Machine Learning Implementation: Concrete steps to follow


Machine learning is an important feature of any successful Artificial Intelligence Algorithm. If a successful AI  tries to imitate the human brain, machine learning would be what allows it to learn new things. In the human brain, each time new information is obtained new connections are made making existing neurons both weaker and stronger. Similarly, Machine learning allows the system to quickly learn, unlearn, and relearn as more and more data is made available and changes to the system both good or ill, become apparent.

There is no quick and easy shortcut that allows a data scientist to implement machine learning in any system, it is always a long process with many steps and constant trial and error.

 Let us look at some concrete steps that everyone wishing to start on a machine learning model has to follow, the order and nature of these steps will always vary depending on specific applications but the principle remains consistent through each of them.

Data Collection

The first step in any task that a data scientist must go through is always collecting more data, this step continues throughout every step as parameters change rapidly.

Data Storage

The storage process dictates both the security and accessibility of this data. For most modern applications data is stored on a cloud network.

Data Transformation

This is the process of converting data in one format to another format, this may be to help the system understand the data better or help draw more accurate conclusions.

Data Labelling

Labeling helps to improve comprehension of the vast amount of available data by assigning categories, keywords, and filters.

Model Building

A set of models that handles operational problems via a complex algorithm that can navigate the available data to make logical and accurate decisions. The easiest one of these to understand is the popular decision tree algorithm that analyses the costs and benefits of each branching decision accurately and comes up with the best solution

Model Training

This involves involving a variety of different data into the pre-built model in order to test its validity and accuracy in already established cases. Data Scientist then separates test and training subsets in the ratio of 80:20, if the model performs well on training but poorly in tests, the entire model has to be rebuilt.

Model Assessment

Model assessment involves more tests this time involving real-world data, it is the only way to establish the validity of the model.

Model Accuracy Improvement

The usefulness of a machine learning algorithm will only improve when the process adapts and changes to fit information accurately while drawing valid conclusions. Machine learning is the true implementation of the supervised learning model where mistakes are encouraged as the entire process is very fast in a simulated environment.

As stated earlier the stages of machine learning implementation are rarely set in stone, but each of these steps has to be followed at some point, more actions may be taken as the technology improves.


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