I started in Data Science back in 2015. It was not an intended move but the answer to the needs of my employer. I was working for a company providing automation services to Spanish corporations and we had the need to leverage data to automate complex tasks whose rules could not be easily hard-coded. I had recently graduated as an engineer in the middle of a terrible economic crisis, had some statistical modeling knowledge and was proficient using MATLAB. In 2015 there was not specialized Data Science degrees or boot-camps to jump-start in the field (at least, in Spain) and the naturally closest studies you could have were, in this order: Mathematics (in Spain with a strong focus in becoming a teacher/professor in the public education system) or Software Engineer (most of them more interested in App Development or creating the new Uber of "X" than in boring Data Science stuff back then).

Illustrating children's books with too many detailed, non-essential pictures makes it'harder for kids to focus and absorb knowledge', a study has demonstrated. Colourful pictures intended to motivate young readers may achieve the exact opposite by drawing attention away from the story text, US researchers warned. Although reading is considered a'gateway for learning', around 20 per cent of children in the UK do not meet the minimum level of literacy proficiency. Children's books typically include eye-catching illustrations to help readers visualise the characters and setting of the story. However, eye-tracking studies found that too many pictures can prove distracting.

Linear Regression and Logistic Regression for beginners NEW Created by Start-Tech Academy English [Auto] Students also bought Seven to Heaven - HTML5, CSS3 and jQuery Course The complete gRPC course [Protobuf Golang Java] Spanish: The Most Useful Phrases 300 The Complete Java Masterclass: Learn Java From Scratch C Programming for Beginners - Master the C Fundamentals Preview this course GET COUPON CODE Description You're looking for a complete Linear Regression and Logistic Regression course that teaches you everything you need to create a Linear or Logistic Regression model in Python, right? You've found the right Linear Regression course! After completing this course you will be able to: Identify the business problem which can be solved using linear and logistic regression technique of Machine Learning. Create a linear regression and logistic regression model in Python and analyze its result. Confidently model and solve regression and classification problems A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.

My name is Palash Shah, and I'm the author of Libra: a machine learning library that lets you build and train models in one line of code. My journey in the open source community started as a normal college student -- I worked on my library after classes in my dorm room. Quite quickly though, it began to grow into something much bigger than that, going from 0 to close to 2,000 stars in under a month. All of a sudden it was being used at universities like Carnegie Mellon and MIT in several of their machine learning classes. As someone who previously had no professional presence and/or connections in the technical industry, my experience starting this project was unique compared to the rest of the players in this space.

Deep learning approaches to anomaly detection have recently improved the state of the art in detection performance on complex datasets such as large collections of images or text. These results have sparked a renewed interest in the anomaly detection problem and led to the introduction of a great variety of new methods. With the emergence of numerous such methods, including approaches based on generative models, one-class classification, and reconstruction, there is a growing need to bring methods of this field into a systematic and unified perspective. In this review we aim to identify the common underlying principles as well as the assumptions that are often made implicitly by various methods. In particular, we draw connections between classic 'shallow' and novel deep approaches and show how this relation might cross-fertilize or extend both directions. We further provide an empirical assessment of major existing methods that is enriched by the use of recent explainability techniques, and present specific worked-through examples together with practical advice. Finally, we outline critical open challenges and identify specific paths for future research in anomaly detection.

Open domain question answering (OpenQA) tasks have been recently attracting more and more attention from the natural language processing (NLP) community. In this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA, collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and traditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages, respectively. We implement both rule-based and popular neural methods by sequentially combining a document retriever and a machine comprehension model. Through experiments, we find that even the current best method can only achieve 36.7\%, 42.0\%, and 70.1\% of test accuracy on the English, traditional Chinese, and simplified Chinese questions, respectively. We expect MedQA to present great challenges to existing OpenQA systems and hope that it can serve as a platform to promote much stronger OpenQA models from the NLP community in the future.

However, one of the ways professionals are keeping up their relevance in their organisations as well as in the industry is by upskilling and learning the latest tools and technologies of this evolving field. Webinars and workshops have always been an excellent way for professionals and enthusiasts to keep themselves updated with the latest trends and technologies. For attendees, these webinars and workshops are not only an easy way to know and train themselves on the latest tools and technologies but also allows them to hear from the best minds of the industry on relevant topics. In fact, for a few years now, large tech companies have been conducting free webinars and workshops, which will not only boosts the community and users at large but also acts as a great marketing tool for advertising their solutions and services. With machine learning being explored in various industries, including healthcare, eCommerce, finance and retail, the possibilities are endless.

Multicasting in wireless systems is a natural way to exploit the redundancy in user requests in a Content Centric Network. Power control and optimal scheduling can significantly improve the wireless multicast network's performance under fading. However, the model based approaches for power control and scheduling studied earlier are not scalable to large state space or changing system dynamics. In this paper, we use deep reinforcement learning where we use function approximation of the Q-function via a deep neural network to obtain a power control policy that matches the optimal policy for a small network. We show that power control policy can be learnt for reasonably large systems via this approach. Further we use multi-timescale stochastic optimization to maintain the average power constraint. We demonstrate that a slight modification of the learning algorithm allows tracking of time varying system statistics. Finally, we extend the multi-timescale approach to simultaneously learn the optimal queueing strategy along with power control. We demonstrate scalability, tracking and cross layer optimization capabilities of our algorithms via simulations. The proposed multi-timescale approach can be used in general large state space dynamical systems with multiple objectives and constraints, and may be of independent interest.

Classification is a major tool of statistics and machine learning. A classification method first processes a training set of objects with given classes (labels), with the goal of afterward assigning new objects to one of these classes. When running the resulting prediction method on the training data or on test data, it can happen that an object is predicted to lie in a class that differs from its given label. This is sometimes called label bias, and raises the question whether the object was mislabeled. Our goal is to visualize aspects of the data classification to obtain insight. The proposed display reflects to what extent each object's label is (dis)similar to its prediction, how far each object lies from the other objects in its class, and whether some objects lie far from all classes. The display is constructed for discriminant analysis, the k-nearest neighbor classifier, support vector machines, logistic regression, and majority voting. It is illustrated on several benchmark datasets containing images and texts.

New Delhi, September 10, 2020: As part of its ongoing efforts to promote skilling as a national priority, NASSCOM FutureSkills and Microsoft have joined hands to launch a nation-wide AI skilling initiative. The initiative aims to skill 1 million students in AI by 2021. The collaboration will provide Microsoft's AI, machine learning and data science expertise to students through easy to consume modules including live demos, hands on workshops and assignments. These introductory sessions on AI will be available for undergraduate students at no cost and will cover the basics of data science, machine learning models on Azure, and understanding of cognitive services to build intelligent solutions. The partnership with NASSCOM FutureSkills is an extension of Microsoft's global skilling initiative to help 25 million people worldwide acquire new digital skills, needed to thrive in a digital economy.