In this article, Sunila Gollapudi, author of Practical Machine Learning, introduces the key aspects of machine learning semantics and various toolkit options in Python.
Machine learning has been around for many years now and all of us, at some point in time, have been consumers of machine learning technology. One of the most common examples is facial recognition software, which can identify if a digital photograph includes a particular person. Today, Facebook users can see automatic suggestions to tag their friends in their uploaded photos. Some cameras and software such as iPhoto also have this capability.
Let’s spend some time understanding what the “learning” in machine learning means. We are referring to learning from some kind of observation or data to automatically carry out further actions. An intelligent system cannot be built without using learning to get there. The following are some questions that you’ll need to answer to define your learning problem:
Before we plunge into understanding the internals of each learning type, let’s quickly understand a simple predictive analytics process for building and validating models that solve a problem with maximum accuracy:
The following diagram depicts how learning can be applied to predict behavior:
The following concept map shows the key aspects of machine learning semantics:
Python is one of the most highly adopted programming or scripting languages in the field of machine learning and data science. Python is known for its ease of learning, implementation, and maintenance. Python is highly portable and can run on Unix, Windows, and Mac platforms. With the availability of libraries such as Pydoop and SciPy, its relevance in the world of big data analytics has tremendously increased.
Some of the key reasons for the popularity of Python in solving machine learning problems are as follows:
Before we go deeper into what toolkit options we have in Python, let’s first understand what toolkit option trade-offs should be considered before choosing one:
There are three options in Python:
Python has two core toolkits that are more like building blocks. Almost all the following specialized toolkits use these core ones:
Some of the most popular Python toolkits are as follows:
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