About Machine Learning Examples

1. About Machine Learning Examples

2. Siri To begin with, Siri uses various technologies, including question analysis, natural language processing, machine learning, and data mashups. Speech recognition, in particular, involves spoken word conversion to a sequence of words, also known as speech to text (STT) or automatic speech recognition (ASR). Speech recognition apps are available in many forms and can be found in home appliances, cell phones, data entry, medical transcriptions, air traffic controllers, and more. The training process of a speech recognition systems demands several techniques, and the level of complexity varies depending on the nature of the training. Siri doesn’t understand the English language, but because it is based on a set of specific templates that trigger specific actions, it adapts making you believe it’s “human”. It doesn’t learn new things, but it can be expanded and extended. It can only perform programmed tasks.

3. Cortana Microsoft’s Cortana is smart enough to crack a joke and tell whether you should leave early for work because there’s a lot of traffic. You cannot trust it completely, but it’s a start. Cortana redefines the term “virtual assistant” because it was built following a model called “dynamic learning”. Cortana is an advanced business intelligence (BI) analytics suite; a data storage package that includes information management system, business intelligence software, machine learning, all wrapped up in a monthly service subscription. Available on Microsoft’s Azure Cloud, Cortana enables startups and businesses to completely transform their data into intelligent actions. It allows users to process data from a many different data sources; change that data, apply machine learning and data mining to advanced analytics techniques, and then extract actionable information that enables businesses to take timely, intelligent actions.

4. Face Recognition Facebook Helps You “Remember” the Name of Your Friends Another very cool machine learning example is Facebook’s newest face recognition software. We know, it’s pretty scary! How is it possible for Facebook to know the name of your friends when you upload a group picture if you haven’t even had the time to tag them? Back in the day, Facebook “recommended” users to tag their friends. All they had to do was click on their face on the picture and type their name to tag them. Facebook didn’t know who those people were. Now, when a photo is uploaded onto a user’s profile, Facebook automatically recommends that user tag their friends. And it knows exactly who is who. Abracadabra? Or just creepy? The new technology is better known as face recognition, and it uses machine learning (Facebook’s algorithms) to recognise familiar faces in your friends’ list. It’s pretty amazing, and it can do it because it is a form of adaptive technology. You probably tagged those friends before in the past, right? Facebook’s machine learning mojomemorised those patterns, and voila! There’s your answer.

5. How Much Machine Learning is There in Search Engines? Why do you think Google is so powerful? It’s no secret that machine learning is gaining popularity across a wide variety of industries. Not all programs are capable, though. It all depends on how much data these programs have to train their models. That’s the main reason companies like Facebook and Google need as much data as they can get. For instance, Google made TensorFlow open-source. It is an excellent software toolkit that helps build large-scale machine learning apps. Giving that away, for free, was a big deal. If you don’t know what TensorFlow is, imagine that it’s the same thing that powers the almighty Google Translate. However, without Google’s substantial mountains of data (in every possible language) there’s no way someone can compete with Google Translate. What makes Google so powerful? It’s DATA. Google has a lot of data it can leverage, and all that data can be adapted and taught to perform specific tasks.

6. Everyone knows Google Translate, and many people depend on it. But how can the website translate between over 100 human languages so seamlessly? Sure, there are sentences, grammar and structure problems to most translations; but nevertheless, it’s still accurate. At the core of Google Translate there’s a technology called “machine translation”, which has managed to change completely the way people communicate. In the last two years, the approach to basic machine translation has been completely rewritten by deep learning. There’s a pretty fascinating piece of technology hidden behind this breakthrough, known as “sequence to sequence learning”. The technique is pretty powerful and it can be used to fix many problems. We’ve seen what it can do for Google Translate. But did you know that the same algorithms can be applied to describe pictures and write AI chat bots?

7. PayPal Uses Machine Learning to Detect Fraud Detecting Fraud within banks is another Machine Learning Example, let’s explorer in depth how PayPal uses Machine Learning. Fraud detection matters a lot because it addresses a problem that’s urgent, and machine learning can help combat fraud. PayPal for example – an international online payments system that serves as an alternative to conventional payment modes and deals with money transfers online – has dealt with its fair share of fraudulent transactions over the years. So it’s only natural to think that the company would do just about anything to combat fraud.

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