Let's start by observing a real-world example of how machine learning is often accustomed to solving a difficult problem.

Sustainable farming techniques are essential to maximizing food production while protecting a fragile environment. The Yield, an agricultural technology company based in Australia, uses sensors, data, and machine learning to help farmers make informed decisions related to weather, soil, and plant conditions.

You can find out more about how the Yield is using machine learning to feed the world without wrecking the planet here.

How machine learning works

So how do machines learn?

The answer is, from data. In today's world, we create huge volumes of data as we go about our everyday lives. From the text messages, emails, and social media posts we send to the photographs and videos we take on our phones, we generate massive amounts of information. More data still is created by millions of sensors in our homes, cars, cities, public transport infrastructure, and factories.

Data scientists can use all of that data to train machine learning models that can make predictions and inferences based on the relationships they find in the data.

For example, suppose an environmental conservation organization wants volunteers to identify and catalog different species of wildflower using a phone app. The following animation shows how machine learning can be used to enable this scenario.

1. A team of botanists and data scientists collects samples of wildflowers.
2. The team labels the samples with the correct species.
3. The labeled data is processed using an algorithm that finds relationships between the features of the samples and the labeled species.
4. The results of the algorithm are encapsulated in a model.
5. When new samples are found by volunteers, the model can identify the correct species label.

Machine learning in Microsoft Azure

Understand anomaly detection

Imagine you're creating a software system to monitor credit card transactions and detect unusual usage patterns that might indicate fraud. Or an application that tracks activity in an automated production line and identifies failures. Or a racing car telemetry system that uses sensors to proactively warn engineers about potential mechanical failures before they happen.

These kinds of scenarios can be addressed by using anomaly detection - a machine learning-based technique that analyzes data over time and identifies unusual changes.

Let's explore how anomaly detection might help in the racing car scenario.

1. Sensors in the car collect telemetry, such as engine revolutions, brake temperature and so on.
2. An anomaly detection model is trained to understand expected fluctuations in the telemetry measurements over time.
3. If a measurement occurs outside of the normal expected range, the model reports an anomaly that can be used to alert the race engineer to call the driver in for a pit stop to fix the issue before it forces retirement from the race.

Anomaly detection in Microsoft Azure

Understand computer vision

Computer Vision is an area of AI that deals with visual processing. Let's explore some of the possibilities that computer vision brings.

The Seeing AI app is a great example of the power of computer vision. Designed for the blind and low vision community, the Seeing AI app harnesses the power of AI to open up the visual world and describe nearby people, text, and objects.

To find out more, check out the Seeing AI web page.

Computer Vision models and capabilities

Most computer vision solutions are based on machine learning models that can be applied to visual input from cameras, videos, or images. The following table describes common computer vision tasks.

Task	Description
Image classification	Image classification involves training a machine learning model to classify images based on their contents. For example, in a traffic monitoring solution, you might use an image classification model to classify images based on the type of vehicle they contain, such as taxis, buses, cyclists, and so on.
Object detection	Object detection machine learning models are trained to classify individual objects within an image and identify their location with a bounding box. For example, a traffic monitoring solution might use object detection to identify the location of different classes of vehicles.
Semantic segmentation	Semantic segmentation is an advanced machine learning technique in which individual pixels in the image are classified according to the object to which they belong. For example, a traffic monitoring solution might overlay traffic images with "mask" layers to highlight different vehicles using specific colors.
Image analysis	You can create solutions that combine machine learning models with advanced image analysis techniques to extract information from images, including "tags" that could help catalog the image or even descriptive captions that summarize the scene shown in the image.
Face detection, analysis, and recognition	Face detection is a specialized form of object detection that locates human faces in an image. This can be combined with classification and facial geometry analysis techniques to infer details such as age and emotional state, and even recognize individuals based on their facial features.
Optical character recognition (OCR)	Optical character recognition is a technique used to detect and read the text in images. You can use OCR to read the text in photographs (for example, road signs or storefronts) or to extract information from scanned documents such as letters, invoices, or forms.

Computer vision services in Microsoft Azure

Try this

1. Open another browser tab and visit https://aidemos.microsoft.com/computer-vision.
2. Use the demo interface to try each of the steps. For each step, you will choose a picture and review the information returned by the Computer Vision services.