data science


AI Year in Review: Highlights of Papers from IBM Research in 2019

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January 17, 2020 Written by: John R. Smith IBM Research has a long history as a leader in the field of Artificial Intelligence (AI). IBM's pioneering work in AI dates back to the field's inception in the 1950s, when IBM developed one of the first instances of machine learning, which was applied to the game of checkers. Since then, IBM has been responsible for achieving major milestones in AI, ranging from Deep Blue – the first chess-playing computer to defeat a reigning world champion, to Watson – the first natural language question and answering system able to win at Jeopardy!, to last year's Project Debater – the first AI system that can build persuasive arguments on its own and effectively engage in debates on complex topics. IBM's leadership in AI continued in earnest in 2019, which was notable for a growing focus on critical topics such as making trustworthy AI work in practice, creating new AI engineering paradigms to scale AI for a broader use, and continuing to advance core AI capabilities in language, speech, vision, knowledge & reasoning, human-centered AI, and more. While recent years have seen incredible progress in "narrow AI," built on technologies like deep learning, IBM Research pushed its AI research in 2019 towards developing a new foundational underpinning of AI for enterprise applications by addressing important problems like learning more from less, enabling trusted AI by ensuring the fairness, explainability, adversarial robustness, and transparency of AI systems, and integrating learning and reasoning as a way to understand more in order to do more.


Where will data science and audience insights take us in 2020?

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This will make this area of data science even more commonplace not only among top tech companies, but also small and medium-sized businesses across various verticals. However, one aspect which is potentially underrated when looking at the big trends, in terms of the future of data science, is around language frameworks used to make the everyday data science tasks possible. Today, there are two major frameworks, R or Python (or in more pragmatic data science circles, both!). One is praised for having the most beautifully designed data wrangling syntax and plotting libraries, the other for its expressiveness and having the best deep learning libraries available today. However, both suffer from being relatively slow as they're higher level languages.


I had no idea how to write code two years ago. Now I'm an AI engineer.

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Two years ago, I graduated college where I studied Economics and Finance. I was all set for a career in finance. Investment Banking and Global Markets -- those were the dream jobs. Months into the job, I picked up some Excel VBA and learnt how to use Tableau, Power BI and UiPath (a Robotics Process Automation software). I realized I was more interested in picking up these tools and learning to code rather than learning about banking products.


Complete Machine Learning and Data Science: Zero to Mastery

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Complete Machine Learning and Data Science: Zero to Mastery Get udemy course coupon code Learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence) and Python with Tensorflow, Pandas & more! What you'll learn Become a Data Scientist and get hired Master Machine Learning and use it on the job Deep Learning, Transfer Learning and Neural Networks using the latest Tensorflow 2.0 Use modern tools that big tech companies like Google, Apple, Amazon and Facebook use Present Data Science projects to management and stakeholders Learn which Machine Learning model to choose for each type of problem Real life case studies and projects to understand how things are done in the real world Learn best practices when it comes to Data Science Workflow Implement Machine Learning algorithms Learn how to program in Python using the latest Python 3 How to improve your Machine Learning Models Learn to pre process data, clean data, and analyze large data. Build a portfolio of work to have on your resume Developer Environment setup for Data Science and Machine Learning Supervised and Unsupervised Learning Machine Learning on Time Series data Explore large datasets using data visualization tools like Matplotlib and Seaborn Explore large datasets and wrangle data using Pandas Learn NumPy and how it is used in Machine Learning A portfolio of Data Science and Machine Learning projects to apply for jobs in the industry with all code and notebooks provided Learn to use the popular library Scikit-learn in your projects Learn about Data Engineering and how tools like Hadoop, Spark and Kafka are used in the industry Learn to perform Classification and Regression modelling Learn how to apply Transfer Learning Description Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 180,000 developers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. This is a brand new Machine Learning and Data Science course just launched January 2020!


