Machine Learning Applications and Use Cases for 2021

Machine Learning is a buzzword these days. To better understand this concept, it doesn’t get much better than understanding the types of problems that machine learning is trying to solve.

This article listed the 10 most common machine learning applications. These use cases will give you an idea of ​​the issues addressed by machine learning and give you a good idea of ​​the types and formats of data used to solve these kinds of problems.

Machine Learning applications

Top 10 Machine Learning Applications and Use Cases

There are many use cases for Machine Learning in real life. Usually, these algorithms are the core of a web or mobile application. This kernel is probably the most “complicated” part of the application. Think about the complexity of Apple’s voice assistant Siri or Amazon’s product recommendation sections. All of these tools are machine learning applications.

Here are 10 examples of machine learning issues to better understand what machine learning really is.

Price Prediction

The algorithm will estimate the value of something ( the price of a house, or the expected earnings of a store) based on previous observations. For example, to calculate the price of a house according to its surface, its location, possibility of Parking or not etc. These estimates are made by observing other similar products in order to draw conclusions.

SPAM Detection

The algorithm will analyze the composition of an email. In particular, the latter’s content, as well as the number of occurrences of the words constituting it, etc. Following this analysis, the algorithm will decide whether an email is SPAM or not. You can implement your first Anti SPAM filter by following this article.

Medical Diagnostic 

Based on a patient’s medical data, the algorithm can diagnose whether the subject has a particular disease. Sometimes these algorithms can alert of a serious health incident before it happens, especially for heart attacks.

Product Recommendation

This system is based on purchase history, online research (Web Tracking) by an Internet user to recommend products that may interest him. Netflix’s movie recommendations are based on this system. For Amazon, this feature is critical as it is at the heart of increasing sales volumes and, therefore, the company’s earnings.

Fraud Detection

These algorithms can detect fraudulent and abnormal behavior. The best-known use of this technique is to detect financial fraud. It is unlikely that an owner of a bank card in France who spends 1,500$ per month suddenly spends 10,000 $ in Slovakia (a country given at random). The algorithm will signal that this is potentially a fraud.

Grouping of items

This type of technique is used in particular for the Apple Iphoto application to group images according to the people there. Usually, the data does not have labels, and the algorithm will try to find similar items and group them in the same group.

Cybersecurity

The increase in the number and complexity of malware is only increasing day by day.  Some companies, in particular DeepInstinct, are based on Deep Learning (a branch of Machine Learning) to offer a cyber defense solution. The DeepInstinct solution is based on the recognition of call routines (the activity of a program) to decide whether it is a malicious program or not.

Speech Recognition

Voice recognition is the basis of systems like Siri or Alexa (Amazon’s virtual assistant). These algorithms will extract the voice and the words and translate them into text. 

Chatbots

Conversational agents are based on NLP (Natural Language Processing) to translate sentences that we write “naturally” into “intentions”. These intentions are used by ChatBots to best respond to the requests of an Internet user. Conversational agents are useful on e-commerce sites and their after-sales service divisions because they provide a feeling of proximity to customers.

Autonomous Driving

By learning the driving behavior of humans, Deep Learning algorithms with reinforcement learning make learning complex tasks such as driving.

Types of Machine Learning Algorithms

machine learning algorithms

Looking at this list above, we see that some issues are similar. It is the facts of the problem we are trying to solve that will change. As for the learning algorithm, it remains generic.

There are three main families of machine learning algorithms:

  • Regression: This type of supervised algorithms will find a continuous value (a real number), which is the prediction of the value of a new given observation. Think about home price prediction based on its features.
  • Classification: These algorithms will classify data in a category. For example, classifying an email as spam or not, deciding if a tumor is malignant or benign. It is also a supervised algorithm.
  • Clustering: This is a family of unsupervised algorithms. Therefore, the data does not have labels (Non-labeled Data). The algorithm will group the data by similarity. For example, we provide a set of photos of animals (without saying which animals they are). The algorithm will group the photos of cats together and the photos of dogs together, etc.

 We have just seen what problems can be solved by machine learning. Just remember that when your solution to a problem can only be modeled by the data that defines it, you have to use machine learning techniques.

In addition, we have just seen the three main categories of Machine Learning categories (Clustering, Regression, and Clustering). Now you will know more intuitively what category it is when you are faced with a machine learning problem.

If you know of other applications of these techniques in real life, please share them in a comment.

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