# Top products from r/learnmachinelearning

We found 57 product mentions on r/learnmachinelearning. We ranked the 91 resulting products by number of redditors who mentioned them. Here are the top 20.

### 1. Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Sentiment score: 7

Number of reviews: 9

O Reilly Media

### 2. Pattern Recognition and Machine Learning (Information Science and Statistics)

Sentiment score: 2

Number of reviews: 5

Springer

### 4. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Sentiment score: 2

Number of reviews: 4

### 5. All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics)

Sentiment score: 1

Number of reviews: 2

Springer

### 6. All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics)

Sentiment score: 3

Number of reviews: 2

### 8. Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning series)

Sentiment score: 2

Number of reviews: 2

Bradford Book

### 10. Mathematical Statistics with Applications

Sentiment score: 2

Number of reviews: 2

### 11. Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)

Sentiment score: 2

Number of reviews: 2

Mit Press

### 12. Python Machine Learning, 1st Edition

Sentiment score: 1

Number of reviews: 2

Python Machine Learning

### 13. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

Sentiment score: 2

Number of reviews: 2

This refurbished product is tested and certified to work properly. The product will have minor blemishes and/or light scratches. The refurbishing process includes functionality testing, basic cleaning, inspection, and repackaging. The product ships with all relevant accessories, and may arrive in a ...

### 14. Introduction to Algorithms, 3rd Edition (The MIT Press)

Sentiment score: 1

Number of reviews: 2

Hard Cover

### 15. Bioinformatics: The Machine Learning Approach, Second Edition (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning series)

Sentiment score: 1

Number of reviews: 1

### 16. Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (The MIT Press)

Sentiment score: 1

Number of reviews: 1

The MIT Press

### 17. Introduction to Machine Learning (Adaptive Computation and Machine Learning series)

Sentiment score: 0

Number of reviews: 1

MIT Press MA

### 18. How to Solve It: A New Aspect of Mathematical Method (Penguin Science)

Sentiment score: 0

Number of reviews: 1

PENGUIN GROUP

math is a funny thing... our culture gets so hung up on 'good at' and 'bad at', but the more I get into neurobiology and ML, the more amazing our general learning abilities seems to be. My partner and I are radically different, she's better at chess than I am in spite of having a poor ability with 'traditional' chess thinking, she relies almost entirely on pattern recognition, so she has to stand over the board looking down so her brain can feed up ideas from the books she's read (since chess layouts are always shown in those books from the top down).

All this is to say... there's a goddamn giant mountain in front of you, and it's easy to think that you're 'bad' at it because of where you're starting, or even because of base talents and interests that might not seem to line up with math at first glance. Just wanted to start out by saying that's horse shit. You're also 'bad' at judo and chinese (presumably), but given a few years of regular practice, you could get those reasonably under your belt as well. Math is a way of thinking and looking at problems, and it's incredibly helpful. It's kind of mind blowing the doors it can open... information theory, statistics, linear algebra, calculus, game theory, graph theory, group theory, representation theory, category theory... every branch opens up mind blowing new insights, tools, and models for looking at new problems. Don't look at it like this 'thing' you have to learn though. You can't learn all of math. You can just slowly learn new tools, get better at understanding what it even 'means' to learn one of those new fields, and how to organize your study to make real progress as you're slowly getting deeper.

So... my recommendation for where to start? Start with the meta learning. What is math? How can you learn it? How should you study? The best glimpse into those questions I've found is how to think about analysis. It takes a complete beginner's perspective (explaining how to read the standard math notation even... the summation symbol, epsilon, etc) slowly builds up an introduction to the guts of what calculus is, basically. You can read it in a week or two, so it's not a huge time investment, and it'll do a lot I think to arm you for the road ahead.

I'm personally a fan of bottom up learning as much as possible, but that's just because I hat trying to play with half a deck. There's plenty of people though that just treat pieces they're working with like 'black boxes'. You can use a decision tree without any fucking clue about information theory, or even what the decision surface actually looks like for the resulting tree. Finding good visualizations when wrapping your head around that stuff can be really helpful... so if you're struggling with one resource, don't be afraid to look for another. Sometimes a git article with some good graphs can make all the difference.

I don't know what road is best for you, but the only barrier in front of you is your patience, and your willingness to spend time every single week, and turn this into a practice instead of just a hobby. I started a year and a half ago after ten years in an unrelated industry, and while I still have a long way to go, I've also covered a ton of ground too. I'd never even had stats before at all, even in high school... now I'm comfortably following some pretty gnarly multivariate derivations in Bishop's pattern recognition and machine learning. You just keep putting one foot in front of the other, pay attention to your goal, follow your curiosity, and before you know it... people start looking at you funny, because you know things most people don't know, and you can build things most people don't even understand. I can't imagine a more exciting thing to be learning, especially at this time in history. If you have the patience and interest for it, whichever road you take I think you'll find it well worth your time.

