Continuous integration is the crown jewel of DevOps release management. When everything else works and continuous integration is enabled, multiple releases are enabled daily. The most important focus in this course are the enablers [...]
This course is for experienced machine-learning practitioners who want to take their skills to the next level by using R to hone their abilities as predictive modellers. Trainees will learn essential techniques for real machine-learning model development, helping them to build more accurate models. In the masterclass, participants will work to deploy, test, and improve their models.
This advanced machine learning masterclass will explore the many unique applications and extensions of the randomForest package, many of which are implemented in R. Access to the methods in random forest allows the user [...]
A company with strict governance processes can hugely benefit from DevOps but it still needs governance. DevOps is different, it prioritises automation over documentation. That brings a new perspective to governance. This course has [...]
This full day workshop examines the trends in analytics deployment and developments in advanced technology. The implications of these technology developments for data foundation implementations will be discussed with examples in future architecture and deployment. This workshop presents best practices for deployment of a next generation data management implementation as the realization of analytic capability for mobile devices and consumer intelligence. We will also explore emerging trends related to big data analytics using content from Web 3.0 applications and other non-traditional data sources such as sensors and rich media.
This talk examines key trends in analytics deployment and developments in advanced technology. Specific areas of focus include: (1) data acquisition and delivery, (2) operational intelligence in the real-time enterprise, and (3) analytic applications architecture. The implications of these technology developments for analytic implementations will be discussed with examples from across a number of different industries. Learn about key trends in data acquisition and delivery and analytic applications architecture, and discover important modes of delivering operational intelligence.
There are a significant number of big data analytics opportunities where graph processing is an effective model of computation for problem solving. In this workshop we present a programming model for implementation of graph algorithms and explain how the execution model works. We also provide example applications in the area of social network analysis, product cross-selling, and fraud detection.
This course provides a more rigorous, mathematically based view of modern neural networks, their training, applications, strengths and weaknesses, focusing on key architectures such as convolutional nets for image processing and recurrent nets for text and time series. This course will also include use of dedicated hardware such as GPUs and multiple computing nodes on the cloud. There will also be an overview of the most common available platforms for neural computation. Some topics touched in the introduction will be revisited in more thorough detail. Optional advanced topics may include Generative Adversarial Networks, Reinforcement Learning, Transfer Learning and probabilistic neural networks.
Trends in healthcare are toward more data, more complexity, more decisions, and less time and resource for execution. The only way to be successful in this environment is to leverage a more advanced framework for healthcare analytics. This seminar will discuss best practices in healthcare delivery and architecture frameworks for realisation of decisioning services for improving quality and efficiency of care. A reference architecture for deployment will be described in detail along with cases studies of successful realisation in major healthcare organisations within the United States. Special emphasis will be placed on the value proposition for integration of disparate data sources such as Medicaid, TANF, Child Support, and SACWIS.
The detection of anomalies is one of the most eclectic and difficult activities in data analysis. This course builds on the basics introduced in the earlier course, and provides more advanced methods including supervised and unsupervised learning, advanced use of Benford’s Law, and more on statistical anomaly detection. Optional topics may include anomalies in time series, deception in text and the use of social network analysis to detect fraud and other undesirable behaviours.
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