Machine Learning

Improving business process automation through the acceleration of digital transformation by adding intelligence to your processes.

Robotic Process Automation
Tablet with gears above it

MCCi Solution

Human Intelligence and Machine Learning - A Winning Combination.

Machine Learning is an evolving technology – understanding that is critical to realizing its value.


Types of Machine Learning

Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. The logic is used to evaluate a set of inputs to produce an output or moreThere are three core approaches to machine learning: 

Reinforcement Method

This technique is a great choice for complex organizational processes. It learns by a series of positive and negative reinforcements to handle unanticipated issues that arise over time.

Unsupervised Method

This route allows intelligence at work to learn as it goes without any directional guidance from the start.

Supervised Method

This option allows you to feed into the system exactly what you want it to identify step-by-step so it consistently recognizes what you need it to.

When it comes to business process automations, the most common form of machine learning used today is the Supervised method 

The Supervised method is where the software is presented with an example of inputs and the desired outputs. This approach to a machine learning process begins with the “teacher”. The teacher(s) are typically a group of business process owners and business analysts who have gathered a training set of data. This includes samples of input data, and examples of the desired results. The objective is to guide the algorithm(s) on how to map the provided inputs toward the desired outputs.  

Following the achievement of successful baseline results, an operational environment is necessary – it’s designed to allow for incremental improvements over time. This requires a feedback process to allow the algorithms to evolve as additional inputs and outputs are evaluated.