top of page

Subscribe to Wisdom

Thanks for Subscribing!

  • Writer's pictureDimit Chadha

What's Makes Machine Learn


A Machine Learning algorithm is a set of rules and statistical techniques used to learn patterns from data and draw significant information from it. It is the logic behind a Machine Learning model. An example of a Machine Learning algorithm is the Linear Regression algorithm.


A model is the main component of Machine Learning. A model is trained by using a Machine Learning Algorithm. An algorithm maps all the decisions that a model is supposed to take based on the given input, in order to get the correct output.

Predictor Variable

It is a feature (s) of the data that can be used to predict the output.

Response Variable: It is the feature or the output variable that needs to be predicted by using the predictor variable(s).

Training Data

The Machine Learning model is built using the training data. The training data helps the model to identify key trends and patterns essential to predict the output. You take a randomly selected specimen of mangoes from the market (training data), make a table of all the physical characteristics of each mango, like color, size, shape, grown in which part of the country, sold by which vendor, etc (features), along with the sweetness, juiciness, ripeness of that mango (output variables). You feed this data to the machine learning algorithm (classification/regression), and it learns a model of the correlation between an average mango’s physical characteristics, and its quality.

Testing Data

After the model is trained, it must be tested to evaluate how accurately it can predict an outcome. This is done by the testing data set. Next time when you go shopping, you will measure the characteristics of the mangoes which you are purchasing(test data) and feed it to the Machine Learning algorithm. It will use the model which was computed earlier to predict if the mangoes are sweet, ripe and/or juicy. The algorithm may internally use the rules, similar to the one you manually wrote earlier (for eg, a decision tree). Finally, you can now shop for mangoes with great confidence, without worrying about the details of how to choose the best mangoes.

What's the Conclusion

A Machine Learning process begins by feeding the machine lots of data, by using this data the machine is trained to detect hidden insights and trends. These insights are then used to build a Machine Learning Model by using an algorithm in order to solve a problem.

So don't get afraid that some magic is going to happen in machine that will enable machine to learn.

Buddies it's NO MAGIC , Just MATHEMATICS !

54 views0 comments

Recent Posts

See All


Modern Digital Watch
bottom of page