Original article can be found here (source): Artificial Intelligence on Medium
Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence
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Python is a major programming language today widely recognized for being versatile, easy to learn, fast to develop, and it continues to be the most widely-used language for data science, machine learning, and scientific computing.
According to a recent survey done by KDnuggets more than 1800 participants for preferences in analytics, data science, and machine learning, Python maintained its position at the top of the most widely used language in 2019.
This article is a summary to introduce a recent research paper regarding machine learning developments and trends in Python.
Machine Learning in Python: A Survey
A group of researchers did a study that is geared to enrich readers with an introduction to the most relevant topics and trends that are prevalent in the current landscape of machine learning in Python.
Their work provides insight into the field of machine learning with Python and takes the reader through important topics to identify some of the core hardware and software paradigms that have enabled the language. The research covers widely-used libraries and concepts, collected together for holistic comparison, with the goal of educating the reader and driving the field of Python machine learning forward.
In the paper, researchers also summarize some of the significant challenges, taxonomies, and approaches. Throughout the study, they aim to find a fair balance between both academic research and industry topics, while also highlighting the most relevant tools and software libraries.
Potential Uses and Effects
Being a language that is easy to learn and use, Python has evolved to become popular in many research and application areas. The language is expected to stay the dominant language for scientific computers for many years to come, enabled by advancements in CPU and GPU computing and ever-growing user Python communities.
For researchers, practitioners in the field, the survey covers widely-used libraries and concepts, collected together for holistic comparison, with the goal of educating the reader and driving the field of Python machine learning forward.
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