How impactful is python in machine learning

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Python is one of the world’s most widely used and popular programming languages. This is because, in many areas, it excels, for instance, in creating video games, embedded programming and even manufacturing mobiles. Also, Python is essential for machine learning (ML) and artificial intelligence (AI). In fact, all the data science online training programmes have a huge portion of their courses allotted for just Python. Notably, a good machine learning certification course gives a detailed knowledge of Python. Read on...

Why is Python important for machine learning?

Data science and machine learning course

The partnership of Python and machine learning has made its place in data science and IT. Python is used for various tasks, from software application development to web development. Here are some reasons why Python is important for machine learning: 

  • Flexibility

Python is an extremely flexible language, and for this reason, it can be used with other languages as well. It allows developers to choose between scripting or OOP. Python also does not need the recompilation of source code. So, it is very easy to see results, and the operations are also easily done. Thus, there is no room left for errors.

  • Very rich ecosystem

Python is a high-level coding language with a vast ecosystem of frameworks, tools and libraries. These libraries and tools are equipped with pre-written codes that help users carry out a large number of functions. This also saves a significant amount of time while coding.

  • Consistency and simplicity

The codes written in Python are concise and readable. Artificial intelligence (AI) and machine learning (ML) often have complex algorithms, but Python’s simplicity enables one to create reliable systems. It is straightforward and easy to understand, and hence easy to learn. 

  • Independence of platform

This is a binary platform and an independent programming language. Python can run on various platforms and different software architectures. One can write the program, compile and then run it on different platforms. Python runs on various platforms like Macintosh, Windows, Linux and macOS. Integrating other languages like C++, Java and R, with Python is also very easy.

  • Strong community support

Python has very strong community support, although it is a language that is open source. It is free and has a large number of very useful tools and libraries. Developers can also discuss their problems in forums or chat with other developers to find some solutions. Python also has corporate support with companies like Google, Instagram, Facebook, Quora and Netflix.

  • Great data visualisation

The presentation of data is extremely important in machine learning and data science. Python has been really helpful in presenting specific data in a human-readable format. Python libraries have great data visualisation tools, which help set up the data, figures, parameters and plotting. These kinds of libraries help present the data in different forms, like histograms, images, line plots, contouring and pseudocolour, three-dimensional plotting, and multiple subplots and paths.

But, why Python?

Python is one of the foundational languages in machine learning. However, the projects are different from a typical software project, which signifies that deep knowledge of the subject is required. The crux of having a career in machine learning is by knowing Python because it is both flexible and stable. 

It is very important to have the right sets of libraries and an environment well-structured for developers looking forward to solving programming challenges. This is where the pre-written sets of libraries have a huge part in helping them with the sets of frameworks and libraries to choose from, for example:

  • Keras - Used for deep learning and machine learning models
  • NumPy - Used for data manipulation and data cleaning
  • Scikit-learn - Used for data modelling
  • OpenCV - Used for image processing
  • Seaborn - Used for data visualisation
  • Caffe - Used for image processing purposes

It becomes easier for developers to create a product faster with these solutions. More so, the team of developers need not waste time searching for libraries which suit their project the best. The use of an existing library is the best for implementing further changes.

It is also observed that around 150,000 online repositories have packages which are custom built for Python. For example, Python libraries like NumPy, Matplotib and SciPy can be easily installed in programmes that run on the language.

Conclusion

The implementation of Python in different kinds of machine learning projects and various other tasks has made the work easier for data scientists, machine learning engineers and developers. Python can be used to compose the available data and analyse that, which makes it the most popular programming language in data science. The IIT Roorkee data science and machine learning course offered by Imarticus is an excellent programme that helps you to start a career in machine learning with an intricate knowledge of Python. 

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