Machine Learning (ML) and projects based on artificial intelligence (AI) are the core technology of the future.
We want our apps to see, hear, and respond for better “Personalization”, more intelligent recommendations, and more accurate searches.
It is the fruit that artificial intelligence (AI) will bring to enhance user experience and create value in many industries.
The benefits of artificial intelligence are undisputed.
However, now you might be wondering: How can we bring these experiences to life? Which programming language is used for AI, ML?
Why use Python for AI and Machine Learning projects?
The first thing you should keep in mind is that AI projects are different from traditional software projects.
The difference lies in the Tech Stack, the skills required for an AI project, and the need for in-depth research; not everyone has the time to master them all.
Tech Stack is defined as the collection of technologies that an organization uses to build a web or mobile application. It is a combination of programming languages.
Therefore, to realize your ambitions with an AI project, you should use a stable, flexible programming language and have support tools/libraries available.
Fortunately, Python provides all of these, which is why today we see A LOT of AI projects made in Python.
From development to implementation and maintenance, Python helps programmers stay productive and confident in the software they make.
Because their software is made with hundreds of genius brains in it (through the tools, libraries, frameworks they use)
The reasons Python is most commonly used in Machine Learning and AI projects include:
Simplicity and consistency
Allows access to excellent libraries and frameworks for AI and machine learning (ML)
Flexibility
Platform independence
And the vast community.
These things make Python even more popular.
Reason #1: Python is SIMPLE and CONSISTENT
Python allows programmers to write SHORT and READY code.
While the complex algorithms and agile workflows of Machine Learning and AI make it easy to complicated systems, Python’s simplicity is the solution that allows programmers to write reliable systems.
Programmers will be able to focus their time and energy on solving Machine Learning problems instead of dealing with the technicalities of the language.
Also, Python attracts many programmers because it is EASY to learn
Python code has a mathematical sound and is similar to human reading/comprehension, so it helps simplify problems so that you focus on AI and ML more than other languages.
Python is the best language for beginners to learn programming, so it is easily accessible to millions of talented programming enthusiasts from an early age. For this reason, Python creates a beneficial cycle: Don’t know anything about programming -> Choose to learn Python -> Get familiar with the language (Don’t want to waste time learning another language) -> Continue studying and develop deeply according to Python -> Python grows more and more.
Many programmers say that Python is more intuitive than other programming languages. Others say that Python has many Frameworks, Libraries, and extensions that simplify the implementation of various functions.
Alternatively, Python is suitable for collaborative work (projects involving many programmers).
Moreover, since Python is a general-purpose language, it can perform a complex set of Machine Learning tasks and allows you to build rapid prototypes, test your products for good machine learning purposes
Reason #2: Python has a wide selection of Libraries and Frameworks
Implementing AI and ML algorithms can be complicated and time-consuming so having a well-structured and well-tested environment is very important for programmers to develop better solutions.
AI and ML projects are generally very complex. You do not have many three years, five years, or ten years to complete the project.
There are also AI/ML projects that require speedy completion times to gain the upper hand.
Therefore, to reduce project development time, programmers switch to some Python Frameworks and Libraries.
A Framework/Library can be understood and pre-written codes that programmers can immediately use to solve everyday programming tasks. The view is “DO NOT RE-invent the WHEEL.”
Python has a rich arsenal of technologies, including many libraries for artificial intelligence and machine learning. Here are some popular libraries and frameworks:
Keras, TensorFlow, and Scikit-learn for Machine Learning
NumPy for data analysis and high-performance scientific computing
SciPy for advanced computing
Pandas for general purpose data analysis
Seaborn for Data Visualization
