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All about Machine Learning and its application in major industries

1 July, 2022 AI & Machine Learning

Machine Learning is a modern creation that helps people not only in industrial scale and professional processes but also in everyday life. The article will give the definition of this kind of technology and its specific applications nowadays. 

I. What is Machine Learning?

It is a field of science, more specifically a sub-branch of artificial intelligence. It is mainly for algorithms to discover patterns, namely repetitive patterns, in datasets. These data can be numbers, characters, pictures, statistics… Anything that can be stored digitally can be used as data for Machine Learning. By exploring patterns in the data, algorithms learn and improve their performance when executing a task.

In short, Machine Learning algorithms automatically learn how to perform a task or make predictions from the data and improve their performance over time.

II. How does this technology work?

The development of a ML model is based on four basic steps.

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4 steps of the development process of a ML model

The first step selects and prepares a dataset for training. This data will be given to Machine Learning models to learn how to solve problems according to their design. The data can be labelled to let the model know what characteristics it needs to identify. The data may also not be labelled, and the model will only need to identify and derive the characteristics over and over again. Either way, the data needs to be carefully prepared, organized and cleaned. Otherwise, the training of the ML model may be misleading. As a result, its future predictions will be directly affected. 

The second step is to choose an algorithm to run the training dataset. The type of algorithm depends on the type and amount of training data and the type of problem to be solved. 

The third step is algorithm training and this is an iterative process. The variables are run through the algorithm, and the results are compared to the goal to be achieved. The variables will then be re-executed until the majority algorithm yields the expected results. The algorithm trained in this way is called the Machine Learning Model. 

The final step is to use and improve the model. The model is used on new data, the data source depends on the problem to be solved. For example, a Machine Learning model is designed to detect spam in emails, or a vacuum robot’s Machine Learning model enters data from real-world interactions such as moving furniture or adding new items to a room. Performance and accuracy can be improved over time. 

III. 3 kinds of Machine Learning

There are many types of this technology algorithms:

3.1. Supervised learning

This is the most common technique – data is labelled so that the machine knows which pattern to look for. The system trains on a labelled dataset, along with the information it must identify. The data may have been classified along with the system. This method requires less training data than other methods and makes the training process easier because the model results can be compared with the labelled data. However, the labelled data is quite expensive. A model can also be skewed by training data and will affect performance later when processing new data. 

3.2. Unsupervised learning

The data is not labelled, just simply find the possible patterns by scanning. It enters large amounts of data, and uses algorithms to derive the right characteristics to label, sort, and classify data in real-time without human intervention. Without automating decisions and predictions, this method identifies patterns and relationships that people may not be able to identify in the data. This technique is not common because it is more complicated to apply.

However, it is becoming more and more popular in the field of digital security. During training, a smaller labelled dataset is used to guide classification and derive characteristics from a larger unlabeled dataset. This method is useful when labelled data is not sufficient for supervised algorithm training. 

3.3. Reinforcement learning

Letting an algorithm learn from its mistakes to achieve its purpose. The algorithm will try to apply various methods to achieve the set goal. Based on performance, it will be rewarded or penalized to encourage it to continue or change the approach. This technique is used to enable AI to surpass humans in games. 

IV. Machine Learning applications

Currently, Machine learning has been used in many fields and industries.

4.1. Image/Face recognization

This is considered to be the most popular application of Machine Learning. Currently, many cases need to use facial recognition, mainly for security needs such as investigation, crime identification, forensic support, and unlocking the phone,…

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Face recognization based on Machine Learning technology

4.2. Automatic voice recognition

Automatic voice recognition is used by the app to convert voice into digital text. This technology supports the identification of users based on their voices. In addition, they help users perform simple actions through their voices. Vocabulary and voice patterns are incorporated into the system to train the operating model. Currently, voice recognition systems are used in the following areas:

  • Industrial robot
  • Defence and aviation
  • Telecommunications industry
  • Information Technology and Consumer Electronics 

4.3. Finance & Bank industry

Machine Learning algorithms are capable of monitoring and evaluating user behavior. This makes the process of detecting fraud or non-transparency easier. Besides, they also use machine learning to check illegal money laundering. Through the aid of algorithms, this technology helps to make better trading decisions by analyzing thousands of data at a time. 

In addition, this solution is very effective in calculating credit scores and issuance guarantees. 

4.4. Sale & Marketing industry

Machine Learning possesses algorithms that identify potential customers based on website visits, clicks, downloads, opened emails, etc. Through collected data, the business will shape more effective marketing strategies. Machine Learning also supports the analysis of consumer emotions to gauge their reactions to the product. In addition, chatbots are also increasingly improved with Machine Learning.

Read more: How AI Chatbot technology has conquered customer relationships?

4.5. Healthcare industry

One of the common applications of Machine Learning is pathological diagnostics, even dangerous diseases. This solution is also used during radiation therapy for cancer patients. ML technology occurs in the field of medicine, and drug formulation. In addition, Machine learning can make outbreak predictions. Many scientists around the world are using this technology to predict disease outbreaks. 

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Machine learning applications in disease diagnosis

Through this article, you can see that it is not by chance that Machine learning has received much interest in recent years. This technology solution offers great features that help people move into a more modern and advanced future. It can be said that Machine Learning is an amazing breakthrough in the field of Artificial Intelligence. If you’re looking for AI/Machine Learning solution, feel free to contact us!

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