Computers have become so ubiquitous that nearly every aspect of our lives revolves around their use, yet the machines haven’t lost their ability to amaze us. The latest jaw-dropping technology is the ability of computers to teach themselves new skills by analyzing huge amounts of data. The many types of machine learning promise to make […]
Machine Learning Algorithms
Machine learning algorithms establish rules and processes that are used while processing a specific problem. These algorithms analyze data to predict the probable results of certain behaviors. As new data is received, these algorithms learn, optimizing and improving their responses using feedback from previous performances. Combinations of machine learning algorithms can produce artificial intelligence (AI). Machine […]
The Fundamentals of Deep Reinforcement Learning
Reinforcement Learning (RL), a “niche” Machine Learning technique, has surfaced in the last five years. In context-based decision making, Reinforcement Learning helps the machine take action-provoking decision making through a trial-and-error approach to achieve the optimal algorithmic model for a situation. Furthermore, the machine is trained through a reward/penalty-based feedback mechanism, the goal of which […]
Knowledge Graphs, Ontologies, and AI
This past fall, all aspects of the computable knowledge structure KBpedia – its upper ontology (KKO), full knowledge graph, mappings to major leading knowledge bases, and 70 logical concept groupings called typologies – became open source. Making big strides in increasing definitions and mappings has been a main focus of KBpedia v. 1.60. As it always […]
How the Three Paradigms of AI/Machine Learning Will Fuel the Tech Evolution
Click here to learn more about Ram Sivasankaran. The rapid evolution and democratization of advanced technology have accelerated humankind’s ability to achieve what was unthinkable even a decade ago. Self-driving cars, robotic vacuums, and the virtual assistant known as Siri have put us not just over the top technologically, but on conceptual overdrive as well. […]
The Next Level of Data Governance, Machine Learning, and Data Science
The term, machine learning dates back to a 1959 article by Arthur Samuel, in which he posited: “Programming computers to learn from experience should eventually eliminate the need for much of this detailed programming effort.” The hope was that data alone could be used to develop models, rather than relying on fixed rules or theory. […]
10 Most Popular Data Mining Algorithms
Click to learn more about author Ramesh Dontha. Learning about data mining algorithms is not for the faint of heart and the literature on the web makes it even more intimidating. It seems as though most of the data mining information online is written by Ph.Ds for other Ph.Ds. Earlier on, I published a simple article […]
Practicing Data Science
Click to learn more about author Rosaria Silipo. There are many declinations of Data Science projects: with or without labeled data; stopping at data wrangling or involving Machine Learning algorithms; predicting classes or predicting numbers; with unevenly distributed classes, with binary classes, or even with no examples at all of one of the classes; with […]
Machine Learning 101
Click to learn more about author Steve MacLauchlan. By now you’re probably well aware that Big Data and Artificial Intelligence are major disruptors in almost every single vertical. Understanding the landscape can be challenging, particularly for business customers who want to innovate but aren’t sure where to start. In today’s blog, I hope to leave you, […]
Getting Back to the Basics: What is Machine Learning?
Click to learn more about author Seth Deland. Machine Learning seems to be the engineering industry’s latest buzzword – a technology with astonishing potential, yet one that many businesses struggle to understand, let alone embrace. Just last year, fewer than one-third (23%) of businesses had adopted any level of Machine Learning automation, and only 5% reported […]