Our own Informationist and Principal Data Scientist, Alberto Artasanchez, has authored a great new article, entitled “Data is the New Code”. Continue reading New Article from Alberto Artasanchez: Data is the New Code
Knowledgent, the data intelligence company, announced today that it has achieved Amazon Web Services (AWS) Machine Learning (ML) Competency status. This designation is part of the inaugural group for the AWS Machine Learning Competency. Continue reading Knowledgent Achieves Inaugural AWS Machine Learning Competency Status
Our own Chief Data Scientist Ari Yacobi will be presenting at the upcoming SAS Health Analytics Forum on May 9-10 in Cary, North Carolina! Continue reading Chief Data Scientist Ari Yacobi Presenting at the SAS Health Analytics Forum
Knowledgent is pleased to announce the release of a new white paper, entitled “The Ever Evolving Artificial Intelligence and Machine Learning Ecosystem.” Continue reading Knowledgent Releases New Artificial Intelligence and Machine Learning White Paper
As you may recall from the previous posts in the Machine Learning Algorithm Series, when performing Linear Regression and Logistic Regression there is always an assumption of the linearity of the underlying or transformed data. Needless to say, most real world data sets don’t comply with this assumption, so non-parametric algorithms are necessary for accurately modeling non-linear data. In the third installment of our Machine Learning Series, we talk about k-Nearest Neighbors (kNN), amongst the simplest and most popular non-parametric methods used for data classification and regression.
Continue reading Machine Learning Algorithm Series: k-Nearest Neighbors
This post is the second in our series on machine learning (ML) algorithms, focusing on the assumptions, implications, and applications of various techniques. Following the first installment on linear regression, we’ll be discussing a subset of general linear models known as the logistic regression (also referred to as logit regression or a logit model). Continue reading Machine Learning Algorithm Series: Logistic Regression
This post is the first in our series on machine learning (ML) algorithms. (See posts 2 and 3 on Logistic Regression and k-Nearest Neighbors.) In these posts, we explain the basic underlying concepts behind various algorithms, their pros and cons, and their most common applications. We are starting this discussion with the oldest ML algorithm, linear regression. It is an excellent starting point for learning about ML because many of the basic concepts involved are easy to explain in this context and because it is relatively easy to implement your very own linear regression algorithm. Continue reading Machine Learning Algorithm Series: Linear Regression
Editor’s Note: This is the second post in our new series introducing our rockstar summer interns! This post is from Rachel Sholder, Data Science intern for Health and Life Sciences.
I am excited to be working as a Data Science Intern at Knowledgent. I just completed my junior year at Lehigh University in Bethlehem, Pennsylvania, where I am pursuing a Bachelor’s Degree in Mathematics with a Probability and Statistics concentration (along with a minor in Actuarial Science and a minor in Psychology). Continue reading Meet the Interns: Rachel Sholder, Health and Life Sciences Data Science Intern
In case you missed it, Knowledgent’s Data Scientist Dr. Mitchell Shuster recently served as a panelist on a round table webinar. Titled “The Business Potential of Machine Learning and Cognitive Computing,” the webinar featured expert insights on the next phase of machine learning, including deep learning, analytics, and cognitive computing.
“Machine learning and cognitive computing are becoming a critical portion of our technology infrastructure,” said Mitchell. “As with the rise of the Internet and social media, businesses that embrace these technologies will reap tremendous rewards and gain significant competitive advantage over those who fail to adapt to the changing landscape.”
Here are our major takeaways from the webinar: Continue reading “Beware the Hype!” And 4 Other Lessons in Machine Learning
What questions should I be asking to get the most out of my data with machine learning? What are best practices for using machine learning in my organization?
Our own Informationist and Data Scientist Mitchell Shuster will be tackling these questions and more on an IEEE-sponsored round table webinar tomorrow. Titled “The Business Potential of Machine Learning and Cognitive Computing,” this webinar will cover the next phase of machine learning, including deep learning, analytics and cognitive computing. Click here to register for the webinar.
Prior to the webinar, I sat down with Mitchell to talk about the hype and reality around machine learning: Continue reading Q&A: Mitchell Shuster on Machine Learning Hype vs. Reality