Why I Think Python is Perfect for Machine Learning and Artificial Intelligence | by Andrew Luashchuk | Towards Data Science

This article about why Python is good for ML and AI is originally posted on Django Stars blog.
Artificial Intelligence (AI) and Machine Learning (ML) are the new black of the IT industry. While discussions over the safety of its development keep escalating, developers expand abilities and capacity of artificial intellect. Today Artificial Intelligence went far beyond science fiction idea. It became a necessity. Being widely used for processing and analyzing huge volumes of data, AI helps to handle the work that cannot be done manually anymore because of its significantly increased volumes and intensity.
For instance, AI is applied in analytics to build predictions that can help people create strong strategies and look for more effective solutions. FinTech applies AI in investment platforms to do market research and predict where to invest funds for bigger profits. The traveling industry uses AI to deliver personalized suggestions or launch chatbots, plus enhance the overall user experience. These examples show that AI and ML are used process loads of data to offer better user experience, more personal and accurate one.
Today, with the expansion of volumes and complexity of data, AI and ML are used for its processing and analysis. To be fair, the human brain can analyze large amounts of data, but this ability is limited by the volume of data it can absorb at any moment. Artificial intelligence is free from this limitation. More accurate predictions and insights delivered by AI improve business efficiency, lower production cost and increase productivity. No wonder that many industries apply AI and ML to improve performance and propel the product development.
According to Deloitte research, AI-fueled companies are the latest trend in the technological transformation aimed at improvement of productivity. That is also proved by their prediction that within the next 24 months the number of companies that will use AI in their products and processes to achieve greater efficiency and strategic goals will likely increase. To put it short, AI helps doing better work with fewer efforts.
Given the listed advantages of AI usage, more and more companies are eager to use it. However, AI is a two-way street — being used for optimization of the analytic process it is not the easiest technology to develop. Due to huge volumes of data to be analyzed, the AI product has to be able to handle the high-loaded process effectively and does not take too much time for that. To make it work properly, the appropriate language has to be chosen for its development. The one that will not be too complex in terms of syntax, will be able to handle sophisticated processes and is easy to support.
As AI and ML are being applied across various channels and industries, big corporations invest in these fields, and the demand for experts in ML and AI grows accordingly. Jean Francois Puget, from IBM’s machine learning department, expressed his opinion that Python is the most popular language for AI and ML and based it on a trend search results on indeed.com.
According to the graph from Francois Puget, Python is the major code language for AI and ML.
We have conducted some research on Python’s strong sides and found out why you should opt in for Python when bringing your AI and ML projects to life.
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A great choice of libraries is one of the main reasons Python is the most popular programming language used for AI. A library is a module or a group of modules published by different sources like PyPi which include a pre-written piece of code that allows users to reach some functionality or perform different actions. Python libraries provide base level items so developers don’t have to code them from the very beginning every time.
ML requires continuous data processing, and Python’s libraries let you access, handle and transform data. These are some of the most widespread libraries you can use for ML and AI:
Scikit-learn for handling basic ML algorithms like clustering, linear and logistic regressions, regression, classification, and others.
Pandas for high-level data structures and analysis. It allows merging and filtering of data, as well as gathering it from other external sources like Excel, for instance.
Keras for deep learning. It allows fast calculations and prototyping, as it uses the GPU in addition to the CPU of the computer.
TensorFlow for working with deep learning by setting up, training, and utilizing artificial neural networks with massive datasets.
Matplotlib for creating 2D plots, histograms, charts, and other forms of visualization.
NLTK for working with computational linguistics, natural language recognition, and processing.
Scikit-image for image processing.
PyBrain for neural networks, unsupervised and reinforcement learning.
Caffe for deep learning that allows switching between the CPU and the GPU and processing 60+ mln images a day using a single NVIDIA K40 GPU.
StatsModels for statistical algorithms and data exploration.
In the PyPI repository, you can discover and compare more Python libraries.
Working in the ML and AI industry means dealing with a bunch of data that you need to process in the most convenient and effective way. The low entry barrier allows more data scientists to quickly pick up Python and start using it for AI development without wasting too much effort into learning the language.
