Python has been used by data scientists for years, but there are still many things we don't know about it. In this article, I'll share my favorite Python libraries for machine learning.
Python is one of the most popular programming languages ?? on the market and currently takes first place with 33.18% of the market share. And this figure should not be surprising since Python is an extremely easy-to-learn programming language and incredibly flexible at the same time. It is excellent for many purposes, and machine learning is one such purpose. Python has many different libraries of complete tools for integrating machine learning technologies into business projects. In this article, we’ll take a look at 10 well-known machine-learning libraries in Python. So, without further ado, let’s get started.
Note: Keep in mind that the places at this top do not indicate that one library is better than another. We just list the top ten most popular libraries.
This library was created in 2015 by Google Brain and was initially intended for internal use in official Google products. However, with the growing popularity, more and more promising startups have expressed a desire to complement their technologies with this library. Among these companies are such popular ones as Airbnb, Airbus, PayPal, VSCO, and many others.
The reason for this popularity was the combination of several advantages:
While Google was working on its machine learning library, Facebook kept up with trends and did some development. The result of these developments is the PyTorch library. However, the direction of this library is slightly different from Google’s. The main tasks are computer vision analysis, natural language processing, and other complex tasks. However, this library also found its fans in the face of Microsoft, Uber, Walmart, and other companies.
Among the advantages of this library are:
The initial iteration of the Keras library is a deep neural network tool. However, over time, the library has been redesigned and evolved into a standalone Python ML library. This library represents a wide range of tools that currently leverage companies like Netflix, Uber, Yelp, and others.
This library is the choice of many thanks to:
This library is one of the oldest, and nevertheless, it is used in modern projects where machine learning is required. Orange3 was created in 1996, and the library was based on the popular C ++ programming language. In 1997, the library gained wide popularity, and many programmers began to use its widgets in their projects.
This library is still on the market due to the following strengths:
Matplotlib is an open-source and data visualization and plotting library that is primarily used for creating animated, and interactive visualizations in two-dimensional graphics in the Python language and its numerical mathematics extension NumPy. Matplotlib was written and maintained primarily by John Hunter in 2003 and is distributed under a BSD-like license. With this Python library, you can create various plots as per your data like Bar Chart, Scatter Plot, Histograms, Contour Plots, Pie Plot, Box Plot, etc
The main advantage of this library is:
Some Drawbacks of this library include its dependency on other packages like Numpy, and it can’t be used in another language other than Python.
We just mentioned NumPy, and now is the time to talk about it in more detail. We will go from afar and say that when the Python language was just created, it did not include the functionality of numerical calculations. However, when the NumPy library appeared with various math functions, it expanded the capabilities of the Python programming language. It is on the basis of these functions that all machine learning solutions are created.
In addition, this library has the following strengths:
Since SciPy is included in the eponymous stack, this library is also worth talking about a bit. This is an open-source library for scientific and engineering calculations. Among its capabilities, you can find the following mathematical and other operations: finding minimums and maxima of functions, calculating integrals, signal processing, image processing, and many others.
All of these useful features are supported by the following strengths:
The seventh place in our top is opened by the Scikit-learn library, which was originally planned as a third-party extension of the SciPy library. However, the developers’ plans have changed, and over time this library has become one of the most popular on the well-known GitHub platform. Thanks to its advantages, it supports popular applications such as Spotify, Booking.com, OkCupid, and many others.
By the way, about the benefits:
The penultimate place at our top is called the low-level Pandas library, which is based on the already familiar NumPy library. This library was created in 2008 by the financial company AQR because it urgently needed tools for mathematical calculations and quantitative analysis of financial data. Previously, this library was intended for internal use by AQR only. However, when the main developer of the library, Wes McKinney, was about to leave the company, he persuaded the directors to make this library open-source, which allowed it to go public and gain high popularity.
In addition, the Pandas Library has the following strengths:
At the tenth place of our top is a rather old library relevant today. Theano Library was created by MILA in 2007, and the main purpose of this library is to handle various mathematical expressions. These mathematical expressions then become the basis of deep machine learning algorithms. Despite the fact that this library is not as ultimate as the same TensorFlow or PyTorch, it is still a great addition to the process of creating machine learning models for projects of different sizes.
In addition, Theano has several undeniable advantages in its arsenal:
So, we have told you about the ten most popular libraries for creating machine learning models in Python. As you can see, all libraries are quite diverse and serve different purposes. Some libraries are the ultimate tools for machine learning development, while others are narrowly focused tools that complement the creation process, making it more convenient. Now you are equipped with the knowledge you need and are ready to start building custom machine learning models using these libraries.
Kate Orekhova is a technology writer at Cleveroad. A writer by day and a reader at night, she keeps tabs on technology and innovations. She is passionate to tell people about the latest tech trends in the world of IT.
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