Machine Learning is the ability of a machine to understand, self-improve or intuitively make decisions. Artificial Intelligence is comprised of Machine Learning technologies through self improving algorithms. These algorithms form the architecture for decision making, drawing from collected data and related experiences and subsequently employing statistical techniques which progressively improve performance on a specific task without being explicitly programmed. This modern technology enables applications and simplifies interaction providing future opportunities for an easier and more productive lifestyle through automation.
The end goal is to make your application work intelligently for you which means you save time and money.
Classification – The fundamental ability to predict a label or value from existing data given a set of indicator variables.
Statistical classification includes the intelligent determination and subjugation of data into representative categories for ingestion, understanding and conclusion. This is executed by through the application of statistical formulas designed to automate the analysis and iterate the dependent variables to determine the categories on the basis of similar data characteristics.
Once collected, data must be permuted intelligently for use in analysis. While most quantitative data can be directly evaluated, qualitative data need be transformed into ordinal or nominal information. A variety of statistical techniques underlay the preparation of data for use in machine learning and artificial intelligence applications.
This is most useful for:
- Predicting customer behavior
- Recommendation Engines
- Internet of Things Monitoring & Prediction
- Determine Transaction Categories
Clustering – Clustering is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. Statistical techniques seek to empirically determine similarities and differences, both obvious and concealed, in order to facilitate clustering applications.
Tangible exercises include:
- Identifying correlated items
- Customer segmentation
- Determining taxonomy from existing data
Regression – Regression implements a set of statistical processes for estimating the relationship between attributes and making a future prediction. These types of exercises overlap some of the aforementioned activities but also provide prediction for data expansion or discretization.
Useful cases include:
- Inventory and sales predictions
- Profiling markets for expansion
- Next most likely analysis
- Predicting domains of increase or decrease
- Customer Reviews: Unable to wade through millions of reviews? Our technology can make the task manageable. Get a sense of sentiment but also group and cluster concepts to see bright spots and areas requiring improvement.
- Document Review: Need to know what was on a hard drive? Looking for specific and related concepts in files? NLP performs all these and the volume is not a factor. We use a combination of NLP and forensic techniques to digest, verify and report.
- Reputation Monitoring: What are others saying about you and/or your business. Monitor blogs, social media, and more.
- Competitive & Market Intelligence: Sift and determine what a market is doing or what your competitors are posting. Collect news, reporting, press releases, twitter feeds, websites and more.
- Regulatory Compliance & Complaints: Determine if a problem exists before you find out the hard way. Add algorithmic scoring for risk and you have an interesting method of ascertaining exposure.
- Topic Extraction: Whether a core of data exists in healthcare, research or industry, NLP can determine topics and trends unseen to the human eye. When like items are clustered, the core concepts stand out, as does tangential information.
- Human Machine Interface: The keyboard has been our long standing interface with machines. A system of input has developed over time and NLP is the next logical step in that evolution.