PyData Hamburg May Meetup

Hi PyData Hamburg community,

In this May Meetup we are excited to have two inspiring talks on Machine Learning, brought to you by IBM.

What you need to know to participate in remote meetups:

• Our virtual room capacity is limited to 300 attendees. So RSVP on meetup.com please and do take the time to cancel if you cannot join.
• The link to the Zoom meeting will be displayed to you on meetup.com when you RSVP. Please do not share it.
• You need to be logged in with a free Zoom account - this is an anti-troll measure.
• Please check in a few minutes before the event starts so we can support you with questions about access.

Take care, stay healthy and see you soon!

Your PyData Hamburg crew

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# Networking: join at 18:15h, bring a beverage and meet other attendees sharing your interest in data science, machine learning, AI etc. - the talks will start at 18:30h

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# (Talk 1) Nandita Sharma: A Loan Defaulter Prediction Model

In this talk, Nandita will explain how machine learning techniques play a prominent role in detecting the likelihood of defaulting in advance - by developing an understanding of customers' behavioral patterns before granting a loan. Join this talk to understand how AI helps banks solve and understand their liquidity risk and financial abilities while gaining and retaining current customers.

About Nandita

Nandita is Data Scientist and Data Analyst, passionate about AI, neural networks & deep learning. She works at the National College of Ireland as an Assistance Teacher, where she teaches and mentors 80+ students for machine learning projects. In her free time, she loves to learn new things, dancing, painting & baking.

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# (Talk 2) Paco Nathan: AutoML

AutoML is a very active area of AI research in academia as well as R&D work in industry. The public cloud vendors each promote some form of AutoML service. Tech unicorns have been developing AutoML services for their data platforms. Many different open source projects are available, which provide interesting new approaches. But what does AutoML mean?

Ostensibly automated machine learning will help put ML capabilities into the hands of non-experts, help improve the efficiency of ML workflows, and accelerate AI research overall. While in the long-term AutoML services promise to automate the end-to-end process of applying ML in real-world business use cases, what are the capabilities and limitations in the near-term?

About Paco

Known as a "player/coach", with core expertise in data science, natural language, cloud computing; ~40 years tech industry experience, ranging from Bell Labs to early-stage start-ups. Advisor for Amplify Partners, IBM Data Science Community, Recognai, KUNGFU.AI, Primer. Lead committer PyTextRank, kglab. Formerly: Director, Community Evangelism @ Databricks and Apache Spark. Cited in 2015 as one of the Top 30 People in Big Data and Analytics by Innovation Enterprise.

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PyData is a community for developers and users of open source data tools. PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. The PyData Code of Conduct governs this meetup.