Why Python is Widely Used for Machine Learning?

Machine learning is at the heart of the science of driving Weather forecasts, Automated cars, and advanced neurosurgical procedures. Similarly, AI, Machine Learning, and Data Science all have one thing in common: Python. In this article, we'll study the 'why' of Python's dominance in the field of AI and Machine Learning.

Table of content

- Why Python!

- Why Python Machine Learning?

• Ease of Use
• Python Machine Learning libraries
• Python’s visualization approach
  Matplotlib:
  Seaborn:
• Python's adaptability (and ability to merge)
• Availability of resources

- Conclusion

If you're thinking about starting your journey into Machine Learning, you've probably heard about Python's adaptability, which can help you understand Machine Learning efficiently. Python has become the world's fastest-growing language (Stack Overflow Developers Survey 2020) due to its widespread adoption in industries and the flexibility of combining Python with other languages to provide faster and more accurate outcomes.

Python Machine Learning: Why Python!

Python requires 3-4 times less time to code than other programming languages to produce the same results. Python features simpler syntaxes and not overly sophisticated functions, putting greater focus on natural language. Python is often the first language newcomers learn because of its ease of use.

“Python allows us to produce maintainable features in record times, with a minimum of developers” - Cuong Do, Software Architect, YouTube

According to experts, Python is one of the most significant programming languages for Machine Learning. It is quickly becoming a top choice for every developer working in Big Data, AI, and ML. But what makes it one of the viable options for Machine Learning?

Let's sort things out, shall we?

Python Machine Learning: Why Python Machine Learning?

Machine Learning Experts deal with challenging algorithms and clustered data regularly. Python's streamlined syntax allows experts to spend more time working out solutions to complex ML issues rather than focusing on the language's complexity.

Python is a simple language to learn. For the majority of secondary students in schools, Python is the primary programming coding language when introduced to computer science. Anyone with a basic comprehension of English and Math can begin learning Python. It is easier to create models for machine learning since it is easier for humans to grasp.

Factors aiding Python Machine Learning's dominance in the Artificial Intelligence race:

• Ease of Use
• Python Machine Learning Libraries and Frameworks
• Flexibility
• Resources Availability
• Python Visualization Attributes

Python Machine Learning: Ease of use

Python is an open-source language, which means it can be used by anyone, anywhere, as long as one has a digital device to perform on. Its feature of Platform independence makes it more approachable for both new and experienced programmers, enabling them to build things on one system and utilize them on another without making any modifications.

When it comes to modification, Python is flexible and adaptable; it does not require recompilation of the source code, which makes it simpler for programmers to regularly modify the programmers per the newest standards and requirements.

Developers can readily grasp the code of their team members due to its ease of reading. This allows Python Machine Learning Experts to collaborate on the same project without having to go through any room for mistakes, errors, or other types of hiccups, resulting in better inter-exchange of resources and ideas.

Python provides various frameworks and libraries that make your life peaceful as you embark on the Python Machine Learning journey.

Python Machine Learning: Python Machine Learning libraries

Better results, i.e., predictions, requires accurate data analysis, mathematical, and other attributes to be compiled at the same time, which Python Machine learning achieves with simplicity using readily available libraries.

In any programming language, a library is a set of predefined functions that make developing a program easier.

Here are some popular libraries for AI and Machine Learning:

• For General-purpose analysis - Pandas
• For Machine Learning – Keras, TensorFlow, and Scikit
• For Data Analysis – NumPy
• For Advanced Computing - SciPy
• For data visualization - Seaborn

When dealing with sophisticated machine learning applications, these Libraries help Machine Learning Engineers minimize development time and increase productivity.

After assembling the data, it must be represented concisely; With the help of data visualization attributes, Python Machine Learning handles this duty as well.

Python Machine Learning: Python’s visualization approach

Python has a wide range of libraries, some of which are excellent visualization tools. However, ML Developers need to emphasize that in AI, Deep Learning, and Data Science, the ability to express data in a readable style is critical.

Python offers numerous effective tools for plotting data, the most well-known of which being Matplotlib. Seaborn is another good software that provides a much more beautiful plot and also utilizes Matplotlib as its foundation layer.

1. Matplotlib:

It is a robust charting toolkit for Python and its extension NumPy. PyPlot is a Matplotlib package that gives a MATLAB interface to Python. Matplotlib allows users to create publication-quality 2D visuals from their data.

2. Seaborn:

Seaborn aids in visualizing statistical correlations. Statistical analysis is used to determine how variables in a dataset are connected and how that connection is affected by other factors.

Python Machine Learning: Python's adaptability (and ability to merge)

Python supports functional, object-oriented, and imperative programming styles, letting Machine Learning Professionals focus on what technique is appropriate for them. Because of its flexibility, it provides a safe environment for developers and decreases the likelihood of errors.

When it comes to delivering outcomes for a project or a digital product, the ability to integrate with other languages is essential. Python for Machine Learning is used on the back-end to provide an interface for the user. It may be called out by any other language to create a quick and dependable product.

Python, being a general-purpose language, can do a variety of complicated machine learning functions and enables you to quickly develop prototypes that allow you to evaluate your solution for machine learning objectives.

Python Machine Learning: Availability of resources

If you want to study Python Machine Learning, the abundance of internet resources might assist you to figure out where to start. In today's internet age, where the science behind everything and anything can be taught from the ground up to the Finest level, anybody may attain their goals and forge a professional path that is strong enough to pursue.

Subject matter experts, industrial developers, and a variety of other specialists can assist you in learning the language and applying it in the workplace. There are various programming language courses accessible to learn from, such as Machine Learning with Python, which is available on Coursera and is free to enroll in for anybody. The course is taught by Joseph Santarcangelo, Ph.D., Data Scientist at IBM.

There is a wealth of material available online like Python Machine Learning tutorials, Tests, and other Courses which provide an introduction to machine learning with Python, as well as in Python groups and forums where programmers and machine learning developers discuss errors, solve challenges and assist one another.

With all these features of Python Machine Learning, It's no surprise that it is used in a variety of sectors like Travel, Fintech, Transportation, and Healthcare for prediction and other Artificial Intelligence applications.

Python Machine Learning: Conclusion

Python has carved out a distinct niche for itself as the industry has shifted its attention to designing for the future, where data will play a vital role. Google, Instagram, Facebook, Dropbox, Netflix, Walt Disney, YouTube, Uber, Amazon, and Reddit are just a few of the tech giants that have embraced Python machine learning.

Python's agility has made it the language of choice for all AI and ML practitioners and its usage has climbed in tandem with the enhancing need for AI and ML professionals across all industries. With this increased use, hands-on professionals must learn Python Machine Learning if they want to succeed in the AI field in the coming years.