Stats for nerds
3recommend
4mentions
4people
2020–2021years
75% recommend25% neutral
Mentions per year
What Hacker News says
- The latest thinking around 'data science as a product' seems to take this approach. See Building Machine Learning Powered Applications for one resource https://www.oreilly.com/library/view/building-machine-learni...
- I typically recommend a few different books to everyone who finishes the bootcamp, based on a self-assessment they take. I recommend some books based on their strengths, so they can find a career path sooner, and some books based on their weaknesses, so they can widen their cone of oppportunity within ML.
In our consultancy, data science is done in Python and SQL (and PySpark, but I don't hand out books on that during bootcamp!), and ML delivery is a combination of math, software engineering, and architecture/product owner disciplines.
If you're strong in software engineering, I recommend Machine Learning Mastery with Python by Jason Brownlee as it's very hands-on in Python and helps you run code to "see" how ML works.
If you're weak in software engineering and Python, I recommend A Whirlwind Tour Of Python by Jake VanderPlas, and its companion book Python Data Science Handbook.
If you're strong in architecting / product management, I recommend Building Machine Learning Powered Applications by Emmanuel Ameisen since it explains it more from an SDLC perspective, including things like scoping, design, development, testing, general software engineering best practices, collaboration, etc.
If you're weak in architecting / product management, I typically recommend User Story Mapping by Jeff Patton and Making Things Happen by Scott Berkun, which are both excellent how-tos with great examples to build on.
If you're strong in math, I recommend Understanding Machine Learning from Theory to Algorithm by Shalev-Shwartz and Ben-David, as it has all the mathematics for ML and actually has some pseudocode for implementation which helps bridge the gap into actual software development (the book's title is very accurate!)
For someone who is weak in the math of ML, I recommend Introduction to Statistical Learning by Hastie et al (along with the Python port of the code https://github.com/emredjan/ISL-python ) which I think does just enough hand holding to move someone from "did high school math 20 years ago" to "I understand what these hyperparameters are optimizing for."
Anyway, I've spent a lot of time reading and reviewing books about ML, and my key takeaway is ones that get you closer to writing actual code to solving actual problems for actual people are the ones to focus on.
- Disclaimer: This is a book I wrote.
Building Machine Learning Powered Applications walks you through building an ML application end-to-end, from product idea to a (simple) deployed version.
The free first chapter is available here https://mlpowered.com/book/
The github is at https://github.com/hundredblocks/ml-powered-applications
- Always looking for books that build full projects, or that integrate the foundations of many disciplines into a single project book.
Game Engine Black Book: Wolfenstein 3D http://fabiensanglard.net/gebbwolf3d/
Other examples would be things like building Machine Learning Powered Applications, Modern Full-Stack Development,Black Hat Python etc.
This was asked last year, and I came into some very interesting reads. I figured it would be worth asking again!
Related
Understanding Machine Learning
12 recommend · 18
Introduction to Statistical Learning
85 recommend · 120
Machine Learning
16 recommend · 29
Programming Collective Intelligence
86 recommend · 135
Hands-On Machine Learning with Scikit-Learn and TensorFlow
16 recommend · 21
Learning From Data
31 recommend · 49
2020–2026
