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10 Best Machine Learning Online Courses & Certifications You Must Know in 2021

#artificialintelligence

The machine learning field is quite interesting and is constantly evolving. In the modern world, you will find its application in every aspect of your lives starting from Facebook feed to Google Maps for navigation and so on. It is a subfield of artificial intelligence and involves learning computer algorithms that improve automatically through experience. Its demand is gradually rising because it can make high-value predictions to guide better decisions and smart actions in real-time without human intervention. So, to benefit our readers, we have created a comprehensive list of the best online machine learning courses and certifications from the leading educational platforms and renowned universities.


The Top 100 Software Companies of 2021

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The Software Report is pleased to announce The Top 100 Software Companies of 2021. This year's awardee list is comprised of a wide range of companies from the most well-known such as Microsoft, Adobe, and Salesforce to the relatively newer but rapidly growing - Qualtrics, Atlassian, and Asana. A good number of awardees may be new names to some but that should be no surprise given software has always been an industry of startups that seemingly came out of nowhere to create and dominate a new space. Software has become the backbone of our economy. From large enterprises to small businesses, most all rely on software whether for accounting, marketing, sales, supply chain, or a myriad of other functions. Software has become the dominant industry of our time and as such, we place a significance on highlighting the best companies leading the industry forward. The following awardees were nominated and selected based on a thorough evaluation process. Among the key criteria considered were ...


How to start your career as a programmer in artificial intelligence?

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In the last decade, the demand for artificial intelligence programmers has increased exponentially, both in Mexico and throughout the world. According to Gartner, sectors such as energy, retail, financial services, telecommunications and manufacturing, are the most predisposed to take advantage of artificial intelligence in Mexico. Precisely Donald Feinberg, research director at Gartner, specializing in the area of artificial intelligence (AI), assures that in the country this field of information technology is reaching a very important role, as important as the one it already has in the United States. However, according to the National Institute of Statistics and Geography (INEGI), in the country there are 976 thousand people trained in computing or information and communication technologies, of which 241 thousand, at least, do not have a related job to the race. For this reason, it is becoming increasingly necessary to carry out training, through which the knowledge and skills required by emerging technologies, such as artificial intelligence, are obtained, and thus be able to aspire to the jobs offered by different companies.


How AI Is Accelerating Business Growth and Innovation

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Despite the many ominous connotations trumpeted in works of fiction, the adoption and growth of AI can is simply another phase of the technological advance that has marked the development of human society. Yet, because we associate intelligence with living creatures, particularly our own species, the idea of machines that possess that faculty excites some trepidation. AI agents may turn out to be as unpredictable and perverse as any intelligent human. No such worry is evident in Silicon Valley. Sundar Pichai, Google's chief, speaking at the World Economic Forum in Davos, Switzerland, enthused about the technology: "AI is probably the most important thing humanity has ever worked on. I think of it as something more profound than electricity or fire," he said. Google is a major participant in an AI market that is clipping along at a five-year compound annual growth rate (CAGR) of 17.5%. Globally, the industry is projected to swell to $554.3 billion by 2024. Other players of note are IBM, Intuit, Microsoft, OpenText, Palantir, SAS, and Slack.


Confronting Structural Inequities in AI for Education

arXiv.org Artificial Intelligence

Educational technologies, and the systems of schooling in which they are deployed, enact particular ideologies about what is important to know and how learners should learn. As artificial intelligence technologies -- in education and beyond -- have led to inequitable outcomes for marginalized communities, various approaches have been developed to evaluate and mitigate AI systems' disparate impact. However, we argue in this paper that the dominant paradigm of evaluating fairness on the basis of performance disparities in AI models is inadequate for confronting the structural inequities that educational AI systems (re)produce. We draw on a lens of structural injustice informed by critical theory and Black feminist scholarship to critically interrogate several widely-studied and widely-adopted categories of educational AI systems and demonstrate how educational AI technologies are bound up in and reproduce historical legacies of structural injustice and inequity, regardless of the parity of their models' performance. We close with alternative visions for a more equitable future for educational AI research.


Equity and Artificial Intelligence in Education: Will "AIEd" Amplify or Alleviate Inequities in Education?

