If the state-of-the-art research in Machine and Deep Learning for finance has reached interesting results about Credit Scoring models, usage of such models in a heavily regulated context ([5], [1], [3], [4]) such as the one in banks has never been done so far. Our work is thus a tentative to challenge the current regulatory status-quo and introduce new BASEL 2 and 3 compliant techniques, while still answering the Federal Reserve Bank and the European Central Bank requirements. With the help of Gradient Boosting Machines (mainly XGBoost [9]) we challenge an actual model used by BANK A for scoring through the door Auto Loan applicants. We prove that the usage of such algorithms for Credit Scoring models drastically improves performance and default capture rate. Furthermore, we leverage the power of Shapley Values [16] to prove that these relatively simple models are not as black-box as the current regulatory system thinks they are, and we attempt to explain the model outputs and Credit Scores within the BANK A Model Design and Validation framework.

Most of the channels in this list are not on other lists from other articles. Before I start, I would like to preface this article by saying that most of these channels are not on other lists. Another thing is that while you watch these channels remember to code along and remember that what you are watching is not just a video, it is something to interact with. Currently, Machine and Deep Learning are some of the fastest-growing fields. These two fields require engineers to have skills like Statistics, Mathematics, Data Analysis, and Coding Machine Learning and Deep Learning Models.

Udemy is the biggest website in the world that offer courses in many categories, all the skills that you would be looking for are offered in Udemy, including languages, design, marketing and a lot of other categories, so when you ever want to buy a courses and pay for a new skills, Udemy would be the best forum for you. You can find payment courses, 100 free courses and coupons also, more than 12 categories are offered, and that what makes sure you will find the domain and the skill you are looking for. Our duty is to search for 100 off courses and free coupons. Data scientist is one of the best suited professions to thrive this century. It is digital, programming-oriented, and analytical.

New and continuously improving treatment options such as thrombolysis and thrombectomy have revolutionized acute stroke treatment in recent years. Following modern rhythms, the next revolution might well be the strategic use of the steadily increasing amounts of patient-related data for generating models enabling individualized outcome predictions. Milestones have already been achieved in several health care domains, as big data and artificial intelligence have entered everyday life. The aim of this review is to synoptically illustrate and discuss how artificial intelligence approaches may help to compute single-patient predictions in stroke outcome research in the acute, subacute and chronic stage. We will present approaches considering demographic, clinical and electrophysiological data, as well as data originating from various imaging modalities and combinations thereof. We will outline their advantages, disadvantages, their potential pitfalls and the promises they hold with a special focus on a clinical audience.

Data scientist is one of the best suited professions to thrive this century. It is digital, programming-oriented, and analytical. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace. However, supply has been very limited. It is difficult to acquire the skills necessary to be hired as a data scientist.

Introduction- Most of the Artificial Intelligent (Ai) Systems are developed as black boxes, especially Machine Learning and Deep Learning-based systems. Nowadays, these Machine and Deep Learning-based systems make decisions for our daily life, and should be explainable and should not be taken for granted to the end-users. The implication of such systems is rarely explored for the efficiency in the public usage (i.e., usage in -- Agriculture, Air Combat, Military Training, Education, Finance, Health Care, Human Resources, Customer Service, Autonomous Vehicles, Social Media, and several others[1]-[9]). Not only these, but the future might also be relying on Ai based system that will do our laundry, mow our lawn, fight wars [9]. Thus, there is so much room to improve the transparency of the systems along with fairness and accountability. There are some works that already stated the necessity of guidelines and governance of the Ai based systems, but more exposure is required in each area of application.

Are you a data scientist looking to develop a machine learning model? Use scikit learn to start creating your design today! Machine learning (ML), an application of Artificial Intelligence (AI), is rapidly growing at a faster rate. Almost every sector in today's world is adopting machine learning models as it brings value and improved customer experience resulting in higher Return on Investments (ROI). While many programming languages can help you get started with machine and deep learning, it is imperative to choose a programming language that is flexible and user-friendly.

Data scientist is one of the best suited professions to thrive this century. It is digital, programming-oriented, and analytical. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace. However, supply has been very limited. It is difficult to acquire the skills necessary to be hired as a data scientist.

Data scientist is one of the best suited professions to thrive this century. It is digital, programming-oriented, and analytical. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace. However, supply has been very limited. It is difficult to acquire the skills necessary to be hired as a data scientist.

The Digital Age reshaped everything. In the XXth century, philosophers questioned the originality of an artwork ("The Work of Art in the Age of Mechanical Reproduction" by Walter Benjamin). In the XXIst century, we question if something is real at all (and if there is something like a reality). With Machine and Deep Learning's power, entirely new possibilities emerged: we can generate something that never existed before. The technical progress is going on at a rapid pace. And it was a 2018 tweet.