Complete Machine Learning and Data Science: Zero to Mastery

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Complete Machine Learning and Data Science: Zero to Mastery Get udemy course coupon code Learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence) and Python with Tensorflow, Pandas & more! What you'll learn Become a Data Scientist and get hired Master Machine Learning and use it on the job Deep Learning, Transfer Learning and Neural Networks using the latest Tensorflow 2.0 Use modern tools that big tech companies like Google, Apple, Amazon and Facebook use Present Data Science projects to management and stakeholders Learn which Machine Learning model to choose for each type of problem Real life case studies and projects to understand how things are done in the real world Learn best practices when it comes to Data Science Workflow Implement Machine Learning algorithms Learn how to program in Python using the latest Python 3 How to improve your Machine Learning Models Learn to pre process data, clean data, and analyze large data. Build a portfolio of work to have on your resume Developer Environment setup for Data Science and Machine Learning Supervised and Unsupervised Learning Machine Learning on Time Series data Explore large datasets using data visualization tools like Matplotlib and Seaborn Explore large datasets and wrangle data using Pandas Learn NumPy and how it is used in Machine Learning A portfolio of Data Science and Machine Learning projects to apply for jobs in the industry with all code and notebooks provided Learn to use the popular library Scikit-learn in your projects Learn about Data Engineering and how tools like Hadoop, Spark and Kafka are used in the industry Learn to perform Classification and Regression modelling Learn how to apply Transfer Learning Description Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 180,000 developers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. This is a brand new Machine Learning and Data Science course just launched January 2020!


Fake Third-Party Python Libraries Are Stealing Information

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Python removed two fake libraries from Python Package Index (PyPI) after a German developer, Lukas Martini, reported about the packages stealing critical information. Python was released almost three decades ago, but it was only embraced in the last few years due to the increase in artificial intelligence and data science-based third-party libraries. However, these very libraries can become the prime reason for Python's downfall. This is the third time Python org witnessed infiltration and extracting information -- the other three occurred in July 2019, October 2018, and September 2017. Typosquatting – a form of cybersquatting technique that takes advantage typos made by users to hack into information – was used for deceiving and getting access to sensitive data.


Top 50 Free Udemy Courses

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Description: Understand the Theory of how Chatbots work and implement them in Python and PyTorch! Description: This course is for all those people who wants to get a brief idea on Tensorflow.JS in 2020


Machine Learning Building KNN Model Eduonix

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This Video will help you build a KNN model, we will work on a cancel cell Data set, In pattern recognition, the k-nearest neighbors algorithm is a non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space Get flat 15% OFF on the above complete course with other projects here with certification - http://bit.ly/2TwTcxh Get 10% flat off on the Below full E-Degree with certification - (APPLY COPOUN - YTDEG) The Best courses to do with Eduonix with are - 1.Learn Machine Learning By Building Projects - http://bit.ly/2MxMSSl 2.The Complete Web Development Course - Build 15 Projects - http://bit.ly/32Ah9oW 3.The Full Stack Web Development - http://bit.ly/2MZDBRV 4.Projects In Laravel: Learn Laravel Building 10 Projects - http://bit.ly/2MAiHtH 5.Mathematical Foundation For Machine Learning and AI - http://bit.ly/2N23Eb1 Get 15% flat off on the below courses with certification - (APPLY COPOUN - YTEDU) Python Programming An Expert Guide on Python - http://bit.ly/2Bp75Dj Get 10% flat off on the Below full E-Degree with certification - (APPLY COPOUN - YTDEG) AI & ML E-degree- http://bit.ly/2mEUCYC


New UAE-based institute to boost students' Artificial Intelligence skills

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A new institute dedicated to teaching Artificial Intelligence (AI) applications to university students has been launched in Abu Dhabi on Monday (July 15). This is the first-of-its-kind-institute in the UAE will also train government and industries in AI science and applications. With a Dh160 million five-year-fund for AI projects, Khalifa University of Science and Technology launched the Artificial Intelligence and Intelligent Systems Institute (AI Institute) which will focus on AI, data science, robotics, next generation networks, semiconductor technologies and cybersecurity. The AI Institute will bring all the university's research in robotics, artificial intelligence (AI), cyber-security, data science and information and communication technologies under a single umbrella. "Khalifa University's AI Institute, a single umbrella that gathers activities of six research centres, reflects our commitment to research in next generation digital technologies that are priority areas for the UAE's economy," Dr Arif Sultan Al Hammadi, executive vice-president of Khalifa University of Science and Technology said during the launch of the AI Institute.


Data Science Crash Course 3/10: Linear Algebra and Statistics

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This is the third instalment of Data Science Crash Course and today we're going to review mathematics needed for Data Science. Linear algebra is all about manipulations with vectors and matrices. It's both notation and useful way of manipulating object. You can perform operations on vectors like adding by adding each respective term -- they need to have the same length. You can multiply a vector by a scalar, that is a real number, by multiplying each of the entries by this real number.