While learning about machine learning basics, one often confuses Machine Learning, Artificial Intelligence and Deep Learning. The below diagram clears the concept of machine learning.
We hope that the diagram has helped dispel any doubts you had regarding the three disciplines. Now we move to the heart of the matter.
Components of Machine Learning
Let’s break down the machine learning process so that we can understand it in detail. We will take a small example as we go through it.
Collecting and preparing data
The first step in machine learning basics is that we feed knowledge/data to the machine, this data is divided into two parts namely, training data and testing data.
Consider that we want to build software which can identify a person as soon as their photo is shown. We start by collecting data, ie photos of people. Now in this phase, we have to make sure that our data is representative of the entire population ie, if we include only adults from 20 -40 years of age, the software will fail if it is shown a picture of a baby.
The data is usually split into 80/20 or 70/30 to make sure that the model once sufficiently trained can be tested later.
Choosing and training a model
This is the second step in machine learning basics. We have a variety of machine learning algorithms and models which have been created and modified further so that it can solve a particular type of problem. Thus, it is imperative we choose and train a model depending on its suitability for the problem at hand.
Evaluating a model
The machine learns the patterns and features from the training data and trains itself to take decisions like identifying, classifying or predicting new data. To check how accurately the machine is able to take these decisions, the predictions are tested on the testing data.
In this case, we will first work on the training data and once the model is sufficiently trained, we use it on the testing data to understand how successful it is in recognising the faces in the photo.