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A beginner from which perspective? This book is ideal for someone with no knowledge of ML. It is a level 100 equivalent book. Given its mandate of 100 pages,

A beginner from which perspective? This book is ideal for someone with no knowledge of ML. It is a level 100 equivalent book. Given its mandate of 100 pages, it sacrifices breadth for depth - it covers most of its topics in 1 page or fewer. It has lots of paragraphs that end with "There are many advanced techniques that go beyond the scope of this book." The best thing about the book is it is fairly comprehensive and well-organized as an end-to-end overview. It has short, 1-2 paragraph chunks about the 80% of ML work that covers 99% of what ML is. It of course doesn't go into detail but it also does not have many topics missing, which means if you make a mind map while you're reading you'll have a great starting point to branch out further once you're done. When you're done with this book, you should be able to speak intelligibly about most aspects of machine learning development and design. You should also be able to find strengths and weaknesses in your understanding based on what you came to the table with to pursue further. I think this book coupled with some kind of lab practical or a series of Kaggle contests and notebooks to apply what you learn in this book would be an excellent "ML 101" course. I teach out of this book for a 2 week bootcamp to new hires and converts to the data science practice at the consultancy I work at.

kthejoker2 6y ago
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 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.
- melenaboija 6y ago
  > 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 And depending on the industry understand what you are doing as it is mandated by regulations.

kthejoker2 6y ago

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 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.

melenaboija 6y ago
> 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 And depending on the industry understand what you are doing as it is mandated by regulations.