Machine Learning

Machine Learning - as the name suggest, it is about teaching a machine to learn about something. The crust is about how to teach a machine.. the better you teach the better it learns.

How we teach a machine ? well, remember the word 'maths' ? it is every where.. most of the logical problems are solved through maths and that is how machines work. so, we teach the same way.. to do that, it is required to collect 'enough' data sets and feed it to a machine asking to fit a mathematical equation. By doing so, the machine learns about the data and its dynamics .. once it is done, the machine would be able to predict the outcome for a future data point.

the first step in doing so, is identify what kind of ML problem is it. Generally, an ml problem falls into either of the two categories or sometimes both ! depends on what you want..

                                                                ML Problem
                                                                 /              \
                                                                /                \
                                                   supervised                unsupervised
                                                   /            \                                |
                                                  /              \                               |
                                        classification    regression           grouping/clustering


supervised problems are the cases where the data sets contains both input (features) and output (label). The teaching process will be carried out in guidance with the output so that, ml algorithm will know how is it doing how to evaluate the model so that it better fits to the data.

This is further categorized into two types. 'classification' is type of problem where the output is basically classified into two or a few unique values and the goal is to fit a model so that, the future data point will be classified into the given sets. like what is the number that a given picture contains..

Regression is the second type of supervised problems where the output variable is a continuous label instead of limited/discrete value. like whether your favorite team wins a match against a given opponent.


Unsupervised problems are the cases where the outcome is not really known. So, the goal is to fit a model in order to identify the clusters in the data sets.

There are lot of ML Algorithms and libraries and frameworks available as opensource and as commercial products in the market already..