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1. #An introduction to statistical learning solutions how to#
2. #An introduction to statistical learning solutions Offline#

It's an alternative to the deeper coverage of the same materials given in Elements of Statistical Learning by some of the same authors. ISLR is written for practitioners giving enough theory to have substance, while omitting enough to keep you from drowning. These books felt like a depth first search of the topic. I’d explored similar books in the past, but I’d always stopped after slogging through them overwhelmed by the density of the chapters. Reading it, at this point in my career, reminded me of my first year out of undergrad sneaking into work at Ford Motor Company an hour early to borrow from the company library to discover books like The Pragmatic Programmer, Clean Code, Effective Java, Test Driven Development by Example, and others.Īfter a year on a team building recommender systems for LinkedIn Groups and Learning products, with ISLR I finally found a book that structured the many things I’ve been learning the hard way and at the right level of depth. There are a couple of books in my library that have become part of the canon of my engineering philosophy.

A few of it’s chapters were being covered in a reading group at work. I stumbled onto Introduction to Statistical Learning (ISLR).

I set a goal in 2016 to be able to retire that phrase. Early in my tenure on the team I would introduce myself as a systems engineer with data aspirations. I understood the rough concepts, but much of it was still a black box. I was a novice in the data space when I joined the relevance team.

#An introduction to statistical learning solutions how to#

I was eager to not just deliver recommendations to members, but to learn how to generate them as well. I was at the boundary of the application and our relevance team who was responsible for the quality of recommendations. A lot of what I did was build systems that could ingest news recommendations and reliably deliver them to members.

#An introduction to statistical learning solutions Offline#

At the time, I was an applications engineer on our news product building mid-tier web services for mobile applications as well as offline workflows for email distribution. A little over a year ago, I began a career transition of sorts.