Machine Learning Defination:
The world is filled with data, a lot of data-- pictures, music, words, spreadsheets, videos, and it doesn't look like it's going to slow down anytime soon. Machine learning brings the promise of deriving meaning from all of that data. Along the way, we'll see just how easy it is to create amazing experiences and yield valuable insights.
What is Machine Learning:
The value of machine learning is only just beginning to show itself. There is a lot of data in the world today generated not only by people, but also by computers, phones and other devices. This will only continue to grow in the years to come. Traditionally, humans have analyzed data and adapted systems to the changes in data patterns. However, as the volume of data surpasses the ability for humans to make sense of it and manually write those rules, we will turn increasingly to automated systems that can learn from the data and importantly, the changes in data to adapt to a shifting landscape.
We see machine learning all around us in the products we use today. However, it isn't always apparent that machine learning is behind it all. While things like tagging objects and people inside of photos are clearly machine learning at play.
Perhaps the biggest example of all is Google search. Every time you use Google search, you're using a system that has many machine learning systems at its core, from understanding the text of your query to adjusting the results based on your personal interests, such as knowing which results to show you first when searching for Python depending on whether you're a coffee expert or a developer-- perhaps you're both.
 |
|
Applications of Machine Learning:
Today, machine learning's immediate applications are already quite wide-ranging, including image recognition, fraud detection and recommendation systems, as well as text and speech systems too. These powerful capabilities can be applied to a wide range of fields, from diabetic retinopathy and skin cancer detection to retail and of course, transportation in the form of self-parking and self-driving vehicles. It wasn't that long ago that when a company or product had machine learning in its offerings, it was considered novel. Now, every company is pivoting to use machine learning in their products in some way.
It's rapidly becoming, well, an expected feature. Just as we expect companies to have a website that works on your mobile device or perhaps an app, the day will soon come when it will be expected that our technology will be personalized, insightful and self-correcting. As we use machine learning to make human tasks better, faster and easier than before, we can also look further into the future when machine learning can help us do tasks that we never could have achieved on our own.
Advantages of Machine Learning:
Thankfully, it's not hard to take advantage of machine learning today. The tooling has gotten quite good. All you need is data, developers and a willingness to take the plunge.
For our purposes, I've shortened the definition of machine learning down to just five words-- using data to answer questions. While I wouldn't use such a short answer for an essay prompt on exam, it serves a useful purpose for us here.
In particular, we can split the definition into two parts-- using data and answer questions. These two pieces broadly outline the two sides in machine learning, both of them equally important. Using data is what we refer to as training, while answering questions is referred to as making predictions or inference.
Now let's drill into those two sides briefly for a little bit. Training refers to using our data to inform the creation and fine tuning of a predictive model. This predictive model can then be used to serve up predictions on previously unseen data and answer those questions. As more data is gathered, the model can be improved over time and new predictive models deployed.
Data and Machine Learning: As you may have noticed, the key component of this entire process is data. Everything hinges on data. Data is the key to unlocking machine learning, just as much as machine learning is the key to unlocking that hidden insight in data.
This was just a high level overview of machine learning-- why it's useful and some of its applications. Machine learning is a broad field, spanning an entire family of techniques when inferring answers from data.
In our next article, we'll dive right into the concrete process of doing machine learning in more detail, going through a step-by-step formula for how to approach machine learning problems.
So this was today's article. I hope you enjoyed it. If you'd like to see more of my tech articles then check in this website. Otherwise comment below the article for any future explainers you'd like me to make. See you in another article. Have a happy ending. Smile,
0 Comments