7 Steps to Mastering Intermediate Machine Learning with Python -- 2019 Edition

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Are you interested in learning more about machine learning with Python? I recently wrote 7 Steps to Mastering Basic Machine Learning with Python -- 2019 Edition, a first step in an attempt to updated a pair of posts I wrote some time back (7 Steps to Mastering Machine Learning With Python and 7 More Steps to Mastering Machine Learning With Python), a pair of posts which are getting stale at this point, having been around for a few years. It's time to add on to the "basic" post with a set of steps for learning "intermediate" level machine learning with Python. We're talking "intermediate" in a relative sense, however, so do not expect to be a research-caliber machine learning engineer after getting through this post. The learning path is aimed at those with some understanding of programming, computer science concepts, and/or machine learning in an abstract sense, who are wanting to be able to use the implementations of machine learning algorithms of the prevalent Python libraries to build their own machine learning models.


7 Steps to Mastering Intermediate Machine Learning with Python -- 2019 Edition

#artificialintelligence

Are you interested in learning more about machine learning with Python? I recently wrote 7 Steps to Mastering Basic Machine Learning with Python -- 2019 Edition, a first step in an attempt to updated a pair of posts I wrote some time back (7 Steps to Mastering Machine Learning With Python and 7 More Steps to Mastering Machine Learning With Python), a pair of posts which are getting stale at this point, having been around for a few years. It's time to add on to the "basic" post with a set of steps for learning "intermediate" level machine learning with Python. We're talking "intermediate" in a relative sense, however, so do not expect to be a research-caliber machine learning engineer after getting through this post. The learning path is aimed at those with some understanding of programming, computer science concepts, and/or machine learning in an abstract sense, who are wanting to be able to use the implementations of machine learning algorithms of the prevalent Python libraries to build their own machine learning models.


Top 20 Python Machine Learning Open Source Projects

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Pylearn2 is a library designed to make machine learning research easy. Its a library based on Theano NuPIC, 4392 commits, 60 contributors, www.github.com/numenta/nupic The Numenta Platform for Intelligent Computing (NuPIC) is a machine intelligence platform that implements the HTM learning algorithms. HTM is a detailed computational theory of the neocortex. At the core of HTM are time-based continuous learning algorithms that store and recall spatial and temporal patterns.


ŷhat Random Forests in Python

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Random forest is a highly versatile machine learning method with numerous applications ranging from marketing to healthcare and insurance. It can be used to model the impact of marketing on customer acquisition, retention, and churn or to predict disease risk and susceptibility in patients.


Some Essential Hacks and Tricks for Machine Learning with Python

@machinelearnbot

It's never been easier to get started with machine learning. In addition to structured MOOCs, there is also a huge number of incredible, free resources available around the web. Familiarity and moderate expertise in at least one high-level programming language is useful for beginners in machine learning. Unless you are a Ph.D. researcher working on a purely theoretical proof of some complex algorithm, you are expected to mostly use the existing machine learning algorithms and apply them in solving novel problems. This requires you to put on a programming hat.