Machine Learning a brief Introduction

What actually is machine learning

Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly program to do so.

It is also defined as the

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being clearly programmed Expert.ai

Machine learning in a nutshell looks like this. You start with data that contains patterns. You then feed that data into a machine learning algorithm, maybe more than one, that finds patterns in the data. This algorithm generates something called a model. A model is functionality, typically code, that's able to recognize patterns when presented with new data. Applications can then use that model by supplying new data to see if this data matches known patterns.

For example: Creating a pattern for fradulent transaction to catch thieves and corrupt people. Simply spplying data about a new transaction with proper attributes that he wants to know. This model will return a probability of whether this transaction is fraudulent. It knows that because of the patterns which we provide it during modeling. Machine learning in a nutshell.

Why is it important

A big one is that doing machine learning well requires huge amount of of data, which we have. We live in the big data age It requires lot of compute power, which we have. We live in the cloud age. And it requires effective machine learning algorithms, which we have because we have seen researchers spend years, decades, in this space learning what works. All of these things are now more available than ever, and that's a big reason why machine learning is so important today.

Well, you could think about three groups of people who care about this topic.

The first is business leaders. They want solutions to business problems, things I've described so far, fraudulent transactions, deciding whether customers are going to switch or not, all these things. These are business problems. Good solutions have real business value. The other organizations do things faster, better, cheaper, and so business leaders really want those solutions. This is a good thing because business leaders also have the money to pay for those solutions.

Data is the lifeblood of all business. Data-driven decisions increasingly make the difference between keeping up with competition or falling further behind. Machine learning can be the key to unlocking the value of corporate and customer data and enacting decisions that keep a company ahead of the competition. It helps the business man's to grow their business. This make them active and professional in their business skills. Mostly customer data and good decisions keep them on track that in no passage of time develop the business.

Software developers also care about this because they want to build better applications. And, as we saw, applications can rely on models created in VM machine learning to make better predictions. If you're a software developer, machine learning can help you build smarter apps, even if you're not the one who creates the models. You can just use the models. And the third category of people who are really involved in this space are called data scientists who want powerful, easy?to?use tools. There are key things to know about Data Scientists First, good ones are scarce. Second, unsurprisingly, good ones are expensive. And the reason is if you can solve important business problems with machine learning, you can save a lot of money. There's real business value there. And so good data scientists who know all three of these things, statistics, machine learning, and a problem domain, can have enormous value. That's why right now they command substantial salaries and are often hard to keep in your employ.