Proudly Serving Minnesota

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Minnesota Machine Learning Since 2017

Implement hyper-personalization with Machine Learning.

Machine Learning is a method of data analysis that automates analytical model building.  It is a branch of Artificial Intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

Minnesota Customers

Some information I know about Minnesota is I believe the state was admitted or ratified to the United States around or about 'May 11, 1858'.  Minnesota is located around latitude '46.39241' and longitude of '-94.63623' and has a population of roughly '5,706,494 million'.  If I remember correctly the capital is 'St.  Paul' and the largest city is 'Minneapolis'. 

Machine Learning in Today's World

By using algorithms to build models that uncover connections, organizations can make better decisions without human intervention.  Learn more about the technologies that are shaping the world we live in.

What is the Difference Between AI and ML?

Artificial Intelligence and Machine Learning are very closely related and connected.  Because of this relationship, when you look into AI vs.  Machine Learning technology you're really looking into their interconnection.

While AI and Machine Learning are very closely connected, they're not the same.  Machine Learning is considered a subset of AI.

The Evolution of Machine Learning

Machine Learning is a term that goes back to 1959.  Because of new computing technologies, Machine Learning today is not like Machine Learning of the past.  It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in Artificial Intelligence wanted to see if computers could learn from data.  The iterative aspect of Machine Learning is important because as models are exposed to new data, they are able to independently adapt.  They learn from previous computations to produce reliable, repeatable decisions and results.  It's a science that's not new - but one that has gained fresh momentum.

While many Machine Learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data - over and over, faster and faster - is a recent development.  Here are a few widely publicized examples of Machine Learning applications you may be familiar with:

1. The self-driving car?  The essence of Machine Learning.
2. Online recommendation offers such as those from Amazon and Netflix?  Machine Learning applications for everyday life.
3. Knowing what customers are saying about you on Twitter?  Machine Learning combined with linguistic rule creation.
4. Fraud detection?  One of the more obvious, important uses in our world today.

How are AI and Machine Learning Connected?

An "intelligent" computer uses AI to think like a human and perform tasks on its own.  Machine learning is how a computer system develops its intelligence.

One way to train a computer to mimic human reasoning is to use a neural network, which is a series of algorithms that are modeled after the human brain.  The neural network helps the computer system achieve AI through deep learning.  This close connection is why the idea of AI vs.  Machine Learning is really about the ways that AI and Machine Learning work together.

If at any point you decide to reach to me just know the area codes I am familiar with for Minnesota are '218, 320, 507, 612, 651, 763, 952'.  For Machine Learning assistance you will find my rates very reasonable for Minnesota.  Now just keep in mind my time zone is 'Eastern Standard Time (EST)' and I know the time zones in Minnesota are 'Central Standard Time (CST)' in case you wish to call me.  Anyway let me continue.

Why is Machine Learning Important?

Resurging interest in Machine Learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever.  Things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage.

All of these things mean it's possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results - even on a very large scale.  And by building precise models, an organization has a better chance of identifying profitable opportunities - or avoiding unknown risks.

How AI and Machine Learning Work Together

When you're looking into the difference between Artificial Intelligence and Machine Learning, it's helpful to see how they interact through their close connection.  This is how AI and Machine Learning work together:

Capabilities of AI and Machine Learning

Companies in almost every industry are discovering new opportunities through the connection between AI and Machine Learning.  These are just a few capabilities that have become valuable in helping companies transform their processes and products:

Predictive Analytics

This capability helps companies predict trends and behavioral patterns by discovering cause-and-effect relationships in data.

Recommendation Engines

With recommendation engines, companies use data analysis to recommend products that someone might be interested in.

You know, I don't make it out to Minnesota much but I would like to see the 'Common Loon' state bird.  I am a little familiar with the Minnesota 'Lady slipper' state flower as well.  However, I do not know much about Minnesota's state tree the 'Red Pine'.  Fishing is fun to me perhaps I would like reeling in the Minnesota 'Walleye' state fish.  Anyway, sorry I went off topic.  Let me continue.

What's Required to Create Good Machine Learning Systems?

1. Data preparation capabilities.
2. Algorithms - basic and advanced.
3. Automation and iterative processes.
4. Scalability.
5. Ensemble modeling.

Did You Know?

1. In Machine Learning, a target is called a label.
2. In statistics, a target is called a dependent variable.
3. A variable in statistics is called a feature in Machine Learning.
4. A transformation in statistics is called feature creation in Machine Learning.

Speech Recognition and Natural Language Understanding

Speech recognition enables a computer system to identify words in spoken language, and natural language understanding recognizes meaning in written or spoken language.

Image and Video Processing

These capabilities make it possible to recognize faces, objects, and actions in images and videos, and implement functionalities such as visual search.

Sentiment Analysis

A computer system uses sentiment analysis to identify and categorize positive, neutral, and negative attitudes that are expressed in text.