Requirements No Such Pre-req, its good to have some basic math concepts Description Data Science is a multidisciplinary field that deals with the study of data. Data scientists have the ability to take data, understand it, process it, and extract information from it, visualize the information and communicate it. Data scientists are well-versed in multiple disciplines including mathematics, statistics, economics, business, and computer science, as well as the unique ability to ask interesting and challenging data questions based on formal or informal theory to spawn valuable and meticulous insights. This course introduces students to this rapidly growing field and equips them with its most fundamental principles, tools, and mindset. Have an in-depth understanding of the concepts of Machine Learning Be able to grasp, understand, and write machine learning code from scratch Use Builtin Libraries available to build machine learning models Be able to analyze, build, and assess models on any dataset Be able to interpret and understand the black box behind model Understand the applications of data science by exhibiting the ability to work on different datasets and interpreting them.

Using the'What-If Tool' to investigate Machine Learning models. Using Deep Learning for finger-vein based biometric authentication http://dev.thegeeknews.net/0c5afdbb29

The deployment of machine learning models is the process for making your models available in production environments, where they can provide predictions to other software systems. It is only once models are deployed to production that they start adding value, making deployment a crucial step. However, there is complexity in the deployment of machine learning models. This post aims to at the very least make you aware of where this complexity comes from, and I'm also hoping it will provide you with useful tools and heuristics to combat this complexity. If it's code, step-by-step tutorials and example projects you are looking for, you might be interested in the Udemy Course "Deployment of Machine Learning Models".

Face recognition is the problem of identifying and verifying people in a photograph by their face. It is a task that is trivially performed by humans, even under varying light and when faces are changed by age or obstructed with accessories and facial hair. Nevertheless, it is remained a challenging computer vision problem for decades until recently. Deep learning methods are able to leverage very large datasets of faces and learn rich and compact representations of faces, allowing modern models to first perform as-well and later to outperform the face recognition capabilities of humans. In this post, you will discover the problem of face recognition and how deep learning methods can achieve superhuman performance.

Built on a proven BI platform, today's Cognos Analytics is powerfully enhanced with Artificial Intelligence and Natural Language dialogue capabilities that help you tell more compelling data stories, while being easier to use than ever. Cognos Analytics v11.1, driven by AI, opens analytics to business users with guided data preparation & advanced analytics, to rapidly uncover and deliver new insights, and to share their findings across the enterprise for better business decisions. With rich managed reporting and reliable analytics governance, Cognos Analytics changes the BI game with AI. We look forward to virtually meeting you.

The First International Workshop on AI and Intelligent Assistance for Legal Professionals in the Digital Workplace (LegalAIIA) will be held at the Cyberjustice Laboratory at the University of Montreal on June 17th. This workshop is part of the 17th International Conference on AI and Law (ICAIL), a biennial conference which has served as an important forum at the intersection the AI and the law since its founding in 1987. The LegalAIIA workshop itself is an offshoot of the successful decade-long DESI (Discovery for Electronically Stored Informed) workshop series, which was pivotal in helping forge an interdisciplinary community of legal and technical practitioners working on advancing the state-of-the-art in electronic discovery practice. The first edition of Legal AIIA, driven by an impressive set of electronic discovery veterans including Jack G. Conrad (Thomson Reuters), Jeremy Pickens (Catalyst Repository Systems), Amanda Jones (H5), Hans Henseler (Magnet Forensics), and Jason R. Baron (Drinker, Biddle & Reath), aims to tackle head on the issue of human-AI collaboration. Accepted papers will focus on evaluating when and how to best leverage a "human-in-the-loop" approach to AI.

Understanding large-scale patterns in student course enrollment is a problem of great interest to university administrators and educational researchers. Yet important decisions are often made without a good quantitative framework of the process underlying student choices. We propose a probabilistic approach to modelling course enrollment decisions, drawing inspiration from multilabel classification and mixture models. We use ten years of anonymized student transcripts from a large university to construct a Gaussian latent variable model that learns the joint distribution over course enrollments. The models allow for a diverse set of inference queries and robustness to data sparsity. We demonstrate the efficacy of this approach in comparison to others, including deep learning architectures, and demonstrate its ability to infer the underlying student interests that guide enrollment decisions.

We present a reinforcement learning (RL) based guidance system for automated theorem proving geared towards Finding Longer Proofs (FLoP). FLoP focuses on generalizing from short proofs to longer ones of similar structure. To achieve that, FLoP uses state-of-the-art RL approaches that were previously not applied in theorem proving. In particular, we show that curriculum learning significantly outperforms previous learning-based proof guidance on a synthetic dataset of increasingly difficult arithmetic problems.

Academics and practitioners alike believe that continual learning (CL) is a fundamental step towards artificial intelligence. CL is the ability of a model to learn continually from a stream of data. In practice, this means supporting the ability of a model to autonomously learn and adapt in production as new data comes in. The idea of CL is to mimic humans ability to continually acquire, fine-tune, and transfer knowledge and skills throughout their lifespan. CL of models in production will improve accuracy, and bring AI one step closer to real human intelligence.

Productive Edge partners with enterprise clients to develop transformative digital strategies and customer experiences, and we deliver these by applying Artificial Intelligence, Internet of Things (IoT), Intelligent Automation, and Cloud Native technologies. Our digital business consulting and technology solutions are focused on measurable business outcomes. Our culture is built upon our values of commitment, constant improvement, and pride without ego. Our team members are masters at their craft and down-to-earth people who believe in working hard, playing hard, and celebrating our successes.