The Python programming language shines for data science, machine learning, systems automation, web and API development, and more
Dating from 1991, the Python programming language was considered a gap-filler, a way to write scripts that “automate the boring stuff” (as one popular book on learning Python put it) or to rapidly prototype applications that will be implemented in other languages.
However, over the past few years, Python has emerged as a first-class citizen in modern software development, infrastructure management, and data analysis. It is no longer a back-room utility language, but a major force in web application creation and systems management, and a key driver of the explosion in big data analytics and machine intelligence.
Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processing the massive piles of data generated each day to learning from and taking useful action. Deep neural networks, along with advancements in classical machine learning and scalable general-purpose graphics processing unit (GPU) computing, have become critical components of artificial intelligence, enabling many of these astounding breakthroughs and lowering the barrier to adoption. Python continues to be the most preferred language for scientific computing, data science, and machine learning, boosting both performance and productivity by enabling the use of low-level libraries and clean high-level APIs. This survey offers insight into the field of machine learning with Python, taking a tour through important topics to identify some of the core hardware and software paradigms that have enabled it. We cover 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.
With its core focus on readability, Python is a high-level interpreted programming language, which is widely recognized for being easy to learn, yet still able to harness the power of systems-level programming languages when necessary. Aside from the benefits of the language itself, the community around the available tools and libraries make Python particularly attractive for workloads in data science, machine learning, and scientific computing. According to a recent KDnuggets poll that surveyed 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
Artificial Intelligence (AI) and Machine Learning (ML) are the new black of the IT industry. While discussions over the safety of its development keep escalating, developers expand abilities and capacity of artificial intellect. Today Artificial Intelligence went far beyond science fiction idea. It became a necessity. Being widely used for processing and analyzing huge volumes of data, AI helps to handle the work that cannot be done manually anymore because of its significantly increased volumes and intensity.
For instance, AI is applied in analytics to build predictions that can help people create strong strategies and look for more effective solutions. FinTech applies AI in investment platforms to do market research and predict where to invest funds for bigger profits. The traveling industry uses AI to deliver personalized suggestions or launch chatbots, plus enhance the overall user experience. These examples show that AI and ML are used process loads of data to offer better user experience, more personal and accurate one.