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Monica Rogati

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Monica Rogati is a data scientist and the former Vice President of Data of Jawbone. Before that, she was a senior data scientist at LinkedIn.
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Programming Collective Intelligence book cover
Programming Collective Intelligence
Segaran - 2007-09-25 (first published in 2002)
Goodreads Rating
Build smart web applications that analyze and understand data through machine learning and statistics. With Programming Collective Intelligence, you can access datasets from other websites, collect data from your own applications, and draw conclusions about user experiences, marketing, and human behavior in general. This book covers collaborative filtering, clustering, search engine features, optimization algorithms, spam filtering, decision trees, support vector machines, and more using clear explanations and code examples. Don't settle for simple database applications – put the wealth of internet data to work for you.
Monica Rogati
2021-08-20T23:51:51.000Z
When I met Toby, I told him his book 'Programming Collective Intelligence' almost made me cry -- not a typical reaction to a technical book. I explained it had the most useful bits & intuition built during my 6 yr long PhD packaged in a practical, easy to follow guide. Huge loss.      source
Building Machine Learning Powered Applications book cover
Building Machine Learning Powered Applications
Going from Idea to Product
Emmanuel Ameisen - 2020-02-11
Goodreads Rating
This hands-on guide teaches you how to design, build, and deploy machine learning (ML) powered applications, from initial idea to deployed product. With code snippets, illustrations, and screenshots, you'll learn key ML concepts and tools for building real-world applications. This book is perfect for data scientists, software engineers, and product managers with little or no ML experience, who want to learn the process step-by-step. You'll discover how to plan and measure success, build a working ML model, improve performance, and deploy and monitor models in a production environment.
Monica Rogati
2020-05-12T00:10:54.000Z
Before starting to code an ML algorithm, spend one hour trying to do its job. Be the algorithm. Learned this early on from @IBMResearch's Salim Roukos. You can read more in the @mlpowered book, which is full of practical ML advice missing from textbooks:      source