What is machine learning?

What is machine learning?

The world is full of data. Lots and lots of data. Everything from pictures, music, words, spreadsheets, videos, and more. It doesn't look like it will be delayed any time soon. Machine learning promises to mean derived from all data.

In this series, I want to take you on an adventure through the world of AI, to explore the sciences of art, and the tools of machine learning. Along the way, we can create amazing experiences and gain valuable insights on how easy it will be to experience. We will start with a high-level concept and then dive into the technical details.

Arthur C. Clarke once said:

"Any sufficiently advanced technology is different from magic."
At first, ML looks like magic, but once you dive in, you will see that it is a set of tools to extract meaning from data.

Data all around us

Traditionally, people have adapted systems to analyze data and change data formats. As the volume of data surpasses the ability for humans to understand it and the ability to write rules manually, we will transform into increasingly automated systems that can learn from data, and, importantly, changes in data to adapt to moving data.

The machine is already everywhere

We see machine learning around us in the products we use today, but it's not about us that machine learning is behind it. While learning to the machine while tagging objects and people in pictures, you may not realize that even features like the video recommendation system are often driven by machine learning.

Of course, perhaps the biggest example is Google search. Every time you use Google Search, you're using a system that has multiple machine learning systems, from understanding the text of your query to adjusting the results based on your personal interests. When you search for "Java", machine learning determines which results show up first, determining whether it thinks you are a coffee expert or developer. Maybe you are both!

Today, the immediate applications of machine learning are already quite wide, including image recognition, fraud detection, recommendation engine, as well as text and speech systems. These powerful capabilities can be applied to a wide range of fields, from diabetes retinopathy and skin cancer detection to retail, and of course transportation, self-parking, and self-driving vehicles.

Expected feature

Not so long ago, when a company or product put machine learning into its offerings, it was considered a novel. Now, every company is looking to use machine learning in their products. This is fast becoming an expected feature. Just as we expect companies ’websites to work on our mobile devices or applications, the day will soon come when we hope that our technology will be personalized, insightful, and self-improving.

As we use ML to make current human tasks better, faster, or easier than ever before, we can better understand in the future, when ML can help us do things that we can never achieve on our own.
Thankfully, taking advantage of machine learning is not difficult. Tully has been pretty good; All you need is the data, the developers, and the willingness to take the plan.

Using data to answer questions

For our purposes, we can summarize the machine learning definition in just five words:

"Using data to answer questions"

This is certainly an oversimplification, but it can still serve a useful purpose.

Specifically, we can divide the definition into two parts: "using data", and "answer questions". These two pieces outline both sides of machine learning, both of which are of equal importance.

"Using data" is known as "training", while "answering questions" is referred to as "predicting", or "guessing".

What connects these two parts together is the model. We train models to make better and more useful predictions using datasets. This predictive model can be used to provide predictions in later data.

Data is the key

As you can see, the main part of this process is the data. Data is the key to unlock machine learning, as much as machine education is the key to unlocking insights hidden in data.

What now

It was just an overview of the high level of machine learning, why it is useful, and some of its applications. Machine learning is a broad field, detailing the whole family for inferring answers from data. In the future, we will provide you with a better knowledge of the given dataset and what approach to use for the question you want to answer, as well as the tools to complete it.
This is the first in a series of posts on Cloud Ann Adventure. Next time, we’ll dive into the concrete process of ML in more detail, becoming a step-by-step formula for how to access machine learning problems.