Artificial Intelligence upbeats the HR Recruitment Process

  • Artificial Intelligence for the recruiting is a new addition and also problem-solving and learning is displayed using this tool in the recruitment process.
  • Recruitment is integrated with Artificial Intelligence tool, the process becomes consistent, smooth and structured and saves lot of time and can cover large number of candidates.With the help of Automation, we can streamline the selection process with various intervention like screening questionnaires for candidates, managing the hiring process and competency-based feedback forms that can be discussed during each interview.
  • In the recruitment process, the role of Artificial Intelligence is not limited but to find right candidate with a right set of skills and meeting the expectation of an organisation.These tools and techniques have increased the efforts in the recruitment process by researching the data and enables insight.
  • Artificial Intelligence tools have built -in capabilities to reduce the cost and time spent for the recruitment team and easily get the specific skilled potential candidates for an organisation.
  • Talent mapping is definitely succeeding pace in the recruitment process, where recruiters can easily find out the potential candidates in advance and can draft a strategic plan for hiring them in advance.

Artificial Intelligence for recruiting is designed to automate the recruitment flow by reducing repetitive and high volume tasks.
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Partner Opportunities in Data Security

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  • Additionally, we announced a new promotion specifically for partners reselling our Data Security solutions.
  • Informatica offers solutions to help partners help their customers with both “Detect” (Discovery and Classification) and “Protect” (Data Masking, Encryption or other 3rd party tools)

    Best of all, Informatica is relying on you, our partners, to drive this strategy within our joint customer base.

  • Additionally, on the May Partner Pulse Webcast, we launched our Data Security Promotion for partners!
  • This includes additional front-end and back-end margin and rebates for partners who identify and close data security opportunities.
  • You can listen to the replay of this webcast here and get the details of our Data Security promotion and solution portfolio on PARC, our partner portal.

Partner Opportunities in Data Security- We’ve got the enablement materials, sales tools and marketing programs to help get you started.
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Scikit-Learn Cheat Sheet: Python Machine Learning (Article)

Scikit-learn cheat sheet: #machinelearning with #Python -

  • Most of you who are learning data science with Python will have definitely heard already about , the open source Python library that implements a wide variety of machine learning, preprocessing, cross-validation and visualization algorithms with the help of a unified interface.
  • If you’re still quite new to the field, you should be aware that machine learning, and thus also this Python library, belong to the must-knows for every aspiring data scientist.
  • This  cheat sheet will introduce you to the basic steps that you need to go through to implement machine learning algorithms successfully: you’ll see how to load in your data, how to preprocess it, how to create your own model to which you can fit your data and predict target labels, how to validate your model and how to tune it further to improve its performance.
  • Scikit-learn is an open source Python library that implements a range of machine learning, preprocessing, cross-validation and visualization algorithms using a unified interface.
  • >>> from sklearn import neighbors, datasets, preprocessing >>> from sklearn.model_selection import train_test_split >>> from sklearn.metrics import accuracy_score >>> iris = datasets.load_iris() >>> X, y = iris.data[:, :2], iris.target >>> X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33) >>> scaler = >>> X_train = scaler.transform(X_train) >>> X_test = scaler.transform(X_test) >>> knn = >>> knn.fit(X_train, y_train) >>> y_pred = knn.predict(X_test) >>> accuracy_score(y_test, y_pred) Your data needs to be numeric and stored as NumPy arrays or SciPy sparse matrices.

A handy scikit-learn cheat sheet to machine learning with Python, including code examples.
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IBM Bets The Company On Cloud, AI, And Blockchain

IBM Bets The Company On #Cloud, #AI, And #Blockchain by @theebizwizard   #IBMInterConnect

  • IBM has dubbed its AI offering Watson – a powerful cognitive computing engine that pervades everything IBM is bringing to customers.
  • David Kenny, Senior Vice President of IBM Watson and Cloud Platform, laid out IBM’s strategic ONE Architecture consisting of four layers: Cloud, Data AI, and Applications, as the image below shows.
  • The ONE Architecture is remarkable in two ways: first, AI (in the form of Watson) represents an entire layer, indicating the breadth of IBM’s bet on the technology.
  • What, then, does Watson truly bring to customers of the IBM Cloud?
  • This enormous commitment of person-hours from highly qualified professionals as well as vast quantities of data makes Watson more of a consultant’s tool, best suited for selling the time of IBM consultants, more so than a modular, LEGO-block style plug-in for customers to incorporate directly into their own applications.

It’s clear that Rometty has taken the lesson of the Innovator’s Dilemma to heart, and is willing to jeopardize existing revenue streams in order to place large bets on innovation. The big picture here, of course, is business transformation – most notably for IBM, but also for its customers.
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Artificial Intelligence Will Not Replace Lawyers With IQ And EQ

#ArtificialIntelligence Will Not Replace Lawyers With IQ And EQ

  • Many lawyers have elevated IQ’s, though relatively few seem to possess high EQ’s– commonly called ‘people skills’.
  • Artificial intelligence (AI), a recent entrant in the legal vertical, scores high on IQ, but the jury is still out on whether machines can develop comparable EQ.
  • How is human intelligence—IQ and EQ– applied to legal practice, and what functions require specialized training and social skills that cannot be performed by machines?
  • Put another way, what are the core functions that lawyers perform, and what attributes differentiate effective human lawyers from machine ones?
  • And while some of the rote chores that support the work they do—legal research, discovery production review, statistical analysis—can certainly be performed by machines—only human lawyers can synthesize it and communicate it to others in a way that evokes confidence (“I’m sure glad she’s my lawyer!)

Analytical prowess (IQ), people skills (EQ) and artificial intelligence (AI) are three kinds of intelligence in the legal marketplace. Who is best suited for what tasks is largely driven by which resource–human or machine has the necessary form of intelligence. This article examines the three.
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Google’s new machine learning API recognizes objects in videos

Google’s new machine learning API recognizes objects in videos

  • At its Cloud Next conference in San Francisco, Google today announced the launch of a new machine learning API for automatically recognizing objects in videos and making them searchable.
  • The new Video Intelligence API will allow developers to build applications that can automatically extract entities from a video.
  • Until now, most similar image recognition APIs available in the cloud only focused on doing this for still images, but with the help of this new API, developers will be able to build applications that let users search and discover information in videos.
  • Besides extracting metadata, the API allows you to tag scene changes in a video.

Google’s new machine learning API recognizes objects in videos
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Scikit-Learn Cheat Sheet: Python Machine Learning

Scikit-learn cheat sheet: #machinelearning with #Python -

  • Most of you who are learning data science with Python will have definitely heard already about , the open source Python library that implements a wide variety of machine learning, preprocessing, cross-validation and visualization algorithms with the help of a unified interface.
  • If you’re still quite new to the field, you should be aware that machine learning, and thus also this Python library, belong to the must-knows for every aspiring data scientist.
  • This  cheat sheet will introduce you to the basic steps that you need to go through to implement machine learning algorithms successfully: you’ll see how to load in your data, how to preprocess it, how to create your own model to which you can fit your data and predict target labels, how to validate your model and how to tune it further to improve its performance.
  • In short, this cheat sheet will kickstart your data science projects: with the help of code examples, you’ll have created, validated and tuned your machine learning models in no time.
  • In addition, you’ll make use of Python’s data visualization library  to visualize your results.

A handy scikit-learn cheat sheet to machine learning with Python, including code examples.
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