This'Top 10' list has been created on the basis of best content, and not exactly the number of views. I have also taken special care to walk you through the world of ML in Finance in a gentle, step-by-step manner. To get you motivated, we first begin with talks on the various applications of ML in Finance. Then, to enable access to free financial data, is a video detailing various sources for the latter. To get your hands dirty, we then move on to R and Python tutorials for specific financial use cases.

The aim of this blog post is to highlight some of the key features of the KNIME Deeplearning4J (DL4J) integration, and help newcomers to either Deep Learning or KNIME to be able to take their first steps with Deep Learning in KNIME Analytics Platform. With a little bit of patience, you can run the example provided in this blog post on your laptop, since it uses a small dataset and only a few neural net layers. However, Deep Learning is a poster child for using GPUs to accelerate expensive computations. Fortunately DL4J includes GPU acceleration, which can be enabled within the KNIME Analytics Platform. If you don't happen to have a good GPU available, a particularly easy way to get access to one is to use a GPU-enabled KNIME Cloud Analytics Platform, which is the cloud version of KNIME Analytics Platform.

Machine learning is a subfield of artificial intelligence (AI). The goal of machine learning generally is to understand the structure of data and fit that data into models that can be understood and utilized by people. Although machine learning is a field within computer science, it differs from traditional computational approaches. In traditional computing, algorithms are sets of explicitly programmed instructions used by computers to calculate or problem solve. Machine learning algorithms instead allow for computers to train on data inputs and use statistical analysis in order to output values that fall within a specific range.

In this article, I've listed down the essential resources to master the basic and advanced version of data science using: Global Machine Learning Certifications – This list highlights the widely recognized & renowned certifications in machine learning which can add significant weight to your candidature, thereby increasing your chances to grab a data scientist job. This certification offers multiple courses such as algorithms for data science, probability and statistics, machine learning for data science, exploratory data analysis. It teaches aspiring data science candidates to learn data mining, machine learning, big data and data science projects and work with non-profits, federal agencies and local governments and make a social impact. It teaches real world, practical skills to become a data scientist / data engineer.

Peer review, evaluation, and selection is a fundamental aspect of modern science. Funding bodies the world over employ experts to review and select the best proposals of those submitted for funding. The problem of peer selection, however, is much more general: a professional society may want to give a subset of its members awards based on the opinions of all members; an instructor for a MOOC or online course may want to crowdsource grading; or a marketing company may select ideas from group brainstorming sessions based on peer evaluation. We make three fundamental contributions to the study of procedures or mechanisms for peer selection, a specific type of group decision-making problem, studied in computer science, economics, and political science. First, we propose a novel mechanism that is strategyproof, i.e., agents cannot benefit by reporting insincere valuations. Second, we demonstrate the effectiveness of our mechanism by a comprehensive simulation-based comparison with a suite of mechanisms found in the literature. Finally, our mechanism employs a randomized rounding technique that is of independent interest, as it solves the apportionment problem that arises in various settings where discrete resources such as parliamentary representation slots need to be divided proportionally.

This course is a dense presentation of machine learning (ML) tools used in financial risk management, portfolio management, and trading. Ten classes are offered: two on risk management, two on loan portfolio management, three on portfolio optimization, and three on high-frequency trading. The risk classes cover the risk measurement of financial assets using distribution fitting, copulas, PCA, and splines. The loan portfolio management classes cover risk estimation and backtesting using logistic regression, regularization, clustering methods, and the applied statistics concepts such as parameter and process risk. Kaggle competitions for loan portfolios which used tree-based algorithms for predictions are also reviewed.

Artificial Intelligence (AI) is the next big thing, but how can you use it to create a better learning experience? Look at any list of disruptive technologies and AI is likely to be at the top, it's the latest tech buzzword to take over the news media. As an L&D professional, what should you be paying close attention to? And what's safe to ignore? Our round up of the more useful applications of AI for learning includes many of the leading examples from academia and adult education that will be filtering their way into the workplace learning environment as AI becomes more widespread.

This repo contains an incremental sequence of notebooks designed to teach deep learning, Apache MXNet (incubating), and the gluon interface. Our goal is to leverage the strengths of Jupyter notebooks to present prose, graphics, equations, and code together in one place. If we're successful, the result will be a resource that could be simultaneously a book, course material, a prop for live tutorials, and a resource for plagiarising (with our blessing) useful code. To our knowledge there's no source out there that teaches either (1) the full breadth of concepts in modern deep learning or (2) interleaves an engaging textbook with runnable code. We'll find out by the end of this venture whether or not that void exists for a good reason.

Finally, the main aim of this blog post is to give a well-intentioned advice about the importance of Mathematics in Machine Learning and the necessary topics and useful resources for a mastery of these topics. However, some Machine Learning enthusiasts are novice in Maths and will probably find this post disheartening (seriously, this is not my aim). For beginners, you don't need a lot of Mathematics to start doing Machine Learning. The fundamental prerequisite is data analysis as described in this blog post and you can learn the maths on the go as you master more techniques and algorithms. This entry was originally published on my LinkedIn page.

We are proud to present Deepnets as the new resource brought to the BigML platform. On October 5, 2017, it will be available via the BigML Dashboard, API and WhizzML. Deepnets (an optimized version of Deep Neural Networks) are part of a broader family of classification and regression methods based on learning data representations from a wide variety of data types (e.g., numeric, categorical, text, image). Deepnets have been successfully used to solve many types of classification and regression problems in addition to social network filtering, machine translation, bioinformatics and similar problems in data-rich domains. In the spirit of making Machine Learning easy for everyone, we will provide new learning material for you to start with Deepnets from scratch and progressively become a power user.