Python has relished a steady rise to fame over the last few years and is now jostling for one of the popular programming languages in the world.
It is favoured for applications ranging from development to scripting and process automation. Python is quickly becoming the top preference among developers for artificial Intelligence (AI), deep learning projects and machine learning.
ML has created a good atmosphere of opportunities for application developers. It is also used enormously by companies involved in customer service to drive self-service and improve employee productivity and workflows.
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In this ebook, we will learn what makes Python better for Machine learning. We’ll also take a look at the significant reasons why Python is the go-to programming language for developers working in deep learning and machine learning and why you should consider it for your next ML project.
Before we begin, it would be supportive to understand the dissimilarity between artificial Intelligence, machine learning, and deep learning.
Of course, Python is a choice for developers for a whole host of applications, but what makes it the best fit for projects involving ML?
Extensive selection of frameworks and libraries
One of the features that make Python such a preferable choice, in general, is its abundance of frameworks and libraries that facilitate coding and save time. In addition, machine learning and deep learning are extraordinarily well catered for.
SciPy for advanced computation, and sci-kit-learn for data analysis and data mining, NumPy, used for scientific computation. These are among the popular libraries, working alongside such heavy-hitting frameworks as TensorFlow, Apache Spark, and CNTK. In terms of deep learning and machine learning, these frameworks and libraries are, in essence, Python-first, while some, like PyTorch, are written mainly for Python.
Python and Machine Learning
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