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You're reading from  The AI Product Manager's Handbook

Product typeBook
Published inFeb 2023
Reading LevelIntermediate
PublisherPackt
ISBN-139781804612934
Edition1st Edition
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Irene Bratsis
Irene Bratsis
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Irene Bratsis

Irene Bratsis is a director of digital product and data at the International WELL Building Institute (IWBI). She has a bachelor's in economics, and after completing various MOOCs in data science and big data analytics, she completed a data science program with Thinkful. Before joining IWBI, Irene worked as an operations analyst at Tesla, a data scientist at Gesture, a data product manager at Beekin, and head of product at Tenacity. Irene volunteers as NYC chapter co-lead for Women in Data, has coordinated various AI accelerators, moderated countless events with a speaker series with Women in AI called WaiTalk, and runs a monthly book club focused on data and AI books.
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The old – exploring ML

ML models attempt to create some representation of reality in order to help us make some sort of data-driven decision. Essentially, we use mathematics to represent some phenomenon that’s happening in the real world. ML essentially takes mathematics and statistics to predict or classify some future state. The paths diverge in one of two ways. The first group lies with the emergence of models that continue to progress through statistical models and the second group lies with the emergence of models that try to mimic our own natural neural intelligence. Colloquially, these are referred to as traditional ML and DL models.

You can think of all the models we covered in the Model types – from linear regression to neural networks section of Chapter 2 as ML models, but we didn’t cover ANNs in great depth. We’ll discuss those further in the Types of neural networks section later on in this chapter. In this section, we will take a look...

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The AI Product Manager's Handbook
Published in: Feb 2023Publisher: PacktISBN-13: 9781804612934

Author (1)

author image
Irene Bratsis

Irene Bratsis is a director of digital product and data at the International WELL Building Institute (IWBI). She has a bachelor's in economics, and after completing various MOOCs in data science and big data analytics, she completed a data science program with Thinkful. Before joining IWBI, Irene worked as an operations analyst at Tesla, a data scientist at Gesture, a data product manager at Beekin, and head of product at Tenacity. Irene volunteers as NYC chapter co-lead for Women in Data, has coordinated various AI accelerators, moderated countless events with a speaker series with Women in AI called WaiTalk, and runs a monthly book club focused on data and AI books.
Read more about Irene Bratsis