(Part 2) Top products from r/learnmachinelearning

if you're doing this to help prepare to switch careers, look at industries and companies you might be interested in. Every vertical has different tech stack choices that are common. Medicine has a lot of SAS, pharmaceutical researchers I've met all use R, main industry and research at this point is mostly Python. Python gets you the most bang for your buck. If you need to step outside ML and throw together a back end DB, a REST API and a front end to glue the whole thing together or whatever, Python's just as useful there as it will be with ML. I don't use R, but from what I hear it's much less versatile. The Stats libraries for R are a lot more mature though apparently, so if you want to get into doing some more intense statistical stuff, I've heard Python is a little less friendly. I haven't run into any of those limitations, but I've been more playing around with RL and stuff, and doing less intense statistical analysis with rigorous confidence bounds or whatever.

For forecasting from historical data, you're looking at time series. Unfortunately I don't know a ton about time series modeling yet. It's much more complicated than a situation where you're assuming N iid draws from a stationary distribution (the 'typical' entry point for classification and such that you see in supervised machine learning).

Keeping in mind that I have no business giving you advice where to start because I haven't made the trek yet myself, I've heard good things about Time Series Analysis and Its Applications. It's a grad level stats book though, so I hope you aren't joking about your math background, haha. The examples in that book are all in R too, as a head's up.

For a slightly easier (but still standard) introduction to the topic, I've also heard Wei's Time Series Analysis is decent. If you look around for a good introduction to multivariable time series analysis though, I'm sure you could find a lot of resources and judge for yourself what would most fit your needs. If you did pick one of those two books to pound out, I suspect you'll have a radically better idea how to go the rest of the way and get into practical application. As you're getting into the theory (whatever resource you use), I'd highly recommend picking a few datasets you're interested in (Kaggle might be a good source, to go with whatever you care to get into for your own reasons) and as you go, try applying the various methods you're learning on those few different datasets to get some sense of how it works and why. Pro-tip: one or two of your go-to toy datasets should be generated yourself with some simple to understand function to help give a really easily understandable case to play with, where your intuition can still hold up. y(t) = sin(t) +kt + N(0,b) maybe, or some simple dynamic process of the form y^t+1 = f(y^t ).

But either way, make sure you're rolling up your sleeves and cracking your assumptions against actual data in code to make sure you get the idea. All theory and no practical makes Jack a dull boy.

Edit: if you want a more broad introduction without necessarily having the rigorous focus on time series forecasting, 'applied predictive modeling' and 'introduction to statistical learning' are both good big picture intros. The new hands on machine learning book is good too, but more narrow and less comprehensive. Elements of Statistical Learning is kind of the defacto standard reference text going over all the common algorithms from a mathematical perspective. If you have the mathematical maturity to tackle ELS, that'd be a great way to start to get a deep foundation in the theoretical ideas across ML as a whole, though obviously none of that is going to be time series specific.

To be honest laptops are not good for ML, even desktops are not good enough, but since you're talking about using the Cloud, I would recommend a laptop wih at least 17" inches 1080 to clearly see you source code, an SSD of at least 240 GB for fast booting, a laptop with a good battery, I've seen people using external GPUs before, if you could research a little to find a laptop that supports extarnal GPUs getting an average laptop could be a good option, so the specs would be

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Ryzen 5 or Ryzen 7: Better than Intel for multitasking and price/performance

at least 8 GB of RAM: but 16 GB would be recommended

an SSD of at least 256 GB: this is a gamer change for every use case

A laptop with at least a monitor with 15.6" 1080p, but 17" is recommend so you could use two windows at a time.

You didn't told us your budget, but I've selected a few deals on Amazon.com, I think people can help by commeting down below, you can also watch reviews and unboxings to get a grasp of the product here a few ones that you might consider.

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https://www.amazon.com/Lenovo-20NF0018US-Ryzen-3700U-15-6In/dp/B07SH7JDJL/ref=sr_1_4?crid=1AMSARPHDFTI0&keywords=laptop+ryzen+7+3700u&qid=1572450997&sprefix=laptop+ryzen%2Caps%2C233&sr=8-4

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https://www.amazon.com/dp/B07VF6PJZH/ref=psdc_13896615011_t4_B07QGNL94H

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https://www.amazon.com/HP-17-3-inch-Touchscreen-Display-Quad-Core/dp/B07W9295Y7/ref=sr_1_3?keywords=laptop+ryzen+17+inch&qid=1572451425&sr=8-3

/u/LengthContracted this is a good book, as is Daphne Kollers book on PGMs as well as the associated course http://pgm.stanford.edu

A sample of what is on my reference shelf includes:

Real and Complex Analysis by Rudin

Functional Analysis by Rudin

A Book of Abstract Algebra by Pinter

General Topology by Willard

Machine Learning: A Probabilistic Perspective by Murphy

Bayesian Data Analysis Gelman

Probabilistic Graphical Models by Koller

Convex Optimization by Boyd

Combinatorial Optimization by Papadimitriou

An Introduction to Statistical Learning by James, Hastie, et al.

The Elements of Statistical Learning by Hastie, et al.

