Machine Learning course|1: Understanding Machine Learning

Machine Learning is a process where we try to train a machine to predict things for us. There are many online materials where they have covered the topics of machine learning in great depths. But is these depth really necessary to have a career in machine learning. My answer would be NO!!! Indeed the concepts of machine learning is really necessary to understand the insights of the projects you will come accross in the future, but it makes no sense to build an ocean. In this course, I will try to focus on the basics of machine learning which is really necessary for the job interviews you are gonna face in the near future (mostly the mathematical part).

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Understanding Machine Learning

Lets take an example: A person starts working in a company. Every time he cracks a deal, the company rewards him with some token money. So, here the person (in the beginning of his career in this company)being new to the system, has no idea how much reward he gonna get. The same person worked in the company for say 4 years and in these 4 years he had been rewarded several times. So, he now with experience has the ability to estimate the value of reward he is going to get for cracking a deal. So this process of learning things over time when done by a machine is called machine learning.

machine learning example (Image by author)

Steps in Machine Learning:

There are basically three steps involved in the process of machine learning:

• Collect data.
• Understand the data and create mathematical formulations.
• Predict the values.

Photo by Franki Chamaki on Unsplash

Collect data

This is the 1st step in ML. In this process, we try to collect data samples. In the above example, the person tried to collect the data for the deal amount cracked and the rewards he got for them.

Understand the data and create Mathematical Formulations

In this step, we try to analyse the data we collected over time. Once we start analysing the data, we try to find a relation between the data points collected. In the above example, the person,collected the data points of deals cracked vs the rewards he achieved. The person can the try to analyse the data and he can come with a relation or equation between the deals cracked and rewards achieved.

Predict the values

In the above example, the person can come up with an estimate value of reward if he cracks a deal in future. So this estimate value or prediction is beacuse of his experience he gained over the past 4 years rewards values.

In the next blog we will discuss about the, types of machine learning.