Machine Learning Ressources for beginners (and beyond)
Hello everyone, this post is a compilation of useful ressources for those who want to dig into Machine Learning techniques. Enjoy !
To get in the field of Machine Learning and Data Science as a whole, you first need to get solid basics in mathematics (linear algebra, statistics/probabilities and mathematical optimization are at play here).
I ) Notions in Mathematics
To get notions in probabilities and statistics, I highly recommend these two courses !
- Stanford University : Probabilities and Statistics
- Stanford University : Statistical Learning (refers to the excellent Introduction to Statistical Learning book).
I refer you to this article which reprises what to learn in math for data science : Learn Math for Data Science
In fact, you could easily begin you learning with the courses referenced thereafter which go into the heart of the matter, without having compiled all the previous ressources, because the courses are adressed to beginners and little refreshers as for the mathematical aspects will be given. I therefore recommend you the following courses, if you have some mathematical background, and to refer to the previous contents if you need to freshen up your memory.
As for deep learning, if you want to directly begin with this particular field of techniques without heading into Machine Learning or Data Science, you can get on by with knowledge in linear algebra and optimization.
II ) Online courses ( MOOC )
- Datacamp : Introduction to Data Science with Python : This course is a nice introduction to Data Science with Python coding language. It would be really useful for you to get a good grasp of Python as well as R languages which are used plenty in data science and machine learning.
The two main courses I recommend you are Andrew Ng’s (AI researcher and former professor at Stanford (he has, amongst other things, worked for Google and Baidu in this field) coursera MOOCS ! Andrew is really pedagogue in his teaching and his courses are a really great asset in anyone’s learning path.
PS : You can access the course contents for free by clicking on free attendance when you subscribe to each course individually.
Great news ! New AI learning platform
Google has recently launched a AI learning platform : Google AI Platform (friendly visualisations are involved to understand math concepts).
III) Data Communities
Nothing is better than doing projects to learn data science, I recommend you Kaggle projects (it’s a web platform designed to host competitions dealing with data science. Companies can host competitions while offering prizes for the winners), try out the “knowledge competition” first, which are meant for learning purposes :
- Python tutorial on ML : A tutorial about Titanic dataset from Kaggle.
- « Getting started » competitons on Kaggle.
- A really great free mean to learn how to buils ML models..
Meetups (Meetup.com) are a great way to participate to data science conferences and enter the community. There also are many Facebook groups or even Twitter or Quora feeds which deal with discussions revolving around data science.
IV) Other Ressources
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A great repository by Shujian Lu with categorized ressources : https://github.com/Shujian2015/FreeML
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a data science stackoverflow : https://www.kdnuggets.com/
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great content (again) : https://www.analyticsvidhya.com/
I hope this post was useful to you, many ressources are available on the internet, go on and do some data projects yourself ! :)
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