In our previous two articles we have discussed about basic of machine learning and it’s general steps which each ML application should follow.In this article we will discuss about various application of machine learning.

Machine Learning Applications

1 Document classification

Document classification is one of the basic application of Machine Learning (ML) in the form of Natural Language Processing (NLP). By classifying text, we are aiming to assign one or more classes or categories to a document, making it easier to manage and sort. This is especially useful for publishers, news sites, blogs or anyone who deals with a lot of content.

2 Website categorization

Website categorization is a part of document classification application of machine learning. As earlier said assign two or more classes or categories to a website which is usefull for publishers,news sites, blogs etc.

3 Topic modeling

In machine learning and natural language processing, a topic model is a type of statistical model for discovering the abstract “topics” that occur in a collection of documents.

Topic modeling is a frequently used text-mining tool for discovery of hidden semantic structures in a text body.

4 Image classification

Image classification is perhaps the most important part of digital image analysis. Deep learning excels in recognizing objects in images as it’s implemented using 3 or more layers of artificial neural networks where each layer is responsible for extracting one or more feature of the image

Remote sensing

5 Playing Video Games Automatically

machine learning can be used to teach a neural network how to play video games.

One of the first attempts to do this that was successful was the application of Google’s DeepMind. Ultimately, Google’s AlphaGo beat the world master as a game called Go.

6 Search Engine Result Refining

Google and other search engines use machine learning to improve the search results for you. Every time you execute a search, the algorithms at the backend keep a watch at how you respond to the results.

If weopen the top results and stay on the web page for long, the search engine assumes that the the results it displayed were in accordance to the query. Similarly, if we reach the second or third page of the search results but do not open any of the results, the search engine estimates that the results served did not match requirement. This way, the algorithms working at the backend improve the search results.

7 Virtual Personal Assistants

Siri, Alexa, Google Now are some of the popular examples of virtual personal assistants.

Machine learning is an important part of these personal assistants as they collect and refine the information on the basis of your previous involvement with them. Later, this set of data is utilized to render results that are tailored to your preferences.

8 Predictions while Commuting

8.1 Traffic Predictions

Machine learning helps to estimate the regions where congestion can be found on the basis of daily experiences.

8.2 Online Transportation Networks

When booking a cab, the app estimates the price of the ride. When sharing these services, how do they minimize the detours? The answer is machine learning. Uber use ML to define price surge hours by predicting the rider demand. In the entire cycle of the services, ML is playing a major role.

9 Online Fraud Detection

Machine learning is proving its potential to make cyberspace a secure place and tracking monetary frauds online is one of its examples.

For example: Paypal is using ML for protection against money laundering. The company uses a set of tools that helps them to compare millions of transactions taking place and distinguish between legitimate or illegitimate transactions taking place between the buyers and sellers.

10 Videos Surveillance

The video surveillance system nowadays are powered by AI that makes it possible to detect crime before they happen. They track unusual behaviour of people like standing motionless for a long time, stumbling, or napping on benches etc.

11 Social Media Services

From personalizing your news feed to better ads targeting, social media platforms are utilizing machine learning for their own and user benefits.

Such example include people you may know of facebook, face recognization and similar pins of pinterest etc.

12 Banking & Financial services

ML can be used to predict the customers who are likely to default from paying loans or credit card bills. This is of paramount importance as machine learning would help the banks to identify the customers who can be granted loans and credit cards.

13 Healthcare

It is used to diagnose deadly diseases (e.g. cancer) based on the symptoms of patients and tallying them with the past data of similar kind of patients.

14 Retail

It is used to identify products which sell more frequently (fast moving) and the slow moving products which help the retailers to decide what kind of products to introduce or remove from the shelf. Also, machine learning algorithms can be used to find which two / three or more products sell together. This is done to design customer loyalty initiatives which in turn helps the retailers to develop and maintain loyal customers.

15 Email Spam and Malware Filtering

There are a number of spam filtering approaches that email clients use. To ascertain that these spam filters are continuously updated, they are powered by machine learning. When rule-based spam filtering is done, it fails to track the latest tricks adopted by spammers. Multi Layer Perceptron, C 4.5 Decision Tree Induction are some of the spam filtering techniques that are powered by ML.

16 Online Customer Support (Chatbot)

A number of websites nowadays offer the option to chat with customer support representative while they are navigating within the site. However, not every website has a live executive to answer your queries. In most of the cases, you talk to a chatbot.

These bots tend to extract information from the website and present it to the customers. Meanwhile, the chatbots advances with time. They tend to understand the user queries better and serve them with better answers, which is possible due to its machine learning algorithms.

17 Product Recommendations

Top e-commerce websites like Flipkart,myntra use machine learning algorithms to recommend similar product based on the user search history.

18 Automatic Translation

Machine learning can also be used to instantly translate text into another language. Not only this, but it can do the same thing with text on images! In the case of text, the algorithm can learn about how words fit together and translate more accurately.

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