Reddit reviews Python Machine Learning, 1st Edition

For deep learning reference this:
https://www.quora.com/What-are-some-good-books-papers-for-learning-deep-learning

There are a lot of open courses I watched on youtube regarding reinforcement learning, one from oxford, one from stanford and another from Brown. Here's a free intro book by Sutton, very well regarded:
https://webdocs.cs.ualberta.ca/~sutton/book/the-book.html

For general machine learning their course is pretty good, but I did also buy:
https://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka/dp/1783555130/ref=sr_1_1?ie=UTF8&qid=1467309005&sr=8-1&keywords=python+machine+learning

There were a lot of books I got into that weren't mentioned. Feel free to pm me for specifics. Cheers

Edit: If you want to get into reinforcement learning check out OpenAI's Gym package, and browse the submitted solutions

Hello, I was recommended to Coursera by a colleague and have taken an in-depth look at their course catalogue, but I have not taken any courses from them. If you think there are free courses on there that would suit your needs, then go for it, but personally I found that what was offered for free seemed too superficial and purchasable classes did not offer any information that I could not obtain elsewhere for cheaper.

I know a lot of people aren’t like this, but personally I prefer to teach myself. If you are interested in learning a bit about data science, I would strongly recommend Python Machine Learning by Sebastian Rashka. He explains everything in extreme clarity (a rarity in the academic world) and provides python code that permits you to directly implement any method taught in the book. Even if you don’t have interest in coding, Rashka’s fundamental descriptions of data science techniques are so transparent that he could probably teach these topics to infants. I read the first 90 pages for free on google books and was sold pretty quickly.

I’ll end with a shameless plug: a key concept in most data science and machine learning techniques use biased estimation (a.k.a., regularization), of which I have made a brief video explaining the fundamental concept and why it is useful in statistical procedures.

I hope my non-answer answer was somewhat useful to you.

Take the online course by Andrew Ng and then read Python Machine Learning.

If you then become really serious about Machine Learning, read, in this order,

1. Machine Learning: A Probabilistic Perspective
   
2. Probabilistic Graphical Models: Principles and Techniques
   
3. Deep Learning
   

Python Machine Learning. From the semester of machine learning I've done, you basically want to get comfortable with numpy and scikit learn.

I used your textbook to understand the theory behind the algorithms, but it'd be a waste of time (and potentially dangerous) to implement any non-trivial algorithm yourself. Especially since the sklearn python module has basically everything you would need (minus neural networks which you will find through Theano or TensorFlow).

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

Probably Python Machine Learning. It is a more applied than theory machine learning book while still giving an overview of the theory like ISLR :)

Well, I already had some basic programming skills from an introductory college course, but there are definitely online tutorials and exercises that can teach you that. I would recommend searching "introduction to python" and just picking a tutorial to work through (unless someone else has a more specific recommendation).

Python is one of the easiest languages to pick up, but it's extremely powerful. Knowing the basics, I just started trying to come up with fun, little projects I thought would be doable for me. Every time I ran into a component I wasn't sure how to do (or wasn't sure of the best way to do), I searched for the answers online (mostly at Stack Exchange). I later started looking through popular projects on Github to see good examples of proper application structure.

Each of my projects taught me a new skill that was crucial to building myself up to the point of true "software engineering," and they became increasingly more complicated:

1. I started out writing a simple script that would run through certain text files I was generating in my research and report some of the numbers to the console.
   
   
2. I wrote a script that would take a data file, plot the data on a graph, and then plot its 1st and 2nd derivatives.
   
   
3. I wrote a simple chemical database system with a text-prompt user interface because my Excel files were getting too complicated. This is where I really learned "object-oriented" programming.
   
   
4. I wanted to make the jump to graphical user interfaces, so I worked through tutorials on Qt and rewrote my database to work with Qt Designer.
   
   
5. I wrote some stock-tracking software, again starting from online tutorials.
   
   
6. I bought this book on neural networks and worked through the examples.
   
   
7. I wrote an application that can pull molecular structures from the Cambridge Crystal Structure Database and train a neural network on this data to determine atom coordination number.
   
   
8. For a work sample for a job I applied to, I wrote an application to perform the GSEA analysis on gene expression data. I really paid close attention to proper software structure on this one.
   
   
9. Just last week I wrote an application that interfaces with a computational chemistry software package to automate model generation and data analysis for my thesis.
   
   The important thing to remember about programming is there's always more to learn, and you just need to take it one step at a time. As you gain experience, you just get quicker at the whole process.

http://www.urbandictionary.com/define.php?term=laughing&defid=1568845 :))

Now, seriously, if you want to get started, I'd recommend this for R (http://www.amazon.com/Introduction-Statistical-Learning-Applications-Statistics/dp/1461471370/) and this for Python (http://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka/dp/1783555130//).

