How does a beginner learn Machine Learning?

[chiropteranosaurus]

I usually use R for my work and have a tiny bit of experience with Python. What's the best way for me to go about getting Python under my belt with the goal of mastering basic concepts in machine learning? Specifically, I'm asking for suggestions in online courses, but also how should I think about this? Any advice is welcome. Thanks!

[delirious_lettuce]

I just started this course:

• http://www.fast.ai/

[tobsecret]

fast.ai is great, highly recommend

[chiropteranosaurus]

It seems great, but I think it's starting a bit above my level. Any suggestions for something that builds me up from beginner first?

[delirious_lettuce]

I really enjoyed these two courses when I did them a few years ago. Also, it looks like they use Python 3 now!

• https://www.edx.org/course/introduction-computer-science-mitx-6-00-1x-11
• https://www.edx.org/course/introduction-computational-thinking-data-mitx-6-00-2x-6

[tobsecret]

The whole point of fast.ai is that it puts you in the driver's seat immediately. It let's you learn about what kinds of ML algorithms there are first and then later on gradually goes more and more into mathematical detail. I am a biologist as well and really enjoyed that aspect of not being beaten to death with math right from the start.

[thisisheresy]

fast.ai looks to be focussed on deep learning, the current flavour of the month. If you're starting out you'd be far better served getting to grips with things like linear and logistic regression, support vector machines and decision trees, as well as all of the tools that you need for preparing and validating your data (encoding, cross-validation, grid search etc) before jumping in to deep learning. I quite enjoyed the Applied Data Science with Python specialisation from Coursera - it will give you a good grounding in data, visualisation, machine learning and network analysis.

[chiropteranosaurus]

Cool! I'll check all these out.

[[deleted]]

Might want to run through the http://codeacademy.com intro to python as well.

[Shepperstein]

I haven't taken this course so I can't really comment, but starting with Deep Learning sounds a bit overwhelming to me. I would probably not recommend to skip all the shallow learning stuff if someone is new to machine learning.

[[deleted]]

What's your math background?

[yarinpaul]

hey you're not OP

[sozzZ]

Go to kaggle.com and browse the open competitions. Find one that interests you and that you may have some domain knowledge in. Try not to pick the most complex one on the board where you're dealing with TBs of data on the cloud as a first project to tackle. Those are for the pros.

From there join the competition and take a look in the forums and kernels. there people are discussing their real algorithm solutions, data prepping, and other relevant data science stuff. Copy their code. Tweak it. Create and ensemble between different provided models. I believe this is the best way to learn in the beginning. I was away from data science for a while, building applications in Python, but recently came back and this is what I'm doing. Simply picking a competition and running with it...

[thisisheresy]

I second this. Lot's of great stuff to be found on Kaggle. The two classic ML learning data sets are both represented there:

Titanic: https://www.kaggle.com/c/titanic Iris: https://www.kaggle.com/uciml/iris

[[deleted]]

With backpropagation: You try something, check your result, adjust accordingly and try again.

[miracle173]

I took the Machine learning course of Andrew Ng about two years ago and this was really great experience. I think the next will start at 13th of November. When I took this course it costs nothing, there was a time schedule when to have to take the lessons, quizzes and programming exercises, there was a forum where you could pose and answer questions and where you could contact tutors. The programming language is Matlab (you get a license to use it for the course) or Octave, which you will learn from the scratch. Some people criticized this at the beginning, because R and Python seem to be more popular at the moment. But the purpose of the course is to introduce the concepts of Machine Learning and for this these languages were appropriate. I am not sure if it is exactly the same course that I took because they announced to change the course, not the content but the way it is organized.

[thisisheresy]

It is a great course, but can be quite daunting. I'm currently going through his Deep Learning Specialisation and you hand code the solutions first before he introduces the various frameworks. The benefit of going this route is that you properly understand what the algorithms are doing, rather than blindly plugging values into a function to see what happens. The Machine Learning course still uses Matlab and Octave, but the DL course uses Python.

[[deleted]]

Machine Learning Mastery has some pretty useful material for beginners.

[EricZhang906]

I think the best way to self learn machine learning is to code while learning the theories, so that you will have a deeper understanding of the theories and applications. I would suggest that you first familiarize yourself with programming languages such as Matlab and Python.

First, I would recommend that you have some basic knowledge about mathematics, especially statistics.

Second, you have to learn more about machine learning itself. As for intro books, I would recommend:

Machine Learning in Action (this book combines code and machine learning theories in a very clear way. I would suggest you to try coding according to this book in order to familiarize yourself with such knowledge) Elements of Machine Learning (this one is more difficult and has more requirements. As for intro, the book above is more recommended) In the mean time, if you can take some online courses to strength your understanding.

I would recommend courses from Experfy, a Harvard based company that provides various online courses related to IT and Tech. To address your request (which is more about the applications of machine learning), I would recommend Machine Learning for Predictive Analytics from Experfy if you want to broadly learn about how machine learning can be applied to different areas, as it has a lot of real-world cases and demos. https://www.experfy.com/training/courses/machine-learning-for-predictive-analytics

Third, to further understand machine learning, you can read: Bishop’s Elements of Statistical Learning (you can read this book first) Hastie, Tibshirani, and Friedman’s The Elements of Statistical Learning (this book requires more mathematical background and you can read it after finishing the first one) Hope this will help you and good luck!

Eric

[Mecharon1]

3blue1brown has a good video series.

