Python for Machine Learning

Python for Machine Learning
Python for Machine Learning

Have you ever wondered how Google and Facebook find stories that are interesting to you? OR video-streaming services such as Netflix, Youtube fetch videos that you like? 

That’s machine learning!

This article focuses on Python for Machine Learning and explains why it is the most effective.

What is Python? 

Python has emerged as a popular programming language that is built for nearly every purpose that has an easy syntax and dynamic semantics.

What is Python in Machine Learning?

Python, the most preferred programming language due to its features, applicability, and simplicity. 

Python best fits in machine learning due to its independent nature and its popularity in the programming community.

What Is Machine Learning?

Machine learning is here to answer complex questions using a massive amount of data and this allows the computers to learn automatically without human intervention, and adjust actions accordingly.

Machine learning is a subclass of artificial intelligence (AI) that helps in analysing patterns in data to advance decision-making. 

Why is Python the best programming language for machine learning? 

In Python, syntax structure is a little easy to understand and its data handling capacity is just amazing. This is the reason why machine learning projects are handled in python.  

According to Google Trends, the Usage of Python for Machine Learning has raised to an all-new level with other ML languages such as R, Java, Scala, Julia, etc. 

So now we have a notion that Python is by far the most popular programming language for Machine Learning, but “WHY” still remains. So let’s now understand why Python is so popular and best-suited for Machine Learning. 

Some of these reasons for this are given as follows:

Python has a great library ecosystem and frameworks:
Libraries and frameworks are essential in the preparation of a suitable programming environment and reduce software development time significantly.
To reduce development time, programmers use a number of Python frameworks and libraries.
Here are some of the important libraries and Frameworks: 

  • For textual data – use NLTK, SciKit, and NumPy
  • For images – use Sci-Kit image and OpenCV
  • For  audio – use Librosa
  • For the basic implementation of machine learning algorithms – use Sci-Kit- learn.
  • For scientific computing – use Sci-Py
  • For visualizing the data clearly – use Matplotlib, Sci-Kit, and Seaborn. 
  • StatsModels – used for statistical algorithms and data exploration, etc.
  • Seaborn for data visualization
  • PyTorch frameworks – specially written for Machine learning
  •  Independence across platforms

Python can run on multiple platforms 

  • Python has the ability to run on multiple platforms, such as Windows, Linux, and macOS, thus, requiring little or no changes.

Code Simplicity

  • Coding with Python is super easy. 
  • Many developers describe python as self-explanatory commands, less syntactical rules the syntax, and as math-like (similar to most of the mathematical concepts) that is suitable for Machine Learning.
  • Machine Learning involves complex algorithms and versatile workflows, Python’s simplicity allows developers to write reliable codes – Developers are able to put all their efforts into coding rather than focusing on the technical nuances.

High Flexibility

Python is a great choice for Machine Learning as it is very flexible:

  • Different algorithms and languages can be used by developers along with Python (a majority of code can be checked in the IDE)
  • It is an ideal backend development that is suitable for linking different data structures together
  • Python gives an option to choose between OOPs and scripting

    Python is easy to understand and Read 
  • A machine learning programmer is responsible for extracting, processing, cleaning, arranging data to develop intelligent algorithms.
  • If code change is required, any of the Python developers can easily implement it- So It’s easy to read Python.
  • Python eliminates confusion, mistakes, which in turn increases the efficiency of machine learning professionals.

    Low Entry barrier 
  • Python is easy to learn – the entry barrier is low. What does it mean? Aspiring data scientists can master it quickly and as a result, they can get involved in machine learning projects. 
  • Believe it or not! Python is a very simple language, which makes learning it easier. 
  • Python has a simple phrase structure and one can confidently work with complex systems.

    Popularity and an Engaging Community
  • Python is the most simple and versatile programming language due to which a huge community of developers is getting attracted to it, which makes it a preferred programming language for machine learning and other projects.
  • Many well-known brands including Facebook, Google, Quora, Amazon, Netflix have chosen Python as their programming language. 

Wrapping up.

Learning Python or getting trained in Python is the need of the hour as enterprises have realized that there is no better time than now to invest in these technologies.

With its amazing features (above mentioned), Python makes the development process of Machine Learning based projects a lot easier, fast, and budget-friendly.

This is the reason you should consider Python for your Machine Learning venture.

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