My own personal suggestion by the way... take a little time for fundamentals on the regular (starting with linear algebra, a proper textbook with a lot of exercises if possible) and practical (actually implementing stuff, doing Kaggle competitions, whatever). Eventually in the distant future, you'll meet in the middle, and find you have the insight to start pursuing your own questions... possibly even questions no one has ever solved before, and you'll have an enormous amount of practical good to bring to whatever field you've been working in, if you choose to continue there. Good luck!

I believe so.** As u/LanXlot said, Google Colaboratory is free to use for research and learning. Also, you can sign up to use better machines with Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform. All three cloud services allow you to access a virtual machine with all the processing power you'd ever want for a small fee. While taking an NLP class I used AWS to run huge programs for less than $5/hour. I would write most of my program locally with a commented section to enable a GPU when I was ready to run it on the virtual machine.

I can also tell you Amazon has a free tier that was better than my computer for most projects when I started the course and I used it as often as I needed to as well. There was about a 10 hour learning curve to get everything running easily, but overall it was a fun experience.

Best of luck!

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**EDIT: I believe it is worth experimenting with deep learning regardless of what computing ability you have at home.

It may be worth your time to purchase Deep Learning with Python if you want to learn the basic concepts of deep learning from a programmatic, practical perspective. Another good book to start with may be Hands-on Machine Learning with Scikit Learn, Tensorflow, an Keras. There is more to AI and machine learning than just deep learning, and basic machine learning techniques may be useful and fun for you.

I personally really benefitted from Jose Portilla's udemy class on python for Data Science: https://www.udemy.com/python-for-data-science-and-machine-learning-bootcamp. It deals with the machine learning algorithms at a pretty basic level but he does a good job overviewing things and this course personally gave me more confidence. He also wrote a helpful overview for how to become a data scientist: https://medium.com/@josemarcialportilla/how-to-become-a-data-scientist-2d829fa33aba

Additionally, I found this podcast episode from Chris Albon helpful: http://partiallyderivative.com/podcast/2017/03/28/learning-machine-learning

Finally, I have just started going through Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems and I love it. It's very easy to read and applicable: https://www.amazon.com/dp/1491962291/_encoding=UTF8?coliid=I1VIM81L3W5JUY&amp;colid=2MMQRCAEOFBAX

Hope this helps.

It sounds like you have identified your weakness. Presently, that is programming in python, and using the sklearn library.

I would recommend taking a MOOC on python first. Lynda.com has a free trial and python videos. datacamp is another good start. It has a free trial and mayybe some python basics, but definately something on sklearn. and you can get some pandas training or R training there. (the kaggle libraries, most likely).

At that point, if you are going the tensorflow route, Aurelion has a great hands-on book called Learning Tensorflow with sci-kit learn

If you’re going with pyTorch I dunno.

Your mileage is going to vary, you could always use a book to learn python, or whatever.

Just make sure you learn to program first, you’d be surprised how much 2 weeks of very hard work will earn you. Don’t expect it to be ‘easy’ ever tho.

Also, if you’re not formally educated in statisics, keep an eye out for statistics advice until you have the time to work on it. (like in a MOOC, course, or blog). Learning some real analysis will make understanding the papers a real possibility (once again it will probably never be easy)

It is truly stunning how many years of preparation it takes to become competent in this. It’s a lovely science, but the competent ones have generally been on a mathematical/science track since 5th grade. Doesn’t mean we can’t become competent but it takes time. Imagine the equivalent of an undergraduate degree just devoted to ML and you’re about there.

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

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.

If you're worried about not doing projects and participating in Kaggle competitions, why not do those things? They're pretty low risk and high reward. If you're feeling shaky on the theory, read papers and reference textbooks, take notes, and implement things that interest you. For deep learning stuff there are some good resources here: https://github.com/ChristosChristofidis/awesome-deep-learning. For more traditional methods you can't go wrong with Chris Bishop's book (try googling it for a cheaper alternative to Amazon ;): https://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738.

Side projects can really help here, and the key is to use references, but don't just copy-paste. Think of something you'd like to apply machine learning to with a reasonable scope. Search google scholar/arxiv for papers that do this or something similar, read them, and learn the techniques. For reading research papers in an area where you're not extremely knowledgeable, use the references in the text or google things you don't know and make sure you understand so you could teach someone else. If you're interested in the topic and exhausted the references, go up the tree and use google scholar to find papers that list the one you're reading as a reference - you usually find interesting applications or improvements on the technique. You can also often find open source training data in the appendices of papers. Kaggle also has a ton of datasets, including obviously the ones they provide for competitions.