Also, head out to /r/datascience and /r/MachineLearning!

EDIT: Wrong link.

My top 3 are:

1. [Machine Learning] (https://www.cs.cmu.edu/~tom/mlbook.html) by Tom M. Mitchell. Ignore the publication date, the material is still relevant. A very good book.
   
2. [Python Machine Learning] (https://www.amazon.co.uk/dp/1783555130/ref=rdr_ext_sb_ti_sims_2) by Sebastian Raschka. The most valuable attribute of this book is that it is a good introduction to scikit-learn.
   
3. Using Multivariate Statistics by Barbara G. Tabachnick and Linda S. Fidell. Not a machine learning book per se, but a very good source on regression, ANOVA, PCA, LDA, etc.

As far as Python books, you should get these 2:
Python Data Science Handbook and Python Machine Learning.

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&ie=UTF8&qid=1487243060&sr=1-1&keywords=python+machine+learning

I am in probably same boat. Agree with your thoughts on github. I fell in love with this book: https://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka/dp/1783555130/ref=sr_1_1?ie=UTF8&qid=1474393986&sr=8-1&keywords=machine+learning+python

it's pretty much what you need - guidance through familar topics with great notebooks as example.

Take a look at seaborn package for visualization.

Might be worth looking at someone else's more in-depth explanation of these things to see modern uses. I just picked up this book, which gets into SciKit Learn for machine learning in like chapter 3 or something.

(Just an idea. I look forward to reading your tutorial if you ever post about it here!)

Just FYI, because this is not always made clear to people when talking about learning or transitioning to data science: this would be a massive undertaking for someone without a strong technical background.

You've got to learn some math, some statistics, how to write code, some machine learning, etc. Each of those is a big undertaking in itself. I am a person who is completely willing to spend 12 hours at a time sitting at a computer writing code... and it still took me a long time to learn how not to write awful code, to learn the tools around programming, etc.

I would strongly consider why you want to do this yourself rather than hire someone, and whether it's likely you'll be productive at this stuff in any reasonable time frame.

That said, if you still want to give this a try, I will answer your questions. For context: I am not (yet) employed as a data scientist. I am a mathematician who is in the process of leaving academia to become a data science in industry.


> Given the above, what do I begin learning to advance my role?

Learn to program in Python. (Python 3. Please do not start writing Python 2.) I wish I could recommend an introduction for you, but it's been a very long time since I learned Python.

Learn about Numpy and Scipy.

Learn some basic statistics. This book is acceptable. As you're reading the book, make sure you know how to calculate the various estimates and intervals and so on using Python (with Numpy and Scipy).

Learn some applied machine learning with Python, maybe from this book (which I've looked at some but not read thoroughly).

That will give you enough that it's possible you could do something useful. Ideally you would then go back and learn calculus and linear algebra and then learn about statistics and machine learning again from a more sophisticated perspective.

> What programming language do I start learning?

Learn Python. It's a general purpose programming language (so you can use it for lots of stuff other than data), it's easy to read, it's got lots of powerful data libraries for data, and a big community of data scientists use it.

> What are the benefits to learning the programming languages associated with so-called 'data science'? How does learning any of this specifically help me?

If you want a computer to help you analyze data, and someone else hasn't created a program that does exactly what you want, you have to tell the computer exactly what you want it to do. That's what a programming language is for. Generally the languages associated with data science are not magically suited for data science: they just happen to have developed communities around them that have written a lot of libraries that are helpful to data scientists (R could be seen as an exception, but IMO, it's not). Python is not intrinsically the perfect language for data science (frankly, as far as the language itself, I ambivalent about it), but people have written very useful Python libraries like Numpy and scikit-learn. And having a big community is also a real asset.

> What tools / platforms / etc can I get my hands on right now at a free or low cost that I can start tinkering with the huge data sets I have access to now? (i.e. code editors? no idea...)

Python along with libraries like Numpy, Pandas, scikit-learn, and Scipy. This stuff is free; there's probably nothing you should be paying for. You'll have to make your own decision regarding an editor. I use Emacs with evil-mode. This is probably not the right choice for you, but I don't know what would be.


> Without having to spend $20k on an entire graduate degree (I have way too much debt to go back to school. My best bet is to stay working and learn what I can), what paths or sequence of courses should I start taking? Links appreciated.

I personally don't know about courses because I don't like them. I like textbooks and doing things myself and talking to people.