Python language is an easy and user-friendly language. Now the question is, When is a language easy/user-friendly? The answer is, language is easy/user-friendly when the program is easy to read and understand and write too. Right? Similarly, Python is one of the simple and very convenient languages for the user.
Now the question is, only simple and easy-to-understand language is enough to create AI? The answer is “No”. We require the proper libraries to program any AI
5 Reason for the popularity of Python in AI:
1. Great library ecosystem
2. Low entry barrier
4. Convenient visualization options
A Great Library ecosystem
One of the main reasons for the popularity of the Python programming language is the great choice of the library.
Let me explain to you about the library, Python libraries provide base-level items so that the developers don’t have to code them from the beginning every time.
The most popular and important libraries you can use for ML and AI:
- Scikit-learn – It is available to Handel basic ML algorithm like, regression, classification, clustering, linear and logistic regression and other.
- Pandas – It is high-level data structure and analysis.
- Keras – It is use for deep learning.
- TensorFlow – It works with deep learning by setting up training and utilizing artificial neural network with massive dataset
- Matplotlib – Use for 2D plots, histogram, charts, etc.
- NLTK – Use for working with computational linguistics, natural language recognition and processing.
- Scikit-image – Use for image processing
- PyBrain – Use for neural network, unsupervised and reinforcement learning
- Caffe – Use for deep learning that allows switching between the CPU and the GPU
- StateModels – Use for statistical algorithms and data exploration
Low Entry Barrier
Python’s low barrier to entry makes it the perfect choice for the beginner and often makes it fun to use, it allows more data scientists to quickly pick up python to use for AI development without wasting too much effort on learning the language.
Python for Machine learning is a great choice as this language is very flexible.
The reasons are:
- It gives options to choose from OOPs or Scripting.
- The results are quick.
- Python can be combined with other languages to reach the goal.
Moreover, flexibility allows developers 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 like 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.
Convenient visualization options
We’ve already discussed 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.
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 a more 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.