I agree this is a missing area. I've been working on some materials like recent videos on Transfer Learning https://studio.youtube.com/video/vbhEnEbj3JM/edit and One Shot learning https://www.youtube.com/watch?v=H4MPIWX6ftE which might be interesting to you. I'd be interested in your feedback. I also think books like https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291/ref=pd_lpo_sbs_14_t_1?_encoding=UTF8&psc=1&refRID=3829RHN356ZXBEBP0KF3 do a good job of bridging some of this gap. Reading conference papers is a skill that takes practice and a strong math background.

After those you may read the great linear algebra book by Gilbert Stang (he is a great teacher and writer, one of the best math books I've seen): http://math.mit.edu/linearalgebra/. MIT also shared video lectures for this course: https://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/index.htm

And for a next step in statistics I suggest reading All of Statistics: https://www.amazon.com/All-Statistics-Statistical-Inference-Springer/dp/0387402721

Also, first chapters of the Deep Learning Book contain good overview on mathematics needed for ML

I hadn't heard of Lie Groups as well (and didn't look it up the first time you mentioned them) - they sound amazing!

Right now I'm mainly reading the Murphy Book after having finished Probabilistic Models of Cognition (which I enjoyed because I also always wanted to check out Scheme and has some great interactivity).

I suppose I'll have to put these books on the list, thanks! ;)

I'm very new to ML myself (so take this with a grain of salt) but I'd recommend checking out Make Your Own Neural Network, which guides you through the process of building a 2-layer net from scratch using Python and numpy.

That will help you build an intuition for how neural networks are structured, how the forward / backward passes work, etc.

Then, I'd probably recommend checking out Stanford's online course notes / assignments for CS231n. The assignments guide you through building a computation graph, which is a more flexible, powerful way of approaching neural network architectures (it's the same concept behind Tensorflow, Torch, etc.)

I would mention Bishop's Pattern Recognition and Machine Learning (https://www.amazon.fr/Pattern-Recognition-Machine-Learning-Christopher/dp/1493938436) as well as Hastie's Elements of Statistical Learning (https://www.amazon.fr/Elements-Statistical-Learning-Inference-Prediction/dp/0387848576/).

Sure they're not that easy to delve into, but they'll give you a very strong mathematical point of view,

good luck !

Yes. I'm a developer and I started learning ML through fastai's course "Practical Deep Learning for Coders, v2". It was an excellent introduction. It does presume you have previous programming experience, however.

I also really benefitted from Francoise Chollet's book. Link One of the big barriers to learning this stuff is that it tends to be taught by mathematicians. I'm not a mathematician so if you show me the backprop algo as a formula I just glaze over. Francoise Chollet explains it all as a set of python functions, which is very readable. If you are more mathematically inclined, you might not like that approach.

So much of data science is going to involve programming, theres really no way to avoid learning it first. You have to clean your data, move variables around, join tables and whatnot. Step 1 is definitely learn python. Fastai is a good step 2.

Hi,

Not trying to sell anything here, but I've been reading this book : https://www.amazon.ca/Aur%C3%A9lien-G%C3%A9ron/dp/1491962291/ref=mp_s_a_1_1?keywords=machine+learning&amp;qid=1568912246&amp;sprefix=machibe+l&amp;sr=8-1

I think it is a pretty complete book that covers a lot and a good first step into ML.

Plus there are plenty of examples using Python.

GL

When I started on the field I took the famous course on Coursera by Andrew Ng. It helped to grasp the major concepts in (classical) ML, though it really lacked on mathematical profundity (truth be told, it was not really meant for that).

That said, I took a course on edX, which covered things in a little more depth. As I was getting deeper into the theory, things became more clear. I have also read some books, such as,

All these books have their own approach to Machine Learning, and particularly I think it is important that you have a good understanding on Machine Learning, and its impacts on various fields (signal processing, for instance) before jumping into Deep Learning. Before almost three years of major dedication in studying the field, I feel like I can walk a little by myself.

Now, as a begginer in Deep Learning, things are a little bit different. I would like to make a few points:

So, to summarize, you need to start with simple, boring things until you can be an independent user of ML methods. THEN you can think about state-of-the-art problems to solve with cutting-edge frameworks and APIs.

I've wasted too much time trying to find the so-called "right" statistics book. I'm still early in my journey, going through calculus using Prof. Leonards videos while working through a Linear Algebra book all in prep for tackling a stats book. Here's a list of books that I've had a look at so far.

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These seem to be of a similar level with regards to rigour, as they aren't that rigourous. That's not to say you can get by without the calculus prereq and even linear algebra

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The other two I've been looking at which seem to be a lot more complex are

And then there's Casella and Berger's Statistical inference, which I looked at once and decided not to look at again until I can manage at least one of the aforementioned books. I think I'm leaning most to the first book listed. Whichever one you decide to use, good luck with your journey.

