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Machine learning communities

Web communities have limitless value within machine learning, regardless of your capabilities. The reason is that, like programming, you never cease learning. You just cannot know all there is to know, there are always fresh algorithms, new data and combos to find out and practice.  

Communities can be of great assistance. You can find solutions to your queries, learn by responding to the queries of other individuals and find out new spheres from reading through the discourse in the communities. 

Machine learning communities have had a major influence on the education of machine learning engineers and data scientists, and in this blog article by AICorespot, we wish to detail all of the online machine learning communities we are aware of so that you can obtain value from them. 

Stack Exchange 

The stack exchange sites are Q&A communities, so they are intended to identify solutions to problems. You can upload the particular questions you have, provide solutions to queries to which you know the solution, and read questions and solutions to find out new strategies and viewpoints. 

There are four major websites we would like to highlight: 

  • Cross validated: This site is good for low-level queries on algorithms and statistical strategies. 
  • Quantitative finance: Particularly the machine learning tag. This site is good if you are working in the financial field, but typically if you are operating with time series data. 
  • Programmers: Great for particular code questions, like a problem with a provided library or utility you are leveraging. 
  • Stack overflow: Again, like programmers, great for particular questions with the implementation side of machine learning. It’s also the oldest website and can encapsulate machine learning algorithms and libraries. 


Reddit is a community of sub-communities referred to as sub-reddits. A provided subreddit can be a Q&A site, a link sharing website or more conventionally, a mixture of the duo. 

Some sub-reddits that come recommended include: 

  • Machine learning: Consists of a mixture of “how do I begin” and more sophisticated links to machine learning blog posts. Also good for linkage to your own projects to obtain some feedback. 
  • Computer Vision: Primarily questions on computer vision both theoretical and practical (like libraries). 
  • Natural language: Concentrate on natural language processing, furnishing a good mixture of questions and links to appropriate articles and blog articles. 
  • Statistics: Discussion on statistical software and strategies, ideal for delving deeper into a provided method or algorithm. 
  • Data Science: Primarily links to posts that straddle data analysis and machine learning. 
  • Big Data: Concentrated posts and discussions on the big data ecosystem. 

There are other sub-reddits available on appropriate and connected subjects, but we have not found them as useful.  


Quora is a Q&A site that is divided into subjects, a lot like Reddit but just questions and answers. The questions are usually good and the solutions are of high quality. Not like the stack exchange sites, they are usually of reduced technicality, less problem concentrated and more meaty. 

A few Quora subjects that come recommended include: 

  • Machine learning: Useful for high-level queries on algorithms, processes, resources and just beginning. A good combo. 
  • Statistics: Concentrate on in-depth statistical strategies and algorithms, but consists of a ton of useful machine learning content. 
  • Data mining: Good questions with a concentration on the applied side of machine learning, but there is a ton of overlap with machine learning. 
  • Data Science: A lot like the Data Mining and Machine learning subjects, the questions are usually of a higher level. 

There are several other subjects that might prove to be useful, not restricted to Data Analysis, Predictive Analytics, NLP, and Computer Vision. Also there are subjects on particular strategies like SVM, Deep Learning, Classification, and R.  


There are a few other amazing communities that we could not categorize as easily. 

  • MetaOptimize Q+A: Like Cross Validated, this is a Q&A site that is amazing for lower level queries on particular algorithms and strategies. Mathematics and theory heavy. 
  • Kaggle Forums: Good for discourse regarding particular competitions and datasets, and complete of great nuggets of advice for feature engineering, ensembling and refining your test harnesses. 
  • DataTau: A social news website with a concentration on links to posts on data and machine learning connected topics. Reduced traffic and useful links. 


A few social media websites have machine learning groups. We don’t leverage these as much, but we mention them as you might find them useful. 

LinkedIn is also a gold mine for data, which might prove to be of interest, particularly, Data Mining and Machine Learning and the Machine Learning Connection. Again, there are several LinkedIn groups for a provided area without obvious leaders. 

In Person 

Lastly, consider communities in meat space. Take a peek at a website such as Meet Up and search for meet-ups in your region on topics such as machine learning, R, data analytics, and data science R user groups are usually an ideal place to learn and connect with pros. 

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