Why mere Machine Learning cannot predict Bitcoin price

Lately, I study time series to see something more out the limit of my experience. I decide to use what I learn in cryptocurrency price predictions with a hunch of being rich. Kidding? Or not :).  As I see more about the intricacies of the problem I got deeper and I got a new challenge … Continue reading Why mere Machine Learning cannot predict Bitcoin price

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Short guide to deploy Machine Learning

Suppose you have a problem that you like to tackle with machine learning and use the resulting system in a real-life project.  I like to share my simple pathway for such purpose, in order to provide a basic guide to beginners and keep these things as a reminder to myself. These rules are tricky since even-thought … Continue reading Short guide to deploy Machine Learning

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Brief History of Machine Learning

    Since the initial standpoint of science, technology and AI, scientists following Blaise Pascal and Von Leibniz ponder about a machine that is intellectually capable as much as humans. Famous writers like Jules Verne , Frank Baum (Wizard of OZ), Marry Shelly (Frankenstein), George Lucas (Star Wars) dreamed artificial beings resembling human behaviors or even more, swamp humanized skills in different … Continue reading Brief History of Machine Learning

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Machine Learning Pathway

Note : I regularly update this list.  Machine Learning 101: I. Introduction to Machine Learning http://homepages.inf.ed.ac.uk/rbf/IAPR/researchers/MLPAGES/mltut.htm http://jeremykun.com/2012/08/04/machine-learning-introduction/ http://www.omidrouhani.com/research/machinelearning/html/machinelearning.htm http://www.youtube.com/playlist?list=PLD63A284B7615313A (cal tech class) II.  Linear Regression http://en.wikipedia.org/wiki/Linear_regression http://www.youtube.com/watch?v=ExVhaN36jBs http://en.wikipedia.org/wiki/Simple_linear_regression http://www.youtube.com/watch?v=ocGEhiLwDVc

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Machine Learning Work-Flow (Part 1)

So far, I am planning to write a serie of posts explaining a basic Machine Learning work-flow (mostly supervised). In this post, my target is to propose the bird-eye view, as I'll dwell into details at the latter posts explaining each of the components in detail. I decide to write this serie due to two reasons; … Continue reading Machine Learning Work-Flow (Part 1)

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A Large set of Machine Learning Resources for Beginners to Mavens

Note : I regularly update this list.  Machine Learning 101: I. Introduction to Machine Learning http://homepages.inf.ed.ac.uk/rbf/IAPR/researchers/MLPAGES/mltut.htm http://jeremykun.com/2012/08/04/machine-learning-introduction/ http://www.omidrouhani.com/research/machinelearning/html/machinelearning.htm http://www.youtube.com/playlist?list=PLD63A284B7615313A (cal tech class) II.  Linear Regression http://en.wikipedia.org/wiki/Linear_regression http://www.youtube.com/watch?v=ExVhaN36jBs http://en.wikipedia.org/wiki/Simple_linear_regression http://www.youtube.com/watch?v=ocGEhiLwDVc

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Best way to qualify your machine learning model.

Selection of your final machine learning model is a vital part of your project. Using the accurate metric and the selection paradigm might give very good results even you use very simple or even wrong learning algorithm. Here, I explain a very parsimonious and plane way. The metric you choose is depended to your problem … Continue reading Best way to qualify your machine learning model.

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