What’s Machine Learning?

Machine learning is the present and the future. All technologists, data scientists and financial experts can benefit from Machine Learning.

This article focuses on outlining an easy-to-understand explanation of what machine learning is. Then it introduces how machine learning works.

Let’s Start — What Is Machine Learning?

Machine learning is a branch of Artificial Intelligence. Machine learning is a set of algorithms that learn to discover trends and patterns in data to gain insights. These algorithms then become self-sufficient to make decisions on the data.

Machine learning algorithms are now utilized in nearly all sectors — from healthcare to financial organisations to anti-fraud companies to shopping websites.

How is it different from traditional programming?

Traditionally, programmers code a number of software procedures or rules. These procedures are also known as rules or methods. The set of instructions take certain inputs and produce expected outputs. Instructions can also execute other functions.

Machine learning is about getting machines to learn data and then make decisions on similar data. Machine learning is about using predictive algorithms to forecast behaviors of data so that calculated decisions can be taken.

Machine learning algorithms are built on statistical features.

Why Is Machine Learning So Popular Nowadays?

It is probably worth knowing that machine learning is not a new concept. You might have heard the buzzwords artificial intelligence/deep learning/machine learning/big data/data scientist in the near past and possibly more recently.

Machine learning’s growing popularity is primarily due to increase in data availability and advancements in technology. Faster machines and smarter algorithms are implemented daily. Subsequently cloud computing is introduced where we can load a large quantity of data. The amount of data stored in the servers is growing at an exponential rate. . This data is valuable and can help us make better decisions in the future.

Machine Learning Process:

Writing an efficient and accurate model is the key to increasing chances of a successful machine learning process.

At a high level, the process is

1.    Gather and clean data (sample) to represent large data (population) — this step can at times take the longest time.

2.    Learn and understand data to figure out trends and patterns.

3.    Build a model that understands the data and makes decisions on data.

4.    Feed the model 70%-80% of sample data. This set of data is known as Training Data.

5.    Validate model with the rest of data. This set of data is known as Test Data.

6.    Based on results, repeat the steps if required.

Thank you…!