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
While most applications of the Segment of One are unashamedly about sales and revenue, personalized medicine is a use case in life sciences and healthcare that’s focused on a higher purpose: improving and saving lives. And it applies to everyone. Anyone that has been frustrated with an adverse or ineffective reaction to medication is well aware that the “one size fits all” traditional healthcare treatment model where the same diagnosis means receiving the same treatment is an extremely inexact process leading to much trial and error. Continue reading Applying the Segment of One to Personalized Medicine
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
Editor’s Note: This week, we’re launching our new series: “Ask the Informationist.” Every month, one of our Informationists will answer relevant and thought-provoking questions on a wide range of topics in data and analytics. Continue reading Ask the Informationist: 5 “New” Types of Advanced Analytics
We are very excited to announce that our data scientist Mitchell Shuster will be a panelist on a round table webinar on machine learning! The webinar will take place on Wednesday, May 27, from 1-2 pm Eastern Time.
Continue reading Knowledgent Data Scientist to Speak on Machine Learning Webinar
We are excited to announce our silver sponsorship at the 3rd Annual Bristol-Myers Squibb Conference (BITCon) being held on April 27 and 28 at the Princeton, NJ Marriott.
Location: Princeton Marriott at Forrestal, Princeton, NJ
Event Link: BITCon 2015
Knowledgent will also be exhibiting at the event. Make sure to stop by booth number 101 to speak with a Knowledgent Informationist and learn more about exciting employment opportunities.
Interested in other events Knowledgent will be attending? Check out our Events page for the latest info!