Machine learning was the buzzword of the last decade. There are very few domains now in which the magic of machine learning is not evident. Especially in the highly lucrative advertising business, machine learning is now in use more widely than ever.
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Every time you visit a website, every time you search for a particular term on the internet, the data you generate is ‘learned.’ This data is then used to provide you with targeted advertising, ensuring that every user receives different advertisements, regardless of the webpage the user visits.
How Machine Learning Works
So how does machine learning work? In its work, machine learning is very similar to the human brain. Its data is continuously updated, and it is always learning from the new information that it receives. Machine learning involves two types of sets – a test set and a training set. The training set is basically a set of data that represents all the data that the machine learning model will be making predictions for.
Importantly, we have the information for the training and test sets to predict the complete data. Once the machine learning model you have built has recognized a pattern in the training set, it is tested for efficacy on the test set. This back and forth continues till the model reaches a particular level of efficacy.
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Types of Machine Learning
Machine learning has its own types. The two main types of machine learning are the following.
- Supervised Learning
- Unsupervised Learning
In its early form and in the form in which it was explained in the previous section, machine learning was generally synonymous with supervised learning until not very long ago in supervised learning. The training set and the test set will both have labeled data.
Labeled data is the type of data in which all the important data fields, including the field which is to be predicted by the model, are duly labeled so that the model may learn effectively. Supervised learning is entirely experience-based learning and is great if you wish to optimize your model’s performance.
Unsupervised learning is the type of machine learning in which all of the data is unlabelled. Rather, the machine learning model is given free rein to distinguish patterns from among the data provided to it. Unsupervised learning can often throw up unpredictable results and even help discover new patterns in large sets of data. The data you will generally receive will seldom be labeled, and unsupervised learning models are meant for unlabeled data.