Learn about some of the best Python libraries for programming Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL).
A lot of software developers are drawn to Python due to its vast collection of open-source libraries. Lately, there have been a lot of libraries cropping up in the realm of Machine Learning (ML) and Artificial Intelligence (AI). These libraries can be readily employed by programmers of all levels for tasks in data science, image and data manipulation, and much more. This programming tutorial will shed some light on why Python is the preferred language for Machine Learning and AI as well as list some of the best ML and AI libraries to choose from.
Why choose Python for AI development?
Lead developer for Numerical Python and Pyfort, Paul Dubois, once stated that “Python is the most powerful language you can still read.”. Other qualities that have helped propel Python to its current station is its versatility and flexibility, which allows Python to be used alongside other programming languages when needed, including powerhouses like Java and C#. On top of that, Python can operate on nearly all OS and platforms on the market.
That might explain Python’s enduring popularity among developers, but why are so many of them choosing Python to work with ML and AI libraries? For starters, the sheer number of ML and AI libraries that are available means that developers can count on finding one for whatever problem needs solving. Moreover, being an Object-oriented programming (OOP) language, Python lends itself particularly well to efficient data use and manipulation.
Here are a few more reasons why Python is among the top programming languages for Machine Learning, Deep Learning, and Artificial Intelligence:
- Being free and open-source makes Python community friendly and guarantees improvements in the long run
- Exhaustive libraries ensure there is a solution for every problem
- Smooth implementation and integration make it accessible for people with varying skill levels
- Increases productivity by reducing the time to code and debug
- Can be used for Soft Computing and Natural Language Processing as well
- Works seamlessly with C and C++ code modules
Now that we have discussed why Python is one of the top programming languages, the rest of this article will present some of best python libraries for Machine Learning and AI.
Formerly known as “Numeric”, NumPy was the brainchild of Jim Hugunin, along with contributions from several other developers. In 2005, NumPy was officially born when Travis Oliphant incorporated features of the competing Numarray into Numeric, with extensive modifications. Today, NumPy is completely open-source and has many contributors. It is also widely regarded as the best Python library for Machine Learning and AI.
NumPy is mostly utilized by data scientists to perform a variety of mathematical operations on large, multi-dimensional arrays and matrices. NumPy arrays require far less storage area than other Python lists, and they are faster and more convenient to use, making it a great option to increase the performance of Machine Learning models without too much work. Another attractive feature is that NumPy has tools for integrating C, C++, and Fortran code.
Some of NumPy’s other features that make it popular amongst the scientific community include:
- Support for mathematical and logical operations
- Shape manipulation
- Sorting and Selecting capabilities
- Discrete Fourier transformations
- Basic linear algebra and statistical operations
- Random simulations
- Support for n-dimensional arrays
NumPy (see above) is so popular that several libraries are based on it, including SciPy. Like its inspiration, SciPy is also a free, and open-source library. SciPy is geared towards large data sets, as well as the performing of scientific and technical computing against those data sets. SciPy also comes with embedded modules for array optimization and linear algebra, just like NumPy. Playing a key role in scientific analysis and engineering, SciPy has grown to become one of the foundational Python libraries.
The allure of SciPy is that it takes all of NumPy’s functions and turns them into user-friendly, scientific tools. As such, it is often used for image manipulation and provides basic processing features for high-level, non-scientific mathematical functions.
The main features of SciPy include:
- Data visualization and manipulation
- Scientific and technical analysis
- Computes large data sets
Although TensorFlow can be used for a range of tasks, it’s particularly adept at the training and inference of deep neural networks. Using TensorFlow, developers can create and train ML models on not just computers but also mobile devices and servers by using TensorFlow Lite and TensorFlow Serving. These alternatives offer the same benefits but for mobile platforms and high-performance servers.
Some of the areas in ML and DL where TensorFlow excels are:
- Handling deep neural networks
- Natural Language Processing
- Partial Differential Equation
- Abstraction capabilities
- Image, Text, and Speech recognition
- Effortless collaboration of ideas and code
Final thoughts on Python AI and Machine Learning libraries
This tutorial shed some light on why Python is the preferred language for Machine Learning and AI and listed some of the best ML and AI libraries to choose from, including TensorFlow, SciPy, and NumPy. We will be adding to this list in the coming weeks so be sure to check back often.
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