We live in the era of data where data is the new oil. It is inevitable now that we need to pass our knowledge and understanding of the world to computers. They can model this behavior on a large scale. This is the age of machine learning. In this blog we will discuss some of the points required to getting started with machine learning.
Machine Learning is the art and science of making a
computer perform a specific task without explicitly programming it.
Due to the advent of BIG data, enterprises are sitting at lots of data that is not being utilized. By iteratively exploring data, computers can find hidden patterns in the data. Hence there is no need of explicitly programming where to look for it. Let us browse through some examples of what systems have machine learning at their core –
- Google Search Engine: The answer to every question, the remedy to every problem, Google has evolved to be so smart that it can autocomplete the queries for you, look exactly in the right places to retrieve the right content. At the heart, it uses machine learning.
- Recommendations: Frequently Bought Together, You May also know, Similar Items purchased by other users are some of the recommendation schemes gaining a lot of traction in the market, especially in the online retail and social media space. At the heart, it uses machine learning.
- NASA’s self-repairing Mars Rover: Having cameras that constantly monitor its parts, it is fitted with sophisticated algorithms to detect anomalies in its parts and apply some repairing action based on the detection schemes. At the heart, it uses machine learning.
Getting started with Machine Learning
You need to have a passion to look at data and you need to have a passion about solving real life problems. That’s all it takes to get you started. In this series we will be exploring a lot about understanding how these algorithms work and how best to implement them. Mostly I will use the python programming language to explain things, but the discussions can be applied to any other programming language as well.
Being an ardent data enthusiast, I will be going over some of the techniques and systems that I have explored, the problems I have looked at and some of the ways in which we can take a stab at it. I hope you have an intuition about what is machine learning and I cant wait to hear from you on what you think. People approach me with some of the problems they are working on and how best to approach to the solution. You can get to know more about me here.