The applications of AI in businesses have been steadily rising over the years as technology increasingly becomes an integral part of the industry.
As per the 2021 PwC’s annual AI business survey, 86% of CEOs note that AI is a mainstay in their offices.
This graph indicates how and to what extent companies are using AI in their operations.
In this article, we will cover the benefits of using Python for AI. The next step for you is to hire Python developers who can integrate AI into your business.
With AI, Python developers can make machines mimic behavior that is unique to humans like intelligence or intuition.
The most important question is why Python and not any other language?
1. Python is Simple To Learn, Easy To Read And Use
Python is easy to learn and use. It is much easier to find developers who are proficient in it.
This is because the abundance of online resources available to beginners makes learning Python incredibly simple.
For developers working on AI, its straightforward syntax, expressive style, and natural language semantics make it the perfect option compared to other programming languages.
Furthermore, the language’s interpreted execution style makes performing rapid experiments and iterations with it very simple. It may be extensively adjusted to run quickly with compiled versions also.
The simple syntax enables other developers who do not program in Python to also understand the code. Hence, it promotes communication between different developers among the team allowing them to work together.
Here is a snippet of code that is per-processing the data for creating an LDA model. The Python syntax closely resembles English so it is easy to understand that the data is being loaded and the file is being opened to write in it.
2. Python Has A Supportive Community Behind It
Python has been around for almost 30 years. In these 30 years, it has formed a huge Python community.
In fact, according to Jean Francois Puget from IBM’s machine learning department, Python is the most popular language for AI and ML based on a trend of search results on indeed.com.
This means that a lot of Python documentation is available online. Your Python developer can reach out to Python communities and forums, where programmers and machine learning developers discuss errors, solve problems, and help each other out.
3. Python is a Flexible Language
Python is a great language for machine learning because of the flexibility of language.
- Python gives users the choice of using scripting or OOPs.
- Python developers can make any changes without having to recompile the source code and can see the results right away.
- Python and other languages can be combined by programmers to achieve their objectives.
- This flexibility enables programmers to select the programming paradigms with which they are most comfortable and even combine these paradigms to best tackle various challenges.
- The commands in the imperative style specify how a computer should carry out these directives. With this approach, you specify the series of computations that take place as the program’s state changes.
- Since the functional style specifies which actions should be carried out, it is also known as declarative. In contrast to the imperative approach, it declares statements as mathematical equations without taking the state of the programme into account.
- Two ideas—class and object—are the foundation of the object-oriented paradigm, where groups of related items are called classes. Since Python cannot fully perform encapsulation, this style is not entirely supported, although developers can still utilize it to a limited extent.
- The procedural method, which progresses tasks in a step-by-step manner, is the most popular among newcomers. It is frequently used for selection, modularization, sequencing, and iteration.
The ability of programmers to take charge of the issue and work in a flexible manner reduces the likelihood of errors.
4. Python is a Versatile Language
Python developers can perform machine learning development on any platform including Windows, macOS, Linux, Unix, and twenty-one others.
Developers must make a number of minor adjustments and update a few lines of code to convert the process from one platform to another in order to produce executable code for the platform of choice. Developers can prepare their code for operating on many platforms by using tools like PyInstaller.
This streamlines and makes the process more convenient while also saving time and money for testing on other platforms.
Libraries in Python for AI
Python is an open-source language. All its packages and libraries are available for free.
These Python libraries provide base-level items so developers don’t have to code them from the very beginning every time.
As per the Python Developers Survey 2021 Results, these are top libraries used in Python.
Here is an insight into these libraries.
1. Pandas
Pandas is arguably the most comprehensive Python tool for data science.
A Python developer has to slice and dice data in Pandas before moving on to the following phases of your workflow, such as for visualization, reporting, feature engineering, modelling, evaluation, etc., once you have accessed your data via SQL queries, reading image files, scraping HTML pages, etc.
2. Numpy
Numpy and Pandas go hand in hand. It is a fundamental package for scientific computing with Python.
A major portion of data science and machine learning involves working with enormous arrays and matrices, which is essentially performing a lot of linear algebra repeatedly. For a lot of linear algebra, NumPy offers data structures that are well optimized.
3. Scikit-learn
The most well-known Python machine learning package is probably the scikit-learn family of algorithms.
It sits nicely between Matplotlib above and Pandas + NumPy + SciPy below. This is one of the most used ML libraries for Python.
4. SciPy
The basic library for scientific computing is called SciPy.
SciPy offers:
- Numerical analysis, sophisticated linear algebra, and a variety of Python-atop-FORTRAN code for scientific computing.
- It also has functions for reading files in unusual forms, such as image files. In general, this library and NumPy work nicely together.
5. Matplotlib
Matplotlib can create static, animated, and interactive visualisations in Python.
The library also has a large number of third-party packages that extend and build on Matplotlib functionality. This includes several higher-level plotting interfaces (seaborn, HoloViews, ggplot), and a projection and mapping toolkit (Cartopy).
6. BeautifulSoup
BeautifulSoup didn’t make the list but it is one of the most popular packages for accessing data. It can read HTML and XML documents. Most importantly, it can read bad HTML formatting.
BeautifulSoup also automatically translates documents into Unicode, so you don’t have to manually enter the encoding of the file. Using BeautifulSoup, you can turn the HTML markup from the millions of web pages you collected for your NLP project into text data that NLP libraries can interpret.
Now, you have to hire Python developers who can harness the power of these libraries to write AI programs that can boost your business.