dafrdman

You can now find a pdf version of the book at https://github.com/dafriedman97/mlbook/blob/master/book.pdf. JupyterBook is still working on the PDF creation, so this doesn't have any of the images unfortunately. That said, most of them aren't too important (until the neural net chapter, where they get a little more important)

Thanks so much! I would say two major differences: 1, as you mention, it codes each method up from scratch in Python readers can really see each step the method uses. 2, it is focused on the derivations of these methods, rather than their intuition, applications, etc.

It's definitely not deep learning focused. I wanted to start by introducing the models machine learning practitioners should all know. But collaborative filtering and stuff along those lines would be a good addition! Thanks for the feedback.

My hesitance with "Application" is that sounds like I'm going to use some interesting dataset or do some cool project (and this is essentially using iris to build basic models). How about "code" becomes "implementation"?

That's sensible. Maybe change construction to code and code to application? Or keep construction but rename code? I'll have to brainstorm. I definitely don't want people missing the construction section so this is great feedback. Thanks!

I definitely agree. I should add more comments explaining what things like .T does--it's not that it's hard to grasp, but it might turn away newbies. Thanks for the suggestion!

Pandas is only used in the "code" sections, which use packages like scikit-learn anyway

Agreed. That's #1 on my list right now.

I agree though I saw that as outside the scope of this book. I tried to be clear in the introduction that the book is a "user manual" of sorts that simply shows how to construct models, rather than how to decide between them, what the benefits of each are, etc. That information is certainly important but I felt it had been covered more than adequately by books like Elements of Statistical Learning

Perhaps I should have been clearer, but the "code" section within each chapter is not "from scratch". The "construction" section is "from scratch" in that it only uses numpy (not scikit learn). The scikit-learn part is just so new users can see how these could be fit in practice.

Good question. I definitely prefer downloadable books myself. I made it in JupyterBook because that was easiest with the executable ipynb files. I'll look into whether I can make it downloadable and update you if so.

Good ideas. I think I'll try to add an appendix, minimize the number of numpy functions used, and explain any of the weird ones that are real time savers. Thanks for all your thought.

I hadn't even considered licensing it. Want to email me and we can talk? My email is [email protected]. That said, you're welcome to use it (though my lawyer father suggests I say that this "verbal contract" is revocable and non-exclusive).

Yeah the book was built with JupyterBook. It's an awesome tool but I lose track of what it does to the .md files when creating the website.

Thanks for the feedback. Sounds like you're not alone in your thoughts on numpy. I'll brainstorm better solutions--maybe explaining each numpy function in a side note or adding a numpy overview to the appendix. It just makes things so much easier so I am hesitant to go numpy-less.

Getting started was definitely the hardest part. I put the book together with JupyterBook (https://jupyterbook.org/intro.html), which I would highly recommend! Really neat tool and a helpful community.

Thanks for the catch! Ugh those pesky $$s. Changed it now.

Looks like you found the repository. Do you think it would be enough to raise an issue there? (at https://github.com/dafriedman97/mlbook)? I'll look into linking directly from the book to the repo.

Thanks!

Thanks so much for your feedback. Definitely open to comments!

I agree 100% that any use of packages can be intimidating for newbies. I experimented at first with creating the models without using numpy and I thought that it actually made it less clear rather than more clear. It's obviously a tradeoff--you see where everything comes from (rather than np.mysterious_function()) but you take 5 lines of code to do the same thing that a single numpy command could accomplish. I felt in the end that it distracted from the real purpose of the code, which is to demonstrate how the model works.

Do you think a compromise would be to add a section to the appendix introducing numpy? Introducing arrays, random instantiation, stuff like that? Otherwise I might consider adding a no-numpy version in the future.

Thanks so much for your feedback!

Thanks for the helpful feedback. I wanted to put emphasis on the graphs so I chose to hide the code but maybe it's not worth the cuteness of the "click to show". Changing that now.

Good call! I'll work on that ASAP

The approach to this book is very similar to nnfs.io (a similar focus on deriving models from the bare bones). The biggest difference is that his focuses on deep learning while mine covers a) a wider range of models and b) more introductory methods (linear regression, logistic regression, naive bayes, etc.)

It is definitely heavy on the math notation. Since the purpose of the book is to provide mathematical derivations, it's impossible to do that without lots of notation. That's why I added the notation and conventions page (right after the table of contents). If you do get a chance to look through it and see any notation in particular that is difficult to follow, please let me know and I'll update that page.

As for the code, knowing Python is a loose pre-requisite, but familiarity with object oriented programming in general should be enough to at least follow along. Most of the code does one of two things: 1) manipulates data represented as vectors/matrices or 2) creates/manipulates the objects themselves. But I should work on making the commenting clearer so it's apparent what each step does, even for someone less comfortable with Python.

Thanks so much for your feedback!

I'm linking to a free online book I just finished called Machine Learning from Scratch. The book aims to cover the complete, technical, "under the hood" details that other ML textbooks don't. To do that, it shows all the mathematical steps to derive common algorithms and demonstrates how to fit each one from scratch in Python (using just numpy).

You might like this book if you are interested in ML and like to really understand what's going on behind the algorithms. Personally, when learning about these algorithms I would understand them intuitively but not feel comfortable constructing them myself. Even if you totally grasp the algorithms, seeing them derived from scratch is a great way to better understand the comparative advantages between competing models.

The code and math are pretty simple, so there should be a low barrier to entry.

I'm hoping to make the book somewhat of a breathing document, so if you have any edits, questions, or suggestions, I'm all ears. You can reach me at [email protected]. Thanks a lot!