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How To Improve Data Quality When With Unsupervised Machine Learning

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There won't be any business insights if the data quality is poor. When preparing data, I often go through many different approaches to reach a level of quality of data that can provide accurate results. In this article, I describe how unsupervised ML can help in data preparation for machine learning projects and how it helps to get more accurate business insights. For accurate predictions, the data must not only be properly labeled, de-deputed, broad, consistent, etc. The point is that the machine learning model should process the "right" data.


An Important Guide To Unsupervised Machine Learning

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We're living in an era of digital switch-over with only one constant โ€“ evolve. And that digital transformation is being introduced by high-tech solutions. Hence, it comes as no surprise that mundane business tasks are being completely taken over by tech advancements. Machines, artificial intelligence (AI), and unsupervised learning are reshaping the way businesses vie for a place under the sun. With that being said, let's have a closer look at how unsupervised machine learning is omnipresent in all industries.


Understanding data mining clustering methods

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When you go to the grocery store, you see that items of a similar nature are displayed nearby to each other. When you organize the clothes in your closet, you put similar items together (e.g. Every personal organizing tip on the web to save you from your clutter suggests some sort of grouping of similar items together. Even we don't notice it, we are involved in grouping similar objects together in every aspect of our life. This is called clustering in machine learning, so in this post I will provide an overview of data mining clustering methods.


Unsupervised learning explained

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Despite the success of supervised machine learning and deep learning, there's a school of thought that says that unsupervised learning has even greater potential. The learning of a supervised learning system is limited by its training; i.e., a supervised learning system can learn only those tasks that it's trained for. By contrast, an unsupervised system could theoretically achieve "artificial general intelligence," meaning the ability to learn any task a human can learn. If the biggest problem with supervised learning is the expense of labeling the training data, the biggest problem with unsupervised learning (where the data is not labeled) is that it often doesn't work very well. Nevertheless, unsupervised learning does have its uses: It can sometimes be good for reducing the dimensionality of a data set, exploring the pattern and structure of the data, finding groups of similar objects, and detecting outliers and other noise in the data.


Unsupervised Machine Learning: Clustering Analysis โ€“ Towards Data Science

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Up to know, we have explored just supervised Machine Learning algorithms and techniques to develop models where the data had label previously known. In other words, our data had some target variables with specific values that we used to train our models. However, when dealing with real-world problems, most of the time, data will not come with predefined labels, so we will want to develop machine learning models that can classify correctly this data, by finding by themselves some commonality in the features, that will be used to predict the classes on new data. In summary, the main goal is to study the intrinsic (and commonly hidden) structure of the data. This techniques can be condensed in two main types of problems that unsupervised learning tries to solve.