Python programming language resembles the everyday English language, and that makes the process of learning easier. Its simple syntax allows you to comfortably work with complex systems, ensuring сlear relations between the system elements.
For instance, this code is written in an attempt to find out if an input number is prime.
Here’s the view of the code:
test_number = 407 # our example is not a prime number# prime numbers are greater than 1if test_number > 1:# check for factorsnumber_list = range(2, test_number)for number in number_list:number_of_parts = test_number // numberprint(f"{test_number} is not a prime number")print(f"{number} times {number_of_parts} is {test_number}")breakelse:print(f"{test_number} is a prime number")else:print(f"{test_number} is not a prime number")
And as you may see in the last row, the result of this code is that the test number is not a prime one. To put it bluntly, an English-speaking person could easily understand the meaning of the code, as it uses simple English words.
In addition to this, there’s a lot of documentation available, and Python’s community is always there to help out and give advice.
Python for machine learning is a great choice, as this language is very flexible:
It offers an option to choose either to use OOPs or scripting.
There’s also no need to recompile the source code, developers can implement any changes and quickly see the results.
Programmers can combine Python and other languages to reach their goals.
Moreover, flexibility allows developers to choose the programming styles which they are fully comfortable with or even combine these styles to solve different types of problems in the most efficient way.
The imperative style consists of commands that describe how a computer should perform these commands. With this style, you define the sequence of computations which happen as a change of the program state.
The functional style is also called declarative because it declares what operations should be performed. It doesn’t consider the program state, compared to the imperative style, it declares statements in the form of mathematical equations.
The object-oriented style is based on two concepts: class and object, where similar objects form classes. This style is not fully supported by Python, as it can’t fully perform encapsulation, but developers can still use this style to a finite degree.
The procedural style is the most common among beginners, as it proceeds tasks in a step-by-step format. It’s often used for sequencing, iteration, modularization, and selection.
The flexibility factor decreases the possibility of errors, as programmers have a chance to take the situation under control and work in a comfortable environment.
Python is not only comfortable to use and easy to learn but also very versatile. What we mean is that Python for machine learning development can run on any platform including Windows, MacOS, Linux, Unix, and twenty-one others. To transfer the process from one platform to another, developers need to implement several small-scale changes and modify some lines of code to create an executable form of code for the chosen platform. Developers can use packages like PyInstaller to prepare their code for running on different platforms.
Again, this saves time and money for tests on various platforms and makes the overall process more simple and convenient.
Python is very easy to read so every Python developer can understand the code of their peers and change, copy or share it. There’s no confusion, errors or conflicting paradigms, and this leads to more a efficient exchange of algorithms, ideas, and tools between AI and ML professionals.
There are also tools like IPython available, which is an interactive shell that provides extra features like testing, debugging, tab-completion, and others, and facilitates the work process.
We’ve already mentioned that Python offers a variety of libraries, and some of them are great visualization tools. However, for AI developers, it’s important to highlight that in artificial intelligence, deep learning, and machine learning, it’s vital to be able to represent data in a human-readable format.
Libraries like Matplotlib allow data scientists to build charts, histograms, and plots for better data comprehension, effective presentation, and visualization. Different application programming interfaces also simplify the visualization process and make it easier to create clear reports.
It’s always very helpful when there’s strong community support built around the programming language. Python is an open-source language which means that there’s a bunch of resources open for programmers starting from beginners and ending with pros.
A lot of Python documentation is available online as well as in Python communities and forums, where programmers and machine learning developers discuss errors, solve problems, and help each other out.
Python programming language is absolutely free as is the variety of useful libraries and tools.
As a result of the advantages discussed above, Python is becoming more and more popular among data scientists. According to StackOverflow, the popularity of Python is predicted to grow until 2020, at least.
This means it’s easier to search for developers and replace team players if required. Also, the cost of their work maybe not as high as when using a less popular programming language.
