Application of Machine learning

Machine learning has improved our lives in a number of wonderful ways. We have talked about some very good projects (Top 10 projects) of machine learning and their applications in one of our previous posts. Today, let's talk about some other applications.

1. Virtual Personal Assistants

First, let's talk about virtual personal assistants. Google Assistant, Alexa, Cortana and Siri. Now, we've all used one of these at least at some point in our lives.

These help improve our lives in a great number of ways. For example, you could tell them to call someone. You could tell them to play some music. You could tell them to even schedule an appointment for you. 

So, how do these things actually work? First, they record whatever you're saying. Send it over to a server, which is usually in a cloud. Decode it with the help of machine learning and neural networks. And then provide you with an output.

So, if you've ever noticed that these systems don't work very well without the internet, that's because the server couldn't be contacted. 

2. Traffic Prediction 

Next, let's talk about traffic predictions. Now, say I wanted to travel from Buckingham Palace to Lord's Cricket Ground. The first thing I would probably do is to get on Google Maps to search for the path. After searching, the Google map will show me all the paths that will take me to Lord' cricket ground . In the map of the path you will notice combination of three colours, red, yellow and blue. The blue region signify the clear road with no or very less traffic. Yellow indicate that the road is slightly congested while the red signify heavily congested road.

So, how exactly is Google able to tell you that the traffic is clear, slow moving or heavily congested? So, this is with the help of machine learning and with the help of two important measures. 

First is the average time that's taken on specific days at specific times on that route. Second one is the real-time location data of vehicles from Google Maps and with the help of sensors.

Some of the other popular map services are Bing Maps,

3. Social Media Personalisation

Next up, we have social media personalization. So, say I want to buy a drone and I'm on Amazon and I want to buy a DJI Mavic Pro. The thing is, it's close to one lap. So, I don't want to buy it right now. But the next time I'm on Facebook, I'll see an advertisement for the product. Next time I'm on YouTube, I'll see an advertisement of the same drone. Even on Instagram, I'll see its advertisement. 

So, here with the help of machine learning, Google, Facebook and Instagram had understood that I'm interested in this particular product. Hence, it's targeting me with these advertisements. This is also with the help of machine learning. 

4. Email Spam Filtering 

Let's talk about email spam filtering. How does Gmail know what's spam and what's not spam? So, Gmail has an entire collection of emails which have already been labeled as spam or not spam. So, after analyzing this data, Gmail is able to find some characteristics like the word lottery or winner. Any new email that comes to your inbox goes through a few spam filters to decide whether it's spam or not.

Some of the popular spam filters that Gmail uses is content filters, header filters, general blacklist filters and so on.

5. Online Froud Detection

Next, we have online fraud detection. Now, there are several ways that online fraud can take place. For example, there's identity theft where they steal your identity. Fake accounts where these accounts only last for how long the transaction takes place and stop existing after that and man in the middle attacks where they steal your money while the transaction is taking place. 

The feed forward neural network helps determine whether a transaction is genuine or fraudulent. So, what happens with feed forward neural networks are that the outputs are converted into hash values. And these values become the inputs for the next round. So, for every real transaction that takes place, there's a specific pattern. A fraudulent transaction would stand out because of the significant changes that it would cause with the hash values. 

6. Stock Market Trading

Machine learning is used extensively when it comes to stock market trading. Now, you have stock market indices like Nikay. They use long short term memory neural networks. These are used to classify process and predict data when there are time lags of unknown size and duration. This is used to predict stock market trends. 

7. Assistive Medical Technology

Now, medical technology has been innovated. With the help of machine learning, diagnosing diseases has been easier. From which we can create 3D models that can predict where exactly there are lesions in the brain. It works just as well for brain tumours and ischemic stroke lesions. They can also be used in fetal imaging and cardiac analysis.

Now, some of the medical fields that machine learning will help assist in are disease identification, personalized treatment, drug discovery, clinical research and radiology. 

8. Auto Translation 

Finally, we have automatic translation. Now, let's say you're in a foreign country and you see billboards and signs that you don't understand. That's where automatic translation comes of help.

Now, how does automatic translation actually work? The technology behind it is the same as the sequence to sequence learning, which is the same thing that's used with chatbots. Here, the image recognition happens using convolutional neural networks and the text is identified using optical character recognition. 

Furthermore, the sequence to sequence algorithm is also used to translate the text from one language to the other. And this brings us to the end of this post. I hope you guys found this post useful.

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