for a potential application
    • Reframe a business problem as a machine learning problem

  • Module 4 - Introduction to Deep Learning

    Contains 4 Component(s)

    Deep learning is a driving force for many of the artificial intelligence applications changing the world today in areas like image recognition, natural language processing (NLP), and self-driving cars. In this module, you will learn the basics of neural networks and dive into practical examples of deep learning, as well as new advances being made in the field. By the end of this module, you will be able to take a deep learning concept and be able to spot examples of its application in the real world.

    Deep learning is a driving force for many of the artificial intelligence applications changing the world today in areas like image recognition, natural language processing (NLP), and self-driving cars. In this module, you will learn the basics of neural networks and dive into practical examples of deep learning, as well as new advances being made in the field. By the end of this module, you will be able to take a deep learning concept and be able to spot examples of its application in the real world.

    Learning Outcomes
    • Identify applications of deep learning in practice
    • Describe the core differences between ANN, CNN, and RNN
    • Describe a GAN
    • Describe reinforcement learning
    • Select the appropriate deep learning algorithm for a potential application

  • Module 5 - AI & Big Data

    Contains 4 Component(s)

    It’s no secret that the power of artificial intelligence starts with data. The amount of information that is created is growing each day and that brings challenges when applying AI problems at scale. In this module, we will dig into big data by explaining what it is, how it works and how it relates to ML. We will also introduce some techniques and common tools used to analyze big data.

    It’s no secret that the power of artificial intelligence starts with data.  The amount of information that is created is growing each day and that brings challenges when applying AI problems at scale.  In this module, we will dig into big data by explaining what it is, how it works and how it relates to ML.  We will also introduce some techniques and common tools used to analyze big data.

    Learning Outcomes
    • Define big data
    • Describe the role of Hadoop and Spark with big data
    • Correctly match big data predictive analytics techniques to real-world applications

  • Module 6 - Responsible AI

    Contains 4 Component(s)

    The use of artificial intelligence is widespread and with the use of AI systems comes questions transparency, trust, and the role of humans when AI is used. Responsible AI is an emerging discipline that seeks to provide some frameworks for addressing the questions about AI. In this module, we will discuss the major areas of Responsible AI, considerations in the enterprise with Responsible AI, and introduce techniques that can help answer questions like is the AI system making the best decisions? Can AI be trusted? Is this AI system secure? This module will use real-world examples and use cases to show how Responsible AI is used right now and some areas where there may be a future impact.

    The use of artificial intelligence is widespread and with the use of AI systems comes questions transparency, trust, and the role of humans when AI is used.  Responsible AI is an emerging discipline that seeks to provide some frameworks for addressing the questions about AI.  In this module, we will discuss the major areas of Responsible AI, considerations in the enterprise with Responsible AI, and introduce techniques that can help answer questions like is the AI system making the best decisions? Can AI be trusted? Is this AI system secure? This module will use real-world examples and use cases to show how Responsible AI is used right now and some areas where there may be a future impact.

    Learning Outcomes

    • Correctly classify the types of Responsible AI by description
    • Distinguish between categories of Responsible AI in the enterprise
    • Correctly apply the appropriate Responsible AI method to do deal with issues like explainability, bias, security, and model debugging

  • Module 7 - Machine Learning Operations

    Contains 4 Component(s)

    In this module, you will learn about the basics of machine learning operations, known as model ops or ML Ops. Model operations covers vital areas related to running models in production environments. Key topics include production model deployment, production model lifecycle management, and production model governance. In each area, we will discuss the issues and solutions as organizations move from manual processes to a more managed and governed approach.

    In this module, you will learn about the basics of machine learning operations, known as model ops or ML Ops. Model operations covers vital areas related to running models in production environments. Key topics include production model deployment, production model lifecycle management, and production model governance. In each area, we will discuss the issues and solutions as organizations move from manual processes to a more managed and governed approach.

     Learning Outcomes
    • Describe the roadblocks for production models in today's environment
    • What are the functional areas of production operations for machine learning models
    • Distinguish between the value of taking a managed and governed approach to machine learning production issues
  • Module 8 - What to Do Next

    Contains 1 Component(s)

    Developing and learning the AI 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 AI 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 AI Foundations badge.

  • AI Foundations Final Exam & Badge

    Contains 2 Component(s)

    Complete this course by taking the final exam and obtaining your badge.

    Complete the Exam with an 80% and receive your H2O.ai ML Foundations Badge

You must be logged in to post to the discussion
  • Renzo Silva

    Here is a big shout-out to the H2O.ai team for preparing these courses and making them available to learners from a diverse background  :thumbsup:

    Reply
  • KS

    I happen to register late for Module 1 live session, had to take the recorded session and finished the Module 1 - quiz after July 7. Would this impact the earning the badge ?

    Reply
  • FV

    Hi Karthik, as long as you pass all the quizzes with a score of 8/10, you will receive the AI Foundations Badge. We recommend that you complete the quizzes, if you can, before upcoming modules so that you can be on track with the live sessions. 

    Reply
  • PM

    Hi, I will be on leave for some of the Module 3 sessions. Will I be able to view the sessions on-demand?

    Reply
  • FV

    Hi Philip, yes, we are making all the replays and slides available so that you can go back and review them in case you miss any live session. 

    Reply
  • RC

    We are live w Session 3 in Module 1: AI in Healthcare and Covid19

    Reply
  • FQ

    pls first login to zoom with the same e-mail they used for the course.  And then try this link: https://h2oai.zoom.us/webinar/...

    Reply
  • SM

    I am unable to connect I had zoom account also can u please help me.

    Reply
  • NK

    Hi Team

    Getting below error, not able to join the session.

    Zoom API Error: User does not exist: nagavenkatanaveen.kollipara@gmail.com.

    Sorry, you do not have permission to access /webinar/178117 with Request method: GET

    Reply
  • AG

    Hi Team

    Getting below error, not able to join the session.

    Zoom API Error: User does not exist: amangupta.rocks1996@gmail.com.

    Sorry, you do not have permission to access /webinar/178117 with Request method: GET

    Go back home

    Reply
  • VG

    Hi,

    Due to India Evening schedule of sessions I may have clashe with my Org meetings.

    Will we be having recording for these sessions?


    Reply
  • FV

    Hi Vaibhav, yes, there will be a recording of each session. We will let you know once the recordings are available. 

    Reply
  • SK

    Hi, Can you please tell me the duration of each of the sessions? I see it starts at 7AM PDT. Thanks

    Reply
  • FV

    Hi Shailesh, each session is 60 minutes long. However, the study sessions during the weekends are 30 minutes long.

    Reply