How does Machine Learning work?

Machine Learning algorithm is trained using a training data set to create a model. When new input data is introduced to the ML algorithm, it makes a prediction on the basis of the model.

The prediction is evaluated for accuracy and if the accuracy is acceptable, the Machine Learning algorithm is deployed. If the accuracy is not acceptable, the Machine Learning algorithm is trained again and again with an augmented training data set.

This is just a very high-level example as there are many factors and other steps involved.

To explain clearly, Machine Learning is classified into 3 types.They are:

Supervised and unsupervised are mostly used by a lot machine learning engineers and data geeks.

Reinforcement learning is really powerful and complex to apply for problems.

1.Supervised Learning:

Supervised learning occurs when an algorithm learns from example data.Supervised learning algorithms try to model relationships and dependencies between the target prediction output and the input features such that we can predict the output values for new data based on those relationships which it learned from the previous data sets.

Here the human experts acts as the teacher where we feed the computer with training data containing the input/predictors and we show it the correct answers (output) and from the data the computer should be able to learn the patterns.

To understand easily, initially we have to train the data sets and then after that if we give any input then according to that they will show the output.

There are two types of Supervised Learning.They are:

a)Regression: This is a type of problem where we need to predict the continuous-response value (ex : above we predict number which can vary from -infinity to +infinity)

Some examples are

etc… there are tons of things we can predict if we wish.

b)Classification: This is a type of problem where we predictthe categorical response value where the data can be separated into specific “ classes “ (ex: we predict one of the values in a set of values).

Some examples are :

Basically ‘Yes/No’ type questions called binary classification.

Other examples are :

This type is called multi-class classification.

Here is the final picture

2.Unsupervised Learning:

Unsupervised learning occurs when an algorithm learns from plain examples without any associated response, leaving to the algorithm to determine the data patterns on its own.They are quite useful in providing humans with insights into the meaning of data and new useful inputs to supervised machine learning algorithms.

To understand easily, in the unsupervised learning we do not need to train the data sets because they will train itself.Example is given below:

There are also different types for unsupervised learning like Clustering and anomaly detection (clustering is pretty famous)

a)Clustering: This is a type of problem where we group similar things together.

Bit similar to multi class classification but here we don’t provide the labels, the system understands from data itself and cluster the data.

Some examples are :

Unsupervised learning is bit difficult to implement and its not used as widely as supervised.

I would like to cover reinforcement learning in a separate full article as it is intense. so

That’s all for this story, Hope you get some idea.

In the next story I would like to talk about the first machine learning algorithm Linear Regression with Gradient descent.

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