Statistical Decision Theory by Liese, et al.

Statistical Decision Theory and Bayesian Analysis by Berger

I will avoid listing off the entirety of my shelf, much of it is applications and algorithms for fast computation rather than theory anyway. Most of those books, though, are fairly well known and should provide a good background and reference for a good deal of the mathematics you should come across. Having a solid understanding of the measure theoretic underpinnings of probability and statistics will do you a great deal--as will a solid facility with linear algebra and matrix / tensor calculus. Oh, right, a book on that isn't a bad idea either... This one is short and extends from your vector classes

Tensor Calculus by Synge

Anyway, hope that helps.

Yet another lonely data scientist,

Tim.

re. question 2, to my knowledge, there's no comprehensive book or MOOC that covers the applications of machine learning in biology. there's this book, but it's almost 20 years out of date at this point (which is a huge amount of time in this field), so i wouldn't recommend it. it seems to focus mostly on analysis of genomic sequencing data.

it's probably a safer bet to read review papers that are more recent. this paper covers a lot of current applications in molecular biology and human genetics, and brendan frey is well known in the field. for deep learning, there's this collaboratively written review, which is probably the most comprehensive resource you'll find.

if you have a more specific subfield of biology that you're interested in, i can try to help you find more resources.

Hey I'm very very new to machine learning.
BUT I am very familiar with your situation. School didn't teach me anything and I don't think I can take the topics I should know into the workforce.

I've been reading this book
https://www.amazon.com/How-Not-Be-Wrong-Mathematical/dp/0143127535

And it has put a lot into perspective.

A lot of my education (this is at least for me going to school in the US) has been more about rote memorization and just glossing over concepts. Not really about the logic behind it, I doubt my grade school teachers even understood the concepts better than I did. But now I'm older I'm sucking it up and actually teaching myself the basics all the way up. Going to extremes as learning the Common Core math basics (and I mean the basics!) even though I have no kids.
While it seems like a lot to relearn, your actually going to be working on understanding the concept more and less about solving the problems and getting the right answer, so it's quicker than you can believe.

I say get some books that put stats into perspective, even in a fun way like the book I'm reading. Anything putting you to sleep is cause you are forcing yourself, so read something interesting in the field even if it's for people without any stats knowledge.
Go back and see your old coursework from new eyes. Do side projects and analyze things on your own and ask for help in forums.

Well, that's what I'm doing at least with all math and CS topics.

Yeah, school sucks. I think I understand why (I think) Mark Twain said "I don't let schooling get in the way of my education"

Machine Learning: A Bayesian and Optimization Perspective is a great and very indepth book that focuses more on the concepts instead of the algorithms, while still covering almost everything the field has to offer. It's my favorite book on the subject, next to Kevin Murphy's book

I really like Deep Learning by Ian Goodfellow, et al. You can but it from Amazon at https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618/ref=sr_1_1?ie=UTF8&qid=1472485235&sr=8-1&keywords=deep+learning+book. If you are a little cash strapped, there is an html version at http://www.deeplearningbook.org/. Of course, this book is specifically focused on neural networks as opposed to ML in general.

I have this book, and it has been really useful for learning the theory and math behind many of the main ML models: https://www.amazon.com/Fundamentals-Machine-Learning-Predictive-Analytics/dp/0262029448/ref=sr_1_2?ie=UTF8&qid=1499437032&sr=8-2&keywords=machine+learning+mit

Worth having in my opinion!

Based on the description, I don't think this is it but maybe the OP had a fundamental misunderstanding of it.

https://www.amazon.com/When-Genius-Failed-Long-Term-Management/dp/0375758259

But that's my best guess. Or The Quants, but that doesn't really line up with the comment either. Idk.

A) How to Solve it: A New Aspect of Mathematical Method (Penguin Science) https://www.amazon.co.uk/dp/0140124993/ref=cm_sw_r_cp_apa_i_jmYGDbXX07R9M

B) Mathematical Techniques: An Introduction for the Engineering, Physical, and Mathematical Sciences https://www.amazon.co.uk/dp/0199282013/ref=cm_sw_r_cp_apa_i_ZkYGDbWSX4JSP

These two.

Read Polya, then do the hard work of studying through the sections of Techniques you need to know.

Machine Learning: The New AI by Ethem Alpaydin is a non-technical introduction that gives a very high level overview of ML algorithms. You can skip the first part of the book which seems to be for people who don't know what computers and data are.

https://www.amazon.com/Artificial-Intelligence-Security-Chapman-Robotics/dp/0815369824

Check out Introduction to Machine Learning by Alpaydin.

https://www.amazon.com/Vector-Calculus-Linear-Algebra-Differential/dp/0136574467

Plus you get to learn differential forms!

Amazon links to books mentioned (no affiliate). Warning: A lot of high textbook prices here...look for eBooks and/or used copies of earlier versions:

Introduction to Mathematical Statistics (Hogg, McKean, & Craig)

All of Statistics (Wasserman)

Statistical Inference (Casella & Berger)

Pattern Recognition and Machine Learning (Bishop) (only reasonably priced as an eBook)

Hitchhiker's Guide to Python