Artificial Intelligence: Machine Learning Basics and its Applications

Hello, everyone! In my last article, I compared the two big giants’ products that is the Google Home and the Amazon Echo. Both the devices are smart, with an intelligent voice assistant system in it. They are used to perform various tasks and make task performing simpler for the end user. (You can take a look at the comparison between Google Home and Amazon Echo here). In today’s article, I’ll be talking about the basics of Machine Learning and Artificial Intelligence. After understanding what the basics are, I’ll be discussing some points which make use of Machine Learning and Artificial Intelligence that is their applications. In my previous articles, I have mentioned how companies are making use of AI-based technologies in order to provide better service to the end user. In this articles, I’ll tell what the basics of Machine Learning and AI are and what their prospective applications can be.

Before starting with the basics, I would like to start with few examples which make use of Machine Learning, so that you get an idea of what this field does. One of the biggest examples of Machine Learning is the recommendation engine that we experience. A recommendation engine (recommender system) recommends a different type of videos, ads, products, services to the user based upon their interest. It keeps a track on the user likes and dislikes and based on that, the engine recommends the product or service. Such recommender system is possible because of Machine Learning. One such example of a recommendation engine is YouTube. You might have watched hundreds and thousands of videos on YouTube, based on your interest and you might have noticed that the next time when you log in to YouTube, you get a list of related videos which you might like or which is based on your interest. This becomes possible with Machine Learning and recommendation engine. The system recommends and thus it is termed as a recommendation engine.

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One more example which deals with recommendation engine is Facebook. Millions and billions of people use Facebook throughout the world. Also, Facebook gives a privilege to advertise your business and create a brand awareness. The advertisements that you see is based on your activity of liking and disliking previous ads. It’s purely based on what your interest lies in. For example, if you are looking for shoes on few websites, and then you log in to your Facebook network, you’ll realize that now the frequent ads that you see are showing you only a shoe ad. This is possible again because of Machine Learning and AI.

Another example of Machine Learning is to classify between two items. The classification process is done automatically based on what the user, what item (task) it is that is to be classified and how the user’s previous activity is and many other parameters might also be possible. Consider a scenario where two sets of users are to be classified. One as a spam user and one as a legitimate user. This automated classification of the user can be possible by using Machine Learning and Artificial Intelligence.

Machine Learning as the name suggests, it is making your machine (system) learn from the external environment. It is a field which comes under the Artificial Intelligence umbrella. Machine Learning are computing systems and algorithms that make use mathematical models, in order to give an efficient and productive outcome to the end user. The working of a basic Machine Learning system is performed in 3 major steps:

  1. Initial Knowledge Base and Data to Learn: Every AI problem requires a huge amount of knowledge base (data) to help solve the problem efficiently. If you have more knowledge base and data, more efficient your system becomes. With a huge chunk of data, it becomes difficult to process it, but it makes the overall system highly productive, accurate and reliable. The initial knowledge base helps you in identifying and classifying the unwanted tasks or upcoming tasks in the model (model here meaning the machine learning system, where input from the user or the external environment will be considered). Though many times, there is no knowledge or very less knowledge. At such point also machine learning is used. For every new input, the user gives, the system learns and stores it in the knowledge base that it has. The knowledge base keeps on updating and this helps in increasing the data. Once the knowledge base becomes to use, slowly the efficiency of the system also increases and a much better outcome is given by the system overall
  2. Computational Algorithm to be used to prepare the model: For every machine learning task to be performed, there are a different set of algorithms that are used, in order to give the output accordingly to the end user. There are different uses of machine learning, for example, classification, clustering, predictive purposes and many other. For each of such uses mentioned, there are different types of algorithms that are used. Algorithms have different mathematical complexity and efficiency. Some of the algorithms that are used for machine learning tasks are Regression, Support Vector Machines, K-Means Clustering, Bayesian Approach and much more.
  3. What is the outcome of the model: We have the initial knowledge base (data) and we know the model that are we using for the machine learning task, the third step is to understand what your outcome will be. For any task to complete, we must understand what the input is and what will be the associated output for the same. Once we understand what the output of the system is, it is easy to make the end user understand what the outcome is, why this outcome has been considered and how the end user can make use of such outcome. The outcome here means the output that is generated from the machine learning model.

Performing these three main tasks, your machine learning system can be developed. Though this is just an idea. Developing a machine learning model creates a lot of stuff. It is not that simple to model a machine learning system. First, you need to understand the math behind it. Once you understand the math, you need to be aware of how the math behind the model will be coded in the system. And once these two steps are understood, completing and deploying the machine learning model becomes slightly simpler. The major 3 tasks are mentioned above, that is, to understand the input or the existing knowledge base, next is to understand what kind of algorithm will be useful and lastly what output will be generated for the end user.

Machine Learning is in demand nowadays and it is important that each one of us should know the basics of it and understand how it can be used in your business. To know how it can be used in your business, I will be listing some of the applications that can be helpful to you and might give you an idea on how you can make machine learning work for you. The application of machine learning are as follows:

    1. Recommendation: As stated above while giving an example, machine learning can be used to provide a recommendation to the end user. The recommendation can be of any product or a service. Making this work with high accuracy and reliability might generate new customers for your business and will also retain the existing clientele.
    2. Classification: Machine Learning can be used to classify set of users. Consider that your business has thousands of employees and you need to classify the employees based on their work input. Gone are the days when everything was done using a pen and paper. Now is the time to automate and make your system to the job for you. This is possible by using machine learning.
    3. Prediction: Machine learning systems are used to make a prediction and generate future outcomes. This can help gain a lot of insights for your business which can be in terms of profit outcome, what kind of product will be more consumed and likewise.
    4. And much more where machine learning proves to be boon to the industry.

Talking in terms of real-time applications, ML applications can be used in sectors such as:

  1. Education
  2. Marketing
  3. Technology Industry
  4. Banking sector
  5. Home and Domestic Purposes
  6. Retail industry
  7. Manufacturing sector
  8. And much other industry which can make use of machine learning.

That’s wrap on what the basics of machine learning are and their respective applications. You can also comment your views on the same below.

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Article written and submitted by: Akshay Rakesh Toshniwal

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