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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.
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From my understanding, artificial intelligence can be divided into two subsets, deep learning and machine learning. Which one of those categories does the minimax algorithm fall into when developing an AI to play chess?
1- AI is much wider than Machine Learning. ML is a subset of Learning, Learning is a subset of AI
2- Deep Learning is a platform for ML( if not a subset of ML), to help do the automatic feature selection at the same time as training.
3- There is not really such a boundary of which algorithm belongs to which part of AI
4- A major part of AI named "Problem Solving" in the AI modern approach book. MinMax has been discussed there if I am not mistaken
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Im developing android app fro groceries shop.
How can include or what way i can use ML, AI, Deep learning in my app.
Im just beginner to ML , AI , DL. IM developing app for engineering mini project. So that parallely i can learn both things.
AI means getting a computer to mimic human behavior in some way.
Machine learning is a subset of AI, and it consists of the techniques that enable computers to figure things out from the data and deliver AI applications.
Deep learning, meanwhile, is a subset of machine learning that enables computers to solve more complex problems.
In addition to Zunaib's answer:
Machine Learning is a subset of AI which consists of types of algorithms that are able to autonomously calculate conclusions based on the given data. Where as Deep Learning is a subset of machine learning where we use different types of Neural Networks as algorithms.
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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.
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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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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.