Python offers many features that are helpful for AI and ML in particular, and that makes it the best language for these purposes. No wonder that various industries use Python for predictions and other machine learning tasks.
Let’s take a closer look at some examples in:
Travel;
Fintech;
Transportation;
Healthcare.
For example, the travel industry giant Skyscanner used a Python unsupervised ML algorithm to predict the behavior of new airplane routes. They compared thousands of origins and destinations, evaluating each one of them with 30 different criteria to define the demand of passengers. Their results are displayed on a dashboard, where you can choose any origin city to see the groups of destinations numbered from 0 to 9 and their characteristics.
Such an example of AI implementation in the traveling industry is extremely helpful for suggesting destinations to the users, assisting the creation of marketing budgets, as well as setting an initial price for new routes.
AI used in financial services helps to solve problems connected with risk management, fraud prevention, personalized banking, automation, and other tools which help to provide a high-quality financial service to the users. It’s predicted that AI in fintech could reduce operating costs by 22% by 2030, resulting in an impressive $1 trillion.
Some successful examples of online banking software built on Python are Venmo, Affirm or Robinhood. These services not only allow users to make and control their payments and purchases but they also create a social network inside the software, so people can stay connected.
When it comes to cryptocurrency, Python is used to build solutions like Anaconda to effectively analyze the market, make predictions and visualize data.
Uber developed an ML platform Michelangelo PyML using Python. They use it for online and offline predictions solving day-to-day tasks. The Michelangelo PyML is the extension of the initial Michelangelo product which was scalable but not flexible enough. Now, users can validate models with PyML and then replicate them in Michelangelo for full efficiency and scalability.
AI is reshaping the healthcare industry by helping to predict and scan diseases, detect injuries, and help people maintain good health even on a day-to-day basis with easy-to-use mobile applications.
There are many great AI based projects in the industry. For example, Fathom is a natural language processing system which is made to analyze electronic health records, and their mission is “to automate medical coding.” Their leaders have come from companies like Google, Amazon, Facebook or the universities of Stanford and Harvard.
AiCure is another startup focused on making sure that patients take the right medications at the right time. For that purpose, they use technologies like face recognition, pill recognition, and action recognition. The application is also able to analyze the patient’s state and understand if the treatment is working. They use IMA which is an interactive medical assistant that can gather clinically significant data which then can be analyzed by the software.
Growing popularity leads to the growing demand for Python programmers inside the data science community, and it’s a wise choice to choose a language that’s in high demand, as, in the future, it will allow even more functionality.
The open-source nature of Python allows any AI development company to share their achievements with the community. If you’ve made up your mind and decided to learn Python, or want to use this language for your AI projects, here’s a list of useful opensource projects for you to begin with:
OpenCog FoundationOpenCog is “building better minds together” by putting effort into creating the Artificial General Intelligence (AGI) with human capacities. It was founded in 2011, and, now, it’s used at the SingularityNET project, as well as at the Hanson Robotics delivering intelligence to the Sofia and other robots.
Institute for Artificial IntelligenceThe Institute of Artificial Intelligence is a part of the Faculty of Computer Science at the University of Bremen. It conducts research on AI and holds different workshops and events which help move the AI technologies forward, involving more young people into the sphere and educating them, while also supporting existing AI-powered projects and companies.
ZulipIt’s the “world’s most productive team chat” that allows the processing of thousands of real-time messages a day. Fortune 500 companies and other large and open source projects use Zulip, which offers clear organization, asynchronous communication, and other great advantages that are useful for teams.
MagentaMagenta is a Python library and a research project, the biggest goal of which is to make music and art using AI. It works on image, songs, drawings generation and lets artists explore new ways of creating.
MailPileMailPile is an innovative email client that focuses on safe and private communication. It’s a project that tries to answer a question: “How can I protect my privacy online?” It’s fast, has no ads and comes with a powerful search function as well as privacy and encryption.
AI and ML are fast-growing and universal technologies that let scientists resolve real-life dilemmas and come up with clever solutions. The reason why many of them consider Python the perfect programming language for AI is due to the following advantages: