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Post By AdminLast Updated At 2020-06-11
Why python machine learning is trending?

Any tool/ technology does not become trending unless it has great advantages when compares to other tools in the market. This trend of the tool/ technology continues until the new ones replace the existing ones in the market. There are many IT tool combinations that were trending today in the IT market. One such combination is python machine learning. Hence today in this article I would like to share why it is trending today in the IT industry.

But prior to knowing why it is trending, let us initially move with python application in machine learning

According to recent statistics, python has become one of the favorite languages among developers. This coding language suits best from process automation to scripting to web development to machine learning. In simple words, its application is found everywhere in the IT industry. 

Why Machine learning professionals opt Python?

Before going to know why machine learning professionals opt for python, let us first know

python learning Curve:

Many people think that python is a simple and easy-to-learn programming language. This is because it offers great scalability and functionality. This coding language focuses on code readability and is a versatile and well-structured programming language. Moreover, even a newbie can easily understand this coding language.

Since we have gotten a brief idea of the python learning curve, let us proceed further in our discussion. The following were the reason for opting for Python by machine learning professionals.

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The extensive go set of libraries:
libraries refers to the set of functions and routines that were written in a given language. 

Usually, a sold set of libraries helps developers in eliminating code duplications. It means with python libraries, developers can perform complex tasks easily. The python machine learning libraries help researchers to perform the study easily. This study includes mathematical optimization, probability, and statistics. The commonly used python machine learning libraries in machine learning are pandas, NumPy, seaborn, and so on. The following image shows the python libraries

python machine learning | OnlineITGuru

Need for good programming language:

Python is considered the best coding language for machine learning professionals. When compared to the other programming languages like JAVA, R, python suits best in most of the machine learning algorithms. 

simplicity:

python has gained the most important for its high readability and its concise code. Hence this coding language suits best in terms of ease to use and simplicity for novice developers. Multi-stage algorithms and complex workflow are the two pillars of machine learning. Hence machine learning professionals focus more on finding solutions to the problems to achieve the project goal. On a whole, this readability plays a major role in exchanging the project between the teams. Moreover, this simplicity is responsible for the faster development of the code.

Great Support:

The python programming language is developed in an open-source- environment. Besides this programming or coding language has great community support. Hence the developers can easily solve their problems with this huge community.

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Flexibility:

It is the other advantage that this programming language offers. Besides this coding fits best in linking different data structures. And Python IDE's usually check the code errors and alerts the developer. Hence this developer does not require much time for debugging the project.

Algorithm Exploration:

one you have gotten enough idea of python machine learning libraries, it's time to explore Python machine learning algorithms. Some of these machine learning algorithms include linear progression, logistic progression, k-means clustering, etc.

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Easy integration:

It is the most important feature of opting python in many projects. It is responsible for integration with many other languages such as JAVA, C++, and so on. Besides, we can also use this coding language to incorporate the python stack with data scientists' work. And this incorporation increases the efficiency of production.

Easy prototype creation:

As mentioned above, the application of this programming language is vast. one such app is website creation. Moreover, with less coding python allows you to create prototypes and test the concepts quickly. Besides, developing prototypes saves developer type and decreases the company expenditure as well.

platform-independent:

Python programming is not only simple and easy to learn programming language. But this language is also versatile. It means we can use the python coding language for machine learning on different platforms. These platforms include Windows, macOS, Linux, Unix, and so on. And in order to transfer the file from one platform to the other, only a minute change is enough to transfer the file. Moreover, they make use of packages like pyinstaller to make their code running on different platforms.

Python machine learning libraries 2020

There are many libraries available for Python that supports Machine Learning for scientific and analytical computing. Moreover, Python machine learning libraries have become the most preferred language for ML algorithm apps. Python is also useful with many well-known brands like FaceBook, Google, Quora, etc. Also, it is useful with the latest and exciting techs like ML, Robotics, AI, etc. Let us know about these Python Machine Learning libraries and their use.

Numpy

This is a general-purpose array-processing package including extensive and highly complex maths functions. It makes this tool much powerful to process multi-dimension arrays and matrices on large scale. This is much useful in handling random numbers, linear Algebra, etc. Users can describe random data types and can integrate with different databases using Numpy. Also, it works as a multi-dimensional container for any datatype. 

SciPy

It is one of the popular Python Machine Learning libraries that includes various modules for linear algebra, integration, and stats. It is different from the SciPy stack but usually, it is useful to make up the SciPy stack. This is also useful in modifying images. 

Scikit-learn

The most popular Python Machine Learning library useful for classical ML algorithms. This is built on the top of Numpy, and SciPy, python libraries. Moreover, this library supports supervised and unsupervised learning algorithms in ML and also useful in data mining and analysis. This feature makes it a great tool among others to start learning ML programs.

Theano

Theano is one of the popular Python machine learning libraries that works as an optimizing compiler for various activities. These include various maths expressions and matrix calc. changes.  The interface of Theano matches with NumPy as it is built on it. It can work on both architectures- GPU and CPU due to which it yields faster results. 

This tool can perform various data-intensive calculations much faster on GPU than CPU. In 

Further, it eliminates errors and bugs while working with logarithmic and exponential functions. It includes various built-in tools useful for unit-testing and validations. 

Tensorflow

This is one of the Python Machine Learning libraries with an open-source platform built for high-level numerical calculations. It is a framework that includes tensors while defining and running various computations. The tool can train and run various neural networks useful in developing AI apps. Further, it is useful in deep learning research and apps.

Keras

This is also the most powerful and popular Python Machine Learning library that include high-level neural networks API. The framework is capable of running on top of other popular Python libraries like Theano, Tensorflow, etc. It also works on both processing units- GPU and CPU. The tool is a much popular one because it allows easy and faster prototyping. 

This also includes various features to work on images and text images that come easily with Deep NN codes. The library supports recurrent neural networks along with regular neural networks.

PyTorch

It is an open-source/free Python Machine Learning library based on the Torch platform, an ML library. This library includes a great choice of tools and libraries that support system vision, NLP, and other various ML programs. It has multi-GPU support, robust and simple processors, and manipulated data loaders.

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Pandas

This is the most useful Python Machine Learning Library useful for data analysis. It is not directly associated with ML but useful in data extraction and preparation. This lit provides different types of high-level data structures and tools for data analysis purposes. It involves grouping, filtering, and combining data from different sources. Its fast, flexible data structures are enough strong to work on relational or labeled data. It involves solving real-time data analysis within Python coding. 

There are two different data structures that Pandas use- Series and DataFrame. Both together works on managing critical data needs. Also, it deals with different use cases of various sectors like social, science, finance, and engineering, etc. 

In this way, we can define different Python Machine learning libraries that are useful in every aspect of learning python. Python is used in ML due to its ease of learning and flexibility to use with different dimensions. It also makes data validation much faster and makes it error-free to use. Due to the access to world-class libraries, developers also execute complex tasks without much extensive coding. 

Conclusion

All of it makes Python the most popular coding language for the current IT market trends. To learn more insights into Python coding, get into the Python Online Course with the ITGuru platform. People interested in learning ML techniques with Python to break their career in a new way can opt for this. This learning will help you get the latest skills on python and its real-time experience.

So likewise, there are many other reasons to opt for python as the major programming language in machine learning. On the other hand, the other coding languages don't have the capability to provide this rich set of features. In other words, since there are no other coding languages to replace python in machine learning, python machine learning is a hot cake today in the IT industry.