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Data Science using Python Tutorials

Machine Learning Data Science and Deep Learning with Python

Business intelligence is gaining more attention than ever these days. The BI field is huge, including Machine Learning, Artificial Intelligence, Data Science, and other things. One major common thing that plays a deciding factor is the programming language to perform all these activities. In the current market, there are multiple programming languages available. Still, only a few languages support extremely well for Data Science and Machine Learning when it comes to the best programming languages. Python is one of the main languages which is used by many developers across the world. So, let us understand why Python is best for data scientists and machine learning developers. 

Advantages of Python for Data Science and Machine Learning


One of the first things that strike developers’ minds when they hear the term Python is simplicity. The easy to understand and readable code and short syntax is very useful for Data Science and Machine Learning process. ML’s main objective is to reduce human effort, and Python reduces human efforts in the coding stage. The simplicity and consistency of Python make it easy for even beginner developers. In Machine Learning, Python’s usage helps to focus on solving logical problems rather than spending time on the nitty-gritty of programming language. This is one of the primary reasons developers agree that data science and machine learning with python is the best choice. 

Vast library

The simplicity first attracts all developers from beginners to advanced developers, but the extensive library and other functions make the developers keep using this language. Developing and implementing ML is always tricky, and without the support of good tools and libraries. The well-structured library of Python assists in reducing the time required to write code. It has a separate library set only for Artificial Intelligence & Machine Learning with a rich technology stack. The tools such as TensorFlow, Scikit-Learn, and Keras are great for Machine Learning, wherein Scipy, Seaborn, Pandas, and NumPy are great for data visualizations. These are just a few popular names apart from this; you have Shogun, PyMC, PyLearn2, SymPy, and many other useful libraries for making data science and ML processes efficient. 

Platform independence

Python is a platform-independent programming language. In simple words, it allows the developers to develop and implement things on one device and use the same code on any other platform without changing any code. This is one of the coolest features of Python that brings a lot of fame for it. Python supports all popular platforms, including Windows, Mac, and Linux operating systems. You can use Python code to create standalone executable programs for popular platforms. That means Python can be easily distributed on any operating system without using a Python interpreter. Additionally, developers use GPUs to train their machine learning models, and the platform independence feature of Python makes the training process easier and cheaper. 

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Great community support

Python is in the programming world for many years, and it is finding its place in the top ten programming languages for all these years. Due to this, people are learning python basics for data science and machine learning. With the increasing popularity, the community of Python is also increasing with each passing day. That means if you stuck with any problem while writing code, you can get support from this community. It is guaranteed that you will get the person who knows your problem in the vast community. 

Enterprise Application Integration

When it comes to Data Science, EAI plays an important role. Python language is a great tool for  Enterprise Application Integration. Python has numerous advantages, and it is highly embeddable with applications. The embedding is possible even the application is developed using other programming languages. Hence the integration of Python with other languages is easy, making the web development job easier. For example, it can invoke COM/COBRA components, and you can directly call from and to C++, C, or Java code. The strong integration of Python with other languages makes it perfect for application scripting. The features, such as strong support for text processing & integration features, make this platform the best for the software testing field. 

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