Staying up to date in AI, Machine Learning and Data Mining [closed] - machine-learning

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I'm currently finishing my Master and I probably won't be joining the academic ranks. I really like Machine Learning, Data Mining, AI in general.
How can I stay up to date with all the new research? Should I subscribe to a Journal? Maybe IEEE or ACM? I don't mind reading papers at all, but it seems like a lot of published papers are accessible only in academia.
How do you stay up to date in this fields?

Besides a rich bank of free papers on the web there are a lot of website providing the state-of-the-art artificial intelligence courses which are fun to take and learn such as MIT open courseware and moocs such as coursera which is the most fun and enjoyable resourse in the web.

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What kind of algorithm to use [closed]

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For a course term project we have to build a machine learning algorithm in which user fills the form and the algorithm analyse the best suitable university based on the responses. I am new in the field of machine learning and I do not know what kind of algorithm can we use. Is the recommendation systems a right approach for this?
I did some review on the internet for some similar projects, however still can not find a good resource.

What are the limitations of Octave compared to Python for Machine Learning? [closed]

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Is Octave good to learn for Machine Learning?
Or Python and other libraries would do?
Depends what you want to do.
Octave is excellent for fast prototyping and learning. The language is simple and you can focus on grasping the concepts of ML. On the other side, Python is very powerful and has unparalleled stack of libraries and frameworks that give you the ability to dive into machine learning on the level that you are comfortable with. And it's also a simple language in which you can get comfortable pretty fast.
If you just want to play with machine learning a little, I would recommend Octave as it's simple and straightforward. In all other cases, I would recommend Python as it's a powerful language for building complete systems and has a large community which can help you with any problem you could possibly encounter.

predicting customers buying preference using Machine Learning [closed]

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I have set of transactions from a retail store. If I need to cluster the customers based on buying pattern I can do that using K-Means algorithm in Python.
How can I predict based on their earlier buying pattern, what are all products customers would be interested to buy in coming months?
I need to list products based on their choice of buying preference (high to low). What models or algorithm can be used for this?
Association Analysis is a text-book algorithm build for this use-case. You can also use Collaborative Filtering can also be used to model the problem that you've described.
Here is a Python implementation of Apriori Algorithm which I believe would help you

Getting through in Machine Learning [closed]

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I have just completed Machine learning course from Andrew ng and would like to proceed further.
I also want the python implementation of Machine Learning from beginning so that i can practice on Kaggle.
Also, is there any better book or tutorial or some resource like that so that i can proceed further without wasting any time searching such resources.
The best book unequivocally that has implementation of Machine Learning algorithms in Python is the "Introduction to Machine Learning with Python: A Guide for Data Scientists" by Andreas C. Müller. Machine Learning algorithms in Python can be used from a package called scikit-learn. This package has everything you need for Machine Learning. All the algorithms, scaling, cross validation. And that book is written by the chief developer of scikit-learn itself.

How can Machine Learning approaches be applied to Natural Language Processing? [closed]

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I am trying to do a paper about the Machine learning been applied in NLP. Can you guys please suggest me applications that have already used the Machine learning with the NLP?
The list is broad since machine learning is becoming more and more mainstream.
Regarding text, images and video, a good list of APIs would be:
AT&T Speech, IBM Watson, Google Prediction, Wit.ai, AlchemyAPI, Diffbot and I guess Project Oxford as well.
Hope it helps.
If you want something generic you can use this tutorial: http://www.cs.columbia.edu/~mcollins/papers/tutorial_colt.pdf
It is probably not the more recent information but you could find it useful if you start to learn ML methods for NLP.
As it is mentionned in this tutorial, ML methods are generally linked to the NLP task (Information Extraction, Machine Translation, etc.).
IBM Watson project is an example of platform that uses NLP and ML.

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