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Yes, this tutorail is very useful for scikit learner, before watch the videos, I would like to recommend the book Python machine learning first! https://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka/dp/1783555130/ref=sr_1_1?s=books&amp;ie=UTF8&amp;qid=1487243060&amp;sr=1-1&amp;keywords=python+machine+learning

I didn't give this a try. If you are looking for practical applications, you can check "Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems". I heard good reviews about this book

https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291/ref=sr_1_1?ie=UTF8&amp;qid=1509062586&amp;sr=8-1&amp;keywords=hands-on+machine+learning+with+scikit-learn+and+tensorflow

It is there in safaribooks. You can use the free trial to see the contents (https://www.safaribooksonline.com/library/view/hands-on-machine-learning/9781491962282/)

If you already know Python and are familiar with the numpy, pandas, matplotlib and jupyter notebooks, then this book does a great job of teaching basic Machine Learning and more advanced Deep Learning concepts.

Hands on Machine Learning with Scikit learn and Tensorflow by Aurelion Geron.

https://www.amazon.in/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291

One caveat though - Tensorflow has undergone a lot of changes since this book came out, so you might have to tweak the code a bit.

Github Repo for the code - https://github.com/ageron/handson-ml

The book that I followed and I think it's pretty standard is

https://www.amazon.com/Reinforcement-Learning-Introduction-Adaptive-Computation/dp/0262193981

Curated lists available on Github

https://github.com/muupan/deep-reinforcement-learning-papers

https://github.com/aikorea/awesome-rl

The deepmind website

https://deepmind.com/blog/deep-reinforcement-learning/

The above content is what I am familiar with. Perhaps there are better resources others can point toward.

Since you are already going to take Machine Learning and want to build a good statistical foundation, I highly recommend Mathematical Statistics with Applications by Wackerly et al.

You can check out sites like HackerRank. It has problems from the book,

CrackingTheCodingInterview

I like and recommend this book as well,

Introduction to Algorithms

I head that the newer Machine Learning: A Probabilistic Perspective is equally good, and from the small amount I've read so far I'd agree.

tariq rasheed has a great book on ML and he breaks it down for total beginners. he breaks down the math as if your in elementry school. I think its called ML for beginners.

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Book link:

https://www.amazon.com/Make-Your-Own-Neural-Network-ebook/dp/B01EER4Z4G/ref=sr_1_1?crid=3H9PBLPVUWBQ4&keywords=tariq+rashid&qid=1565319943&s=gateway&sprefix=tariq+ra%2Caps%2C142&sr=8-1

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I got the kindle edition bc im broke. Its just as good as the actual book.

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&amp;qid=1499437032&amp;sr=8-2&amp;keywords=machine+learning+mit

Worth having in my opinion!

Hands-On Machine Learning . The 2019 version. It’s one of the best ML books I have come across.

In addition to the 3blue1brown video someone else described this book is a great introduction to the algorithms, without going into much math (though you should go into the math to fully undestand what is going on).

Make Your Own Neural Network

https://www.amazon.com/dp/B01EER4Z4G/ref=cm_sw_em_r_mt_dp_U_NkqpDbM5J6QBG

These both seem like okay books. I would actually recommend reading CLRS introduction to algorithms Introduction to Algorithms, 3rd Edition (The MIT Press) https://www.amazon.com/dp/0262033844/ref=cm_sw_r_cp_api_i_zme3DbCFYP00F

This teaches you algorithms that are less specific to a particular language. But if you really want to focus on python I think the books you picked are okay but won’t Supplement the math background.

https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow-dp-1492032646/dp/1492032646/ref=mt_paperback?_encoding=UTF8&amp;me=&amp;qid=1568461722

The kindle edition and ebooks are out but it looks like the paperback isn't out quite yet.

Hands-On Machine Learning is pretty much a staple to start out with.

Pretty standard:

https://smile.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738/ref=sr_1_1?ie=UTF8&amp;qid=1524692863&amp;sr=8-1&amp;keywords=prml

yes and no. For beginners, I recommend https://www.amazon.de/Deep-Learning-Python-Francois-Chollet/dp/1617294438 and https://www.reddit.com/r/learnmachinelearning/comments/bmj2zh/get_a_free_early_release_copy_of_handson_machine/

I am sure this is the book you're referring to https://www.amazon.ca/Think-About-Analysis-Lara-Alcock/dp/0198723539

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.

Do you mean this one? The new content is Keras, not PyTorch.

Check out Introduction to Machine Learning by Alpaydin.

Try this book: https://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka/dp/1783555130

Bishops book includes answers to selected exercises

There are tons of them. For example: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291/

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

Does it necessarily have to be a video course? If not, then I wholeheartedly recommend this book.