46 Big Data Terms Defined
Organizations must deal with the collection and storage of continuously-growing data, and then harvest it to capture value. "Big Data," as its call...Learn More
The phrase machine learning seems to appear alongside every new technology or service. Despite its popularity, many people still don’t understand exactly what machine learning means, nor how to make practical use of it.
Today, we will explain the basics of machine learning, introduce you to Amazon Machine Learning, and show you how you can use it to improve your business through Amazon Web Services and their machine learning utility.
Machine learning is all around us. Believe it or not, each time we use our smartphones or computers, we’re using some form of machine learning. Of course, we’re not aware of the computing potential that is observing, tracking, and analyzing our online behavior on a daily basis. Servers all over the world are processing massive volumes of data using machine learning to find out what we’ll do next, where we’ll go, and what we’ll click on. The information that this data is revealing about us and our habits can be worth a fortune.
Machine learning is closely related to artificial intelligence, data mining, and deep learning, but it has a different focus. The main focus of machine learning is the development of computer programs that can automatically change when exposed to new data. WhatIs.com defines machine learning as “a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed.” In other words, machine learning uses massive amounts of data to detect patterns, and when the repetition is recognized, computers can adjust the actions of their programs according to the newly identified behavior. To simplify this even further, we could say that it takes past patterns to predict future patterns or behaviors.
Machine learning was born out of pattern recognition and computational learning theory in artificial intelligence. It is also closely related to computational statistics, mathematical modeling, and composite algorithms.
Creating a model for pattern search requires developing a sophisticated mathematical model that will serve as the basis of the pattern. This task can be quite time-consuming and difficult for a small team that wants to exploit such a powerful resource.
Different mathematical models are the starting point for machine learning. Computers are using them to analyze data and learn from previous computations. The quality of the design and data portions enable a computer to produce reliable, repeatable decisions and results that you can use to improve your work.
The results of machine learning data analysis can help an organization identify profitable opportunities, avoid unknown risks, and make conclusions that can improve their decision-making processes.
Given the complexity and processing power needed to apply machine learning, you might think that only large corporations and institutions are using it. And you wouldn’t be far from the truth. However, thanks to automatic platforms such as Amazon Machine Learning, even small teams can get their hands on this exceptional technology. Let’s look at where we can find machine learning in the world around us.
Practical applications of machine learning technology are at work in some of the applications that we use on a daily basis.
How many times have you checked your Facebook feed today? The News Feed that you see there is generated by machine learning algorithms working in the background. They track your behavior and interactions with friends to find patterns and serve information that will be interesting to you. For example, you may have noticed that you see a lot of info in your feed about the people whose posts or photos you like or comment on, and less about the people with whom you rarely interact.
A similar example is Twitter’s “You might like,” “Who to follow,” and “While you were away” feeds. They are also making tailored suggestions based on your interests and activities while you like and share content on the platform.
Of course, it’s not just social networks. Netflix and Amazon Prime are also using machine learning to suggest what to watch next based on the movies, series, and programs that we have seen so far.
E-commerce websites such as Amazon.com and Aliexpress.com are also making suggestions for what to buy next by tracking our previous purchases. They are even sending us emails with good deals and discounts on items that we have checked out along the way, convincing us that this is the best deal for us.
Machine learning also has a significant role in other areas, namely fraud detection and online security. Machine learning is very efficient in detecting fraudulent behavior. Thanks to such actions, the internet is a much safer place today than it was only a decade ago.
It also has extensive application in government institutions, healthcare organizations, finance and banking, transportation, energy, and many other important areas of our lives.
Machine learning is a powerful resource that can be applied in many different areas, from serving your customers with the most desirable content at any time, to forecasting stocks rated on the financial market. The question is, how you can use such a powerful resource for your projects without spending a fortune?
It’s understandable to think that you’ll need to invest a lot of time and money before you could be able to use machine learning in your projects. Just to get started you need to have:
When you sum it all up, it is quite an investment to have all of these resources at your disposal.
But what if there is a way to get the most of the necessary requirements without spending thousands of dollars before you even start your project? Enter the Amazon Machine Learning platform.
The Amazon Machine Learning service provides you with all of the equipment and tools you need to learn how to use machine learning. The service provides a rich toolset that will guide you through each step of the way, from creating machine learning models to data analysis, and applying results to your application. You will find all of the key concepts of Amazon Machine Learning here, as well as setup instructions, tutorials, training materials, data evaluation, analysis models, and many other useful resources.
For use cases and a real example for Python take a look at our post on Amazon Machine Learning.
To be able to use the service, you’ll first need to create your machine learning model. The platform provides visualization tools and wizards that will help you start building your ML model without forcing you to learn all the complex algorithms. These tools will help you evaluate your models and fine-tune them if needed.
If you don’t need a custom model, you can use one of the predefined models for:
Once you decide which model you will be using (custom or predefined), Amazon Machine Learning offers a simple API that you can implement in your app. You don’t need to develop a sophisticated infrastructure, or to write tons of code. All you have to do is apply the API, and works out of the box.
To be able to use your machine learning model on your current dataset, you will need to create a connection to read the data from Amazon S3, Amazon Redshift, or Amazon RDS (Relational Database Service). Please note: You will not be able to import data from elsewhere, so you need to use one of these three services to be able to apply your ML model.
The next step is to check the data quality. You can use Amazon visual tools to ensure that you have a quality data batch. The character of the information is quite relevant. If you have insufficient information, you can end up with bad predictions that will affect the overall quality of resulting actions. So, make sure that you have the best data set you can get.
Finally, when you start the entire process you can decide whether you want to have batch predictions for your entire data set at once or real-time data prediction. Amazon Machine Learning can provide you with both. It’s up to you to decide which best fits your requirements.
The official Amazon website has a wealth of material on machine learning, including handy examples, tutorials, and models that you can analyze to see some of the best practices.
If you need more practice than you can get by using the AWS website, we recommend the Cloud Academy Amazon Machine Learning Lab. It is specialized for human activity recognition, and it will give you some excellent examples and use cases for Amazon Machine Learning.
We have some good news for you: The pricing for using Amazon Machine Learning is flexible. You will pay only for the resources you’re using and spending. There are no upfront costs and no massive investments on the compound infrastructure. Check the official AWS pricing page for more details.
With Amazon Machine Learning, the learning process will cost you far less, and the primary resource that you will have to invest is your time in getting you and your team up to speed.
Machine learning, artificial intelligence, deep learning, and big data analysis are the key concepts that will shape our future and help us make quality decisions. One of the best ways to take advantage of the power of machine learning for your projects and your business is the Amazon Web Services platform.
Course: Introduction to the Principles and Practice of Amazon Machine Learning
This in-depth introduction to the principles and practice of AML covers the basics of ML, working with data sources and how to manipulate data in Amazon Machine Learning to ensure a successful model, generating accurate predictions, and more.
Hands-on Lab: Amazon Machine Learning for Human Activity Recognition
This lab will give you a general idea of how to use Amazon Machine Learning to build and use your own models. After a brief overview of the main machine learning concepts, we’ll use an open dataset from UCI to train and use a real-world model for HAR (Human Activity Recognition). We will walk through the whole process, from the dataset analysis and Datasource creation, all the way to model training/evaluation and a real Python script to generate real-time predictions.
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