See what goes into a great Minitab Insights Conference presentation. Great stories others want to hear. Discovering new tools in Minitab software. Engaging walkthroughs of finding insights in your data, and recommendations on how to act on them. All packed into a few days of learning and fun.
You now see “Machine Learning” appearing along with “Big Data”, “AI” or “Internet of Things” as part of the digital transformation of business. Yet this new hype is creating a series of myths, which can obscure the role of data-driven professionals in this transformation. Watch our webinar today.
Research institutions and museums seek to provide the most accurate data possible to record the past and choose where to explore in the future. Modeling the accuracy of data is essential and nailing down geographical locations of specimen samples as exactly as possible is vital to do it effectively.
At its core, all Machine Learning algorithms follow a two-part process. First a sequence of increasingly complex functions is fit to part of the data (training data set). Then each model in the sequence is evaluated on how well it performs on the data that was held out (the holdout set).
Machine learning utilizes analytical data to discover insights that can create a more efficient manufacturing process and solve problems in a matter of seconds. These tools can be used in countless constructive ways in the manufacturing industry.
Adam Russell, Global Operations Master Black Belt explains how Tate & Lyle deploys Continuous Improvement (CI) tools, including Minitab and Salford Predictive Modeler (SPM), to challenging engineering and manufacturing problems.
The convergence of the widespread deployment of low-cost sensors, cloud and greater computer power has brought together a multitude of connected devices which can monitor, collect, exchange, analyze and deliver insight like never before.