Why Python Is Best for Machine Learning

Today, most companies are using Python for AI and Machine Learning. With predictive analytics and pattern recognition becoming more popular than ever, Python development services are a priority for high-scale enterprises and startups. Python developers are in high-demand — mostly because of what can be achieved with the language. AI programming languages need to be powerful, scalable, and readable. Python code delivers on all three.

While there are other technology stacks available for AI-based projects, Python has turned out to be the best programming language for this purpose. It offers great libraries and frameworks for AI and Machine Learning (ML), as well as computational capabilities, statistical calculations, scientific computing, and much more. 

In this article, we will learn about a few aspects of the Python programming language that make it perfect for Machine Learning engineers. We will review the following:

  • Why use Python software for Machine Learning and AI
  • The best Python libraries for AI
  • Conclusion 

Let’s understand why developers prefer this programming language over others including R, Go, Scala, and others designed for AI-based projects.

Checkout Pros and Cons of Python

Why Use Python for Machine Learning and AI?

Surveys show that Python is now one of the top programming languages after C and Java. It allows developers to build powerful backend systems for Python AI projects. There are multiple benefits of the Python programming language for Machine Learning and AI development. Let’s have a look at them in detail.

Rapid Development

  • The Python community admires the programming language for its quick prototyping capabilities. Developers can reduce time wasted on learning the intricacies of the stack. They can start quickly with AI development and move onto building AI algorithms and programs quickly.
  • Since the Python code is similar to English, it is easily readable and simple to write. Developers don’t have to spend a lot of time writing complicated code. On top of that, there are great libraries and frameworks for AI and Machine Learning (ML) available in Python that can help simplify the process. We will have a look at them in detail later in the article.

Flexible Language

  • Allowing developers the utmost flexibility for AI applications is what Python programmers admire about the language. Python for Machine Learning allows you to choose OOPS or scripting-based programming and enables quick result view without completely recompiling the Python code.
  • There are four different styles of Python software that you can choose from — imperative, object-oriented, functional, and procedural — all of which reduce the possibility of errors based on your AI project.

Readability

  • For most developers, readability is a game-changer. However, Python doesn’t complicate things for you. The syntax of Python for Machine Learning Development is just like English. You don’t have to indulge in understanding the language for a long-time.
  • If there are developers joining in the middle of a project, they can easily understand what’s going on. There are fewer chances of confusion, errors, and conflicting paradigms that enable swift development of any Machine Learning program.

Visualization Options

  • Data is the most important aspect of Machine Learning, Artificial Intelligence, and Deep Learning algorithms. Working with data requires heavy visualization to determine patterns and make sense of all variables and factors. For that purpose, Python software packages are the best.
  • Developers can build histograms, charts, and plots for better understanding of how data will interact and work together. There are APIs as well that make the visualization process easier by enabling you to outline clear reports of data.

Apart from this, there’s an amazing Python community available for support, consistency, and simplicity throughout the development process. The programming language is now becoming common for Machine Learning development. However, there are libraries that make this possible. Let’s look at the best Python libraries for Artificial Intelligence. 

Checkout Difference between Artificial Intelligence (AI), Machine Learning(ML), and Deep Learning(DL)

Python’s 6 Great Libraries and Frameworks for AI and Machine Learning (ML)

The best thing about the Python programming language is the plethora of libraries for Machine Learning development. Here are the top 6 Python libraries that make Artificial Intelligence seamless with readable and powerful algorithms.

NumPy

Data science would be incomplete without NumPy. It is a Python software package that allows scientific computing. NumPy is a fantastic library for multi-dimensional array objects. They work together and reduce the computational complexity of the program.

SciPy

SciPy is another popular library for Python AI projects and is the first choice for Python programmers for scientific and heavy computing involving mathematics and engineering. It provides routines for numerical optimization and integration and is extremely user-friendly for beginners.

Scikit-Learn

This library is built upon NumPy and SciPy.  It is used for supervised and unsupervised learning. It is a perfect tool for data mining and data analysis and is a popular option for any Machine Learning engineer who wants to approach AI projects with an integrated approach.

Pandas

The open-source Python software package, Pandas, enables programmers to manipulate data and analyze it. It has efficient data exploration and visualization capabilities and offers high-level data structures with a wide variety of tools for working closely with multiple datasets.

Keras

Keras is an API that runs over TensorFlow. The focus of Keras is to allow developers quick experimentation for Artificial Intelligence. This library has a far better user experience than TensorFlow — because it was developed in Python, it is easier to understand than other tools.

Matplotlib

The mightiest of all libraries is Matplotlib. It offers capabilities for data visualization and exploration, as well as charts, histograms, scatterplots, etc., to customize Python AI projects. Matplotlib helps in quickly manipulating data for a visual representation in less time.

These were the top 6 libraries for Python and Machine Learning. Apart from these, there are libraries like TensorFlow, NLTK, PyBrain, Caffe, etc., which lead to the proper performance of AI apps. 

Checkout Python use cases and implement it in your organization

Conclusion 

The Python programming language has been in the game for so long, and it’s here to stay. There are only a few programming languages — and Python is one of the best. We saw the benefits of Python for Machine Learning and why it is important for AI. We also looked at the top Python libraries and tools that simplify the process of Python AI development

In essence, Python is an exceptional programming language for Artificial Intelligence. It has the power and scalability to handle massive amounts of data requests simultaneously. It would be interesting to see the integration of Python and Machine Learning in the future.

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