arXiv.org Artificial Intelligence

INTRODUCTION With increasing awareness of the societal risks of algorithmic bias and encroaching automation, issues of fairness, accountability, and transparency in data-driven AI systems have received growing academic attention in multiple high-stakes contexts, including healthcare, loan-granting, and hiring (e.g., Barocas & Selbst, 2016; Holstein, Wortman Vaughan, Daumé III, Dudik, & Wallach, 2019; Veale, Van Kleek, & Binns, 2018). Given these noble intentions, why might AIEd systems have inequitable impacts? In this chapter, we ask whether AIEd systems will ultimately serve to A mplify I nequities in Ed ucation, or alternatively, whether they will help to A lleviate existing inequities. We discuss four lenses that can be used to examine how and why AIEd systems risk amplifying existing inequities: (1) factors inherent to the overall socio-technical system design; (2) the use of datasets that reflect historical inequities; (3) factors inherent to the underlying algorithms used to drive machine learning and automated decision-making, and (4) factors that emerge through a complex interplay between automated and human decision-making. Building from these lenses, we then outline possible paths towards more equitable futures for AIEd, while highlighting debates surrounding each proposal. In doing so, we hope to provoke new conversations around the design of equitable AIEd, and to push ongoing conversations in the field forward. PATHWAYS TOWARD INEQUITY IN AIED We begin by presenting four lenses to understand how AIEd systems might amplify existing inequities or even create new ones (cf. While each lens provides a different way of examining pathways towards inequity in AIEd, all are pointed at the same underlying socio-technical system. Figure 1 provides a coarse-grained overview of the broader social-technical systems in which AIEd systems are embedded, and some of the components we will refer to in the four lenses. The accumulated, collective decisions of designers, researchers, policy-makers, and other stakeholders shape these systems' designs. In addition to using or being affected by AIEd systems, on-the-ground stakeholders such as students, teachers, or school administrators may also play a role in shaping their designs; whether directly, through participatory design processes, or indirectly through the passive generation of training data while interacting with an AIEd interface. In turn, decisions regarding what data is used to shape an AIEd system's design (e.g., when used as training data for use with machine learning methods) can shape an AIEd system's algorithmic behavior (e.g., instructional policies learned from data).


How is AI Contributing to the Education Sector?

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Artificial intelligence has entered every industry, and the educational sector is no exception. The administrative staff, management, teachers, and students are all using AI in different ways to achieve similar goals. During the last few years, AI has spread its roots much wider and deeper in this sector. Markets and Markets has predicted that the global market share of AI in education is estimated to reach $3.68 billion by 2023 at a CAGR (Compound Annual Growth Rate) of 47%. Another platform, Market Search Engine, has predicted that the share will reach $5.80 billion by 2025.


10 free online writing courses for getting real good at words

Mashable

Writing is a much-prized skill and a difficult one to master and, while some are naturally gifted in stringing sentences together, we all need to take the time to learn the craft. Whether you want to write your first novel, pen a poignant poem, pull together a screenplay, or create better business content, there is a free, online course out there to help. We've rounded up a list of free, online writing courses so you can find the perfect program of study to help you write gooderer. This eight-week online writing course is an introduction to the theory and practice of rhetoric, the art of persuasive writing and speech. Using selected speeches from prominent 20th-century Americans -- including Martin Luther King Jr., John F. Kennedy, Margaret Chase Smith, and Ronald Reagan -- to explore and analyze rhetorical structure and style, this course will teach you when and how to employ a variety of rhetorical devices in writing and speaking.


100 Best Coursera Courses & Specializations 2021

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Are you looking for Best Free Coursera Courses 2021? You can earn a Coursera Certificate with Coursera free courses by applying for Coursera scholarship and by doing Coursera paid courses. You are going to get a 7-day free trial on Coursera when you join and start your very first subscription to do a Coursera Specializations for free. If you do not cancel your free trial you will be automatically transferred to paid subscription on the 8th Day. You can continue your Coursera Classes either by using Coursera App on mobile or on any other devices. This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition. Learn and launch your career in Data Science with these best Coursera courses. A nine-course introduction to data science developed and taught by leading instructors. Develop programs to gather, clean, analyze, and visualize data. You will get new insights into your data. Learn to apply data science methods and techniques, and acquire analytical skills. This program is designed to take beginner learners to job readiness in about eight months. Design, develop and manage cloud solutions to drive business objectives. Learn to solve real business problems. Master Excel to add a highly valuable asset to your employability portfolio.


Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data

arXiv.org Machine Learning

Datasets play a critical role in shaping the perception of performance and progress in machine learning (ML)--the way we collect, process, and analyze data affects the way we benchmark success and form new research agendas (Paullada et al., 2020; Dotan & Milli, 2020). A growing appreciation of this determinative role of datasets has sparked a concomitant concern that standard datasets used for training and evaluating ML models lack diversity along significant dimensions, for example, geography, gender, and skin type (Shankar et al., 2017; Buolamwini & Gebru, 2018). Lack of diversity in evaluation data can obfuscate disparate performance when evaluating based on aggregate accuracy (Buolamwini & Gebru, 2018). Lack of diversity in training data can limit the extent to which learned models can adequately apply to all portions of a population, a concern highlighted in recent work in the medical domain (Habib et al., 2019; Hofmanninger et al., 2020). Our work aims to develop a general unifying perspective on the way that dataset composition affects outcomes of machine learning systems.