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Difference Between Coding in Data Science and Machine Learning

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These high-level languages can generate code that is unaffected by the type of computer. Furthermore, they are portable, more human-like in appearance, and extremely valuable for problem-solving instructions. However, many data scientists choose to use high-level coding languages to deal with their data. Those interested in entering the subject might consider focusing on a data science language as a starting point. Machine learning is applied through coding, and coders who know how to write that code will have a better understanding of how the algorithms function and will be able to more effectively monitor and improve them.


Test Automation Robot Framework with Python - Selenium Tests

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Created by Rahul Shetty 8.5 hours on-demand video course Your struggle on designing Test Automation Frameworks ends here. Presenting you the only framework in the Market which is faster and easy to design with very less code. This Framework consists of all the features what (Cucumber TestNG) provides. This Framework by default comes with many Libraries which helps to build automation tests without writing much boilerplate code. Additionally you also have ability to build your custom Libraries with Python code.


Intel open-sources ControlFlag tool to find errors in code

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Intel Labs has big plans for a software tool called ControlFlag that uses artificial intelligence to scan through code and pick out errors. One of those goals, perhaps way out in the future, is to bake it into chip packages as a last line of defense against faulty code. This could make the information flow on communications channels safer and efficient. Last week Intel open-sourced the tool – dubbed ControlFlag – to software developers. The software pores over lines of code and points out errors that developers can then fix. The company ran ControlFlag on a proprietary piece of internal production-quality software with millions of lines of code.


Top 10 Amazing Python Developers to Follow in 2021

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Python is one of the most widely used programming languages in the world, and for good reason. Because of its vast libraries and flexible structure, it's simple to learn, has consistent and easy-to-parse syntax, and is utilized for artificial intelligence applications. The platform's spectacular ascent has sparked a devoted community, fueled in no little part by its adoption by big companies such as DropBox, Reddit, and Instagram, to name a few. Check out this list of Python developers to follow if you're seeking Python programmers who are leading the charge. The people on this list have solid technical credentials, are constantly adding new and interesting features to the platform, and have a strong social media presence.


Top 10 Amazing Python Developers to Follow in 2021

#artificialintelligence

Python is one of the most widely used programming languages in the world, and for good reason. Because of its vast libraries and flexible structure, it's simple to learn, has consistent and easy-to-parse syntax, and is utilized for artificial intelligence applications. The platform's spectacular ascent has sparked a devoted community, fueled in no little part by its adoption by big companies such as DropBox, Reddit, and Instagram, to name a few. Check out this list of Python developers to follow if you're seeking Python programmers who are leading the charge. The people on this list have solid technical credentials, are constantly adding new and interesting features to the platform, and have a strong social media presence.


(Part 2 of 4) How to Modernize Enterprise Data and Analytics Platform - by Alaa Mahjoub, M.Sc. Eng.

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In many cases, for an enterprise to build its digital business technology platform, it must modernize its traditional data and analytics architecture. A modern data and analytics platform should be built on a services-based principles and architecture. This part, provides a conceptual-level reference architecture of a modern D&A platform. Parts 3 and 4, will explain how these two reference architectures can be used to modernize an existing traditional D&A platform. This will be illustrated by providing an example of a Transmission System Operator (TSO) that modernizes its existing traditional D&A platform in order to build a cyber-physical grid for Energy Transition. However, the approaches used in this example can be leveraged as a toolkit to implement similar work in other vertical industries such as transportation, defence, petroleum, water utilities, and so forth.


GitHub - ossu/computer-science: Path to a free self-taught education in Computer Science!

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The OSSU curriculum is a complete education in computer science using online materials. It's for those who want a proper, well-rounded grounding in concepts fundamental to all computing disciplines, and for those who have the discipline, will, and (most importantly!) good habits to obtain this education largely on their own, but with support from a worldwide community of fellow learners. It is designed according to the degree requirements of undergraduate computer science majors, minus general education (non-CS) requirements, as it is assumed most of the people following this curriculum are already educated outside the field of CS. The courses themselves are among the very best in the world, often coming from Harvard, Princeton, MIT, etc., but specifically chosen to meet the following criteria. When no course meets the above criteria, the coursework is supplemented with a book.


Why is Python popular?

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Python is a high-level, general-purpose programming language. It was created to have a syntax that would be easy to read and simple to understand. Python is also used for data science, machine learning, artificial intelligence, and other tasks where one needs a language that handles computation very efficiently. Python is popular because it's not too hard to learn and read. This makes it ideal for beginners or those looking for a way to make their code more efficient without learning another programming language.



Ceph as a Secret Weapon for HPC

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Ceph is open source software defined storage (SDS) designed to provide highly scalable object-, block- and file-based storage under a unified system, setting it apart from other SDS solutions. It allows decoupling data from physical hardware storage, using software abstraction layers, providing scaling and fault management capabilities. As a distributed storage framework, Ceph has typically been used for high bandwidth, medium latency types of applications, such as content delivery, archive storage, or block storage for virtualization. Its inherent scale-out support allows an organization to build large systems as demand increases. Additionally, it supports enterprise-grade features such as erasure coding, thin provisioning, cloning, load-balancing, automated tiering between flash and hard drives, and simplified maintenance and debugging.