AI, Artificial Intelligence, Computer Science, Cybersecurity, Information Technology, Machine Learning, ML

An Introduction To Machine Learning

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This is a follow-up to our previous article in which we provided an overview of Artificial Intelligence (AI), and some of its major components and market applications. Here, we will examine a key subset of it – Machine Learning, or ML for short. 

What Is Machine Learning?

Machine learning is not just about robots as there is so much more to this evolving technology. This technical definition courtesy of IBM sums ML up nicely:

“Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.”

As noted in the above definition, the key distinction here is that an ML system can learn on its own, and improve this process over a period of time without hardly any human intervention.  

A good example of this is Cybersecurity.  One of the main goals in this industry is to create predictive ML models to help determine what the threat landscape will look like in the future. Of course, this is all dependent on the type of data provided to the model. 

The idea here is that once the first iteration and model have been formulated, the ML system will then be able to provide its own strong insight as to what future attacks could look like. This has major benefits for the industry, as it can provide businesses with the advantage of staying one step ahead of a potential cyber attack.

Types of Machine Learning Algorithms

The algorithms that are used in ML can be quite complex, depending upon the type of application that it has been designed for.  However, in general terms, they fall into four distinct categories:

1) Supervised ML Algorithms:

This is where an ML system takes data sets that have been classified to produce a predictive outcome.  Although training can take time, an advantage is that errors or any type of skewness in the data can be detected and corrected automatically for future model development.

2) Unsupervised ML Algorithms:

This is the type of ML system where unclassified datasets are used to predict potential future outcomes. Therefore, it has to formulate hypotheses on its own, independent of the data that is being fed into the system. The direct opposite to Supervised ML Algorithms, where conclusions are dependent on the data it is fed.

3) Supervised & Unsupervised ML Algorithms:

This is where a combination of both types of algorithms is used. It’s important to note that the mix here is not of an equal balance. The preference is much towards using unclassified data sets, with a small dosage of classified data sets. By using both algorithms, the predictive accuracy of the ML systems can be greatly increased.  

4) Reinforcement ML Algorithms:

This is where the ML system learns from the flaws or the validities it has produced. This is more of a trial-and-error approach when it comes to predicting outcomes.  An advantage is that the ideal learning patterns can be formulated and used for the predictive situations the system faces. For example, if the ML system is asked to predict the outcome for instance X, it will know exactly which learning pattern is to be used, thus streamlining the computational and processing times.  

The Applications Of The ML Algorithms

Here, we will look at the typical examples for which algorithms are used:

1) Supervised ML Algorithms:

Multiple Regression Modeling:

This is where statistical values are computed on a continual basis.

Multi Classification:

This is where the ML system must choose between two or more outcomes.

Binary Classification:

This is when the system must subdivide the datasets into at least two or more distinct categories.

2) Unsupervised ML Algorithms:

Reduction of Dimensionality :

Here, the total number of variables that are not needed are automatically excluded to streamline the prediction process.

Association Mining/Skewness Detection :

This is where datasets that are similar and unlike one another are detected and identified.

Statistical Clustering :

Here, datasets are subdivided based upon their degree of similarity from one another.

3) Supervised and Unsupervised ML Algorithms:

Foreign Language Translation:

This is how the ML system is taught to translate a language by providing less than 50% of the total words and vocabulary.

Data Labeling:

In these applications, ML systems can take a very small number of inputs (datasets), and automatically create and apply labels to a much larger dataset.

4) For The Reinforcement ML Algorithms:

Resource Allocation:

A key principle of economics – how to do more with a finite number of resources.  These ML systems are used to determine the optimal mix of how the inputs should be distributed to yield the maximum output possible.

Robotics Technology:

This area is typically found in the manufacturing sector, such as the production of cars.  When these types of algorithms are used, robots powered by machine learning can learn how best to accomplish tasks based on the mistakes or achievements it makes.                                                        

Machine learning has also seen usage in the business sector when it comes to understanding their respective customer base.

Typical examples include the following:

  • Understanding buying patterns behaviors of most valuable customers.
  • Breaking down the lifecycle of the customer acquisition, from identifying prospects to becoming paying customers.
  • Knowing when customers leave to acquire products and services from the competition.
  • Learning how to market the right products/services to the appropriate customer segment at the proper timing.

As highlighted, Machine Learning is not just one entity, it consists of a lot of different techniques and is constantly expanding, evident from the amount of machine learning jobs on the rise globally.

Effective machine learning techniques can solve complex problems that engineers may struggle with. Used correctly, machine learning is capable of disrupting and creating new capabilities in most industries. This can help improve productivity while reducing costs and increasing ROI. 

Stay tuned for our next article which will examine yet another innovative facet of AI  – Neural Networks.

Anthony Figueroa

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