The U.S. economy has been rocked by the coronavirus pandemic, with stock values crashing this year during the period between late February and late March, when states began issuing stay-at-home orders. One intrepid digital company saw its stock surge, however--Zoom Video Communications, Inc., which has more than doubled its individual stock price while the economy around it crashed. But why has Zoom become the go-to platform during the pandemic, when there are dozens of other video conferencing services out there? The answer lies in Zoom's intuitive interface, says Mathias Unberath, an assistant professor of computer science at Johns Hopkins Whiting School of Engineering and a member of the Malone Center for Engineering and Healthcare. "Whether someone is hosting a work meeting or a baby shower, the app is easy to use," he says.

Machine Learning, often called Artificial Intelligence or AI, is one of the most exciting areas of technology at the moment. New to machine learning and seeking ways to enhance your knowledge? Or maybe you work in an industry with artificial intelligence and need a machine learning course to position yourself for advancement? Either way, a machine learning Coursera course is worth considering. There are introductory courses to choose from if you're just getting started, or you can begin with intermediate or advanced options to level up your knowledge. Benzinga is here to help you find a course that fits your needs and busy lifestyle.

Benzinga Money is a reader-supported publication. We may earn a commission when you click on links in this article. New to machine learning and seeking ways to enhance your knowledge? Or maybe you work in an industry with artificial intelligence and need a machine learning course to position yourself for advancement? Either way, a machine learning Coursera course is worth considering.

High level Machine Learning lib & tools walk through. Created by Pradeep D English [Auto] Students also bought Python for Data Science and Machine Learning beginners The Complete Android 10 Development Bootcamp Java and kotlin FL Studio 20 - EDM Masterclass Music Production in FL Studio Quantitative Aptitude for CAT XAT GRE GMAT CSAT SSC CGL GATE Angular Material: Ultimate Masterclass With Angular 9 (2020) Gaussian Process Regression for Bayesian Machine Learning Preview this course GET COUPON CODE Description Lets learn basics to transform your career. I promise not to exhaust you with huge number of videos. Welcome to the most comprehensive Python required for Data Science and Machine Learning course! This course of First Step or prerequisite to learn Machine Learning or Data Science.

As the name implies, this course takes a more applied perspective than Andrew Ng's machine learning lecture at Stanford. You will see more code than mathematics. Concepts and algorithms are using the popular Python libraries scikit-learn and Keras.

Voluntary employee turnover incurs significant direct and indirect financial costs to organizations of all sizes. A large proportion of voluntary turnover includes people who frequently move from job to job, known as job-hopping. The ability to discover an applicant's likelihood towards job-hopping can help organizations make informed hiring decisions benefiting both parties. In this work, we show that the language one uses when responding to interview questions related to situational judgment and past behaviour is predictive of their likelihood to job hop. We used responses from over 45,000 job applicants who completed an online chat interview and also self-rated themselves on a job-hopping motive scale to analyse the correlation between the two. We evaluated five different methods of text representation, namely four open-vocabulary approaches (TF-IDF, LDA, Glove word embeddings and Doc2Vec document embeddings) and one closed-vocabulary approach (LIWC). The Glove embeddings provided the best results with a positive correlation of r=0.35 between sequences of words used and the job-hopping likelihood. With further analysis, we also found that there is a positive correlation of r=0.25 between job-hopping likelihood and the HEXACO personality trait Openness to experience. In other words, the more open a candidate is to new experiences, the more likely they are to job hop. The ability to objectively infer a candidate's likelihood towards job hopping presents significant opportunities, especially when assessing candidates with no prior work history. On the other hand, experienced candidates who come across as job hoppers, based purely on their resume, get an opportunity to indicate otherwise.

Complex categorical data is often hierarchically coupled with heterogeneous relationships between attributes and attribute values and the couplings between objects. Such value-to-object couplings are heterogeneous with complementary and inconsistent interactions and distributions. Limited research exists on unlabeled categorical data representations, ignores the heterogeneous and hierarchical couplings, underestimates data characteristics and complexities, and overuses redundant information, etc. The deep representation learning of unlabeled categorical data is challenging, overseeing such value-to-object couplings, complementarity and inconsistency, and requiring large data, disentanglement, and high computational power. This work introduces a shallow but powerful UNsupervised heTerogeneous couplIng lEarning (UNTIE) approach for representing coupled categorical data by untying the interactions between couplings and revealing heterogeneous distributions embedded in each type of couplings. UNTIE is efficiently optimized w.r.t. a kernel k-means objective function for unsupervised representation learning of heterogeneous and hierarchical value-to-object couplings. Theoretical analysis shows that UNTIE can represent categorical data with maximal separability while effectively represent heterogeneous couplings and disclose their roles in categorical data. The UNTIE-learned representations make significant performance improvement against the state-of-the-art categorical representations and deep representation models on 25 categorical data sets with diversified characteristics.

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"This is the best course to learn NLP from the basic. Do you have idea about Which Artificial Intelligence field is going to get big in upcoming year? According to statista dot com which field of AI is predicted to reach $43 billion by 2025? If answer is'Natural Language Processing', You are at right place.

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