Machine Learning with Python
Machine learning is a branch in computer science that studies the design of algorithms that can learn.
Typical tasks are concept learning, function learning or “predictive
modeling”, clustering and finding predictive patterns. These tasks are
learned through available data that were observed through experiences or
instructions, for example.
The hope that comes with this discipline is that including the
experience into its tasks will eventually improve the learning. But this
improvement needs to happen in such a way that the learning itself
becomes automatic so that humans like ourselves don’t need to interfere
anymore is the ultimate goal.
There are close ties between this discipline and Knowledge Discovery,
Data Mining, Artificial Intelligence (AI) and Statistics. Typical
applications can be classified into scientific knowledge discovery and
more commercial ones, ranging from the “Robot Scientist” to anti-spam
filtering and recommender systems.
But above all, you will know this discipline because it’s one of the
topics that you need to master if you want to excel in data science.
Today’s scikit-learn tutorial will introduce you to the basics of
Python machine learning: step-by-step, it will show you how to use
Python and its libraries to explore your data with the help of matplotlib,
work with the well-known algorithms KMeans and Support Vector Machines
(SVM) to construct models, to fit the data to these models, to predict
values and to validate the models that you have build.
If you’re more interested in an R tutorial, check out our Machine Learning with R for Beginners tutorial
链接：
https://www.datacamp.com/community/tutorials/machine-learning-python#gs.w6oOUyA
原文链接：
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