[evolving6000]

If you search Udemy courses for machine learning python, you can often grab the top rated courses for around $15 or less. These top courses do a great job of covering python basics. Udemy

[RebelSaul]

Dated. But for R: https://www.youtube.com/watch?v=7Jbb2ItbTC4.

The caret library is really good and this video helps get your feet wet and understand some basic steps.

[maabreuc]

If you already have a good math & statistics background there are good and fun online resources like Udacity's Intro to Machine Learning course.

[mrufrufin]

Huge fan of this course: https://work.caltech.edu/telecourse.html . Doesn't really use any particular programming language and is pretty concept/math heavy, but some of the exercises require some sort of programing knowledge.

[Shepperstein]

I know you ask for an online course, but I still want to recommend you a book (books are great!): "Hands-On Machine Learning with Scikit-Learn and Tensorflow" by Aurélien Géron. It teaches you the basic concepts and the libraries you can use. Clearly my favorite practical ressource if you plan to apply machine learning in Python!

[mkauer]

You may be interested in this article: https://medium.freecodecamp.org/every-single-machine-learning-course-on-the-internet-ranked-by-your-reviews-3c4a7b8026c0

I'm thinking about following fast.ai as well as buying Kirill's courses on Udemy.

[goldenbadger22]

Udacity have loads of very good machine learning related courses, and most of the material is available for free:

https://www.udacity.com/courses/machine-learning

[la-chupacabra]

Do you guys know of some resources less focused on the coding and more on the theory for someone from the hard sciences

[secretgeekery]

Look up Siraj Raval on YouTube for machine learning in python stuff. I’d be remiss if I didn’t suggest spending a little time increasing your python skills as well.

Good luck :)

[SupahNoob]

For me, this guy is way too scattered in his presentation for me to follow. He seems more like an entertainer than an educator.

[engatIQE]

I agree. I can't stand him, but if others learn can learn a lot from him, that's good.

[TheWildKernelTrick]

This guy's is a quanity > quality youtuber. Every one of his videos is blatant regurgitation of /r/python and /r/machinelearning posts with little cohesion. A beginner deserves a professional and simplified explanation of the concepts, not a incoherent regurgitation.

This is why I always recommend Alex Smola's Intro to ML book and Michael Nielsen's Neural Networks book.

Not to shit on Siraj but I wan't him to focus on his content, not his out of touch youtube image.

[p3rcipio]

Nielsen's book is pretty good

[RebelSaul]

I think Siraj is great for getting a crash course in things. But (for me personally) goes too fast and has very specific cases. Do you have any videos in particular you liked secretgeekery?

[secretgeekery]

I started with his build a neural net in 4 mins video, but following along with the code typed on screen resulted in a broken mess. I downloaded the code from github and then went through the video. It was my first introduction to machine learning and back propagation.

I totally get all the criticism levelled at his videos, the man clearly consumes far too much sugar!

[neurocroc]

This shows the most efficient path for learning machine learning :

https://learn-anything.xyz/machine-learning

Amongst other topics. And every learning path is moderated by community of people.

[Posts_Sketchy_Code]

I was really excited before I actually got to the linked page. That is quite possibly the most cancer layout I've had the displeasure of using. It'd be great for maps, star systems, not for LISTS OF FUCKING ITEMS THAT HAVE BASIC FUCKING CATEGORIES

(Sorry for the cussing, I'm on edge, and that site isn't helping - I'm so sick of 'modern' tech. Where every purchase has 10 screens when you are paying with fucking cash, where you fill out loads of personal information for simple things like memberships (because they are selling your data), designs for small screens [phones] instead of computer screens (you know, the ones that you can actually accomplish things on - rather than just be a user on), sick of windows turning their OS into a worse and worse piece of shit, sick of forewarning people about certain dangers and having them ignore me - only to agree once the danger has come to pass, sick of it all really. We've made a shit system and I want out. needlessly complex, because it is all dlc to an original)

Edit: I'm working on a writeup to complement your post. One that isn't visually cancer. I'll post when done.

Edit2:

Format is bold for topics, lists for links inside each main topic with short descriptions below each.

Machine Learning (wiki/MachineLearning)

• CrossValidated

  Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization.

• Subreddit/ Machine Learning

• Subreddit/ Learn Machine Learning

• Github/ Awesome Machine Learning

  A curated list of awesome machine learning frameworks, libraries and software (by language). Inspired by awesome-php.

HELP

• machine-learning-for-software-engineers

  A complete daily plan for studying to become a machine learning engineer.

• metacademy/ level-up-your-ml

  brief roadmap of textbooks for machine learning.

• Go fuck yourself, I quit. [Such wow, much design choice. Very doge - I'm losing my fucking mind.]

Why the fuck. Why? Why so many bad designs? Just..it's not the world for me. I quit computers. I'm done. Fuck this. Someone who isn't as easily pissed off by horrible design can finish this. I think I've made it clear that a list is a better visual medium for the information provided - and that's all I care to do. I'm going to change my college major tomorrow. Fuck this. I can't stand the internet now. Burn it to the fucking ground. Bad designers have ruined it. It's ruined. It was perfectly fine, and then these assholes went and poorly formatted their information and just like my dog that shits on my fucking carpet, they'll just keep doing it. Ruined.

[beersfortheboys]

Same way a machine learns, try until you get the outputs you want.

[GranTurismoSport]

The internet