Python For Machine Learning & Data Science

Artificial Intelligence is depicted by a machine that leads to some reasonable solution of a problem. Machine learning takes it further forward by using a set of algorithms to parse and analyze data, and make decisions. Artificial intelligence and machine learning is a big revolution in IT industry. Through these techniques, people can create probabilistic models to look for effective solutions to their problems. Artificial intelligence and machine learning has vast application in different industries like manufacturing, travelling, E-commerce etc. The techniques process loads of data to offer better user experience.

Python for Machine Learning & Data Science

6 Reasons Why Python is best for Machine Learning

Python is a robust and powerful programming language which has its main focus on the rapid application development. Python is continuously growing over the years and is now knocking for the position of one of the most famous programming languages making it the obvious choice for developing applications with machine learning, AI, big data and IOT.

Here is a list of reasons due to which Python is being favored by the developers by a vast host of applications including AI and ML:

Large Set of Libraries

There is an abundance of Python libraries and frameworks that save coding time. Python libraries help you access, handle and transform a massive amount of data. Here is a list of libraries that are being used in AI and ML: • NumPy – For scientific computation • Keran– For building a neural network framework • SciPy– For advanced computation • Scikit-learn – For data mining and data analysis • PyBrain– For neural networks, unsupervised and reinforcement learning. • EasyAI– Simple Python engine for two – players games with AI. • PyBrain– A flexible and effective algorithm for Machine Learning tasks.

Flexibility

Python is a flexible programming language as it offers an option to choose either to use OOPs or scripting. In Python, there is no need to recompile the source code. Developers can implement any changes and see the results. You can also combine Python language to other programming languages to reach your goals. Python has made it easier for developers to use API from an existing program in a different language that make it platform independent.

Readability

Python developer can easily understand the code of their peers, and can make modifications in it. There is no error or conflicting paradigms. This leads to efficient exchange of algorithms and tools between AI and ML professionals.

Community Support

Python has a very strong community support. Being an open-source programming language, there is a bunch of resources open for programmers. A lot of Python documentation is available online where machine learning developers discuss problems and solve them by assisting each other.

Simplicity of Code

Python is easy to learn and implement. It uses English like language with self-explanatory commands with minimum syntactical rules. Many developers consider the syntax of Python like mathematical model that is suitable for ML and AI.

Great Visualization Options

Libraries like MatPlotlib in Python enable data scientists to develop charts, histograms and other plots for better data interpretation and visualization.

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Python Devs on Use Of Python for ML & Data Science

Using Python Language, developers can handle complex challenges due to simple syntax and flexibility. Python offers a number of features that make it an attractive programming platform including stability, portability, object-oriented development, a powerful standard library and a wealth of third-party modules or packages. It has now become easier to find Python developers to work on AI and ML based projects due to its popularity and faster coding time.