Machine Learning Week

Predictive Analytics World is the industry's leading multi-vendor conference for machine learning and predictive analytics professionals, managers, and commercial practitioners. This conference provides case studies, expertise, and resources to achieve the following goals:

Increase the effectiveness of predictive analytics deployment.
Enhanced capabilities: Create new opportunities in the fields of data science and machine learning.
Big data: Use more data to predict and drive more value.

Predictive Analytics World is the only conference of its kind, offering vendor-neutral sessions in industries such as banking, financial services, e-commerce, entertainment, government, healthcare, manufacturing, high technology, insurance, non-profits, publishing, and retail.

Why bring together such a diverse set of activities? The story is the same regardless of how predictive analytics is applied: Customers, employees, students, voters, patients, equipment, and other organizational elements can be scored predictably to improve performance. Predictive analytics initiatives across industries use the same core predictive modeling technology, have similar project overhead and data requirements, and face similar process and analytical challenges.

Data science and big data. The big data and data science movements rely heavily on predictive analytics. The ultimate goal of data is to learn from it and predict the future. Focusing on data or how much of it there is can cause this point to be overlooked. What is the value, function, and goal? Prediction is the most actionable data win in terms of improving organizational operations.

Learning by machine. Predictive analytics refers to the commercial application of machine learning (the two terms are often used synonymously). Although the term "machine learning" was once only used within the confines of research labs, it is now increasingly being used in the context of commercial deployment. We're talking about technology that learns from data to predict or infer an unknown, such as decision trees, logistic regression, neural networks, and many others.