If you work in the area of artificial intelligence (AI) and cognitive computing, you might use buzzwords and phrases that, to others, might be perceived as confusing jargon. This article attempts to explain what these terms mean, how they relate to one other, and where they all fit along the AI and cognitive time continuum. I include a glossary of my top 20 useful AI/cognitive terms and advice on getting started on your AI/cognitive journey. Think of machine learning (ML) as a set of libraries and an execution engine for running a set of algorithms as part of a model to predict one or more outcomes. Each outcome has an associated score indicating the confidence level at which it will occur.

There's no doubt in my mind that machine learning (ML) as part of a data science strategy can help revolutionize many aspects of everyday life. Below I highlight a few examples of how different industries are able to leverage machine learning for competitive differentiation and customer benefit. There are tens of thousands of daily published journals and papers across the world. It is impractical for every clinician to read and absorb these. ML can help identify patterns and correlations that humans alone would otherwise miss -- possibly resulting in diagnosis and treatment plans that are suboptimal.

The power of machine learning, data science and big data is no longer considered a hype or fad. Data has emerged as a real competitive edge and disruptive force for enterprises. Companies that make the most of data by using machine learning and data science will win and outlast in this digital world. IBM recently extended its partnership with Hortonworks to better help businesses accelerate data-driven decision making. Hortonworks is the leading industry and only pure open source Hadoop platform.

IBM Data Science Experience (DSX) is an interactive, collaborative, cloud-based environment where data scientists can use multiple tools to achieve insights. Data scientists can use the best of open source, tap into IBM's unique features, grow their skills, and collaborate with teams. One of the many features of DSX provides the capability to create and train a machine learning model in DSX with little to no coding. This model can subsequently be saved and deployed to Watson Machine Learning on IBM Bluemix and called for scoring in real-time. This tutorial is a continuation of the following logistic regression analysis, which creates, trains, saves and deploys a logistic regression model that predicts the possibility for a tent purchase based on age, sex, marital status, and job profession for an individual.

IBM (NYSE:IBM) announced IBM Watson Data Platform to help companies gain more valuable insights from data. The platform delivers the world's fastest data ingestion engine and cognitive-powered decision-making to data professionals, allowing them to collaborate in the IBM Cloud, with the services they prefer. IBM is also making IBM Watson Machine Learning Service available – making machine learning simple with an intuitive, self-service interface. Machine learning is incredibly powerful, but many of today's data professionals lack the skills to fully exploit it for business and the ability to effectively collaborate on datasets," said Bob Picciano, Senior Vice President, IBM Analytics. "Watson Data Platform applies cognitive assistance for creating machine learning models, making it far faster to get from data to insight.

Although open source code in Python and R is popular because of its low cost, flexibility, and power, the time required to properly create code and ensure that it is working correctly can be frustrating. Not everyone is a programmer or wants to program! That's why the announcement by IBM in June about IBM Data Science Experience is such a game-changer! IBM Data Science Experience is a way for data scientists to collaborate and work on data science programs in the most efficient way possible. What if the collaboration could be extended to the data scientist or analyst who wants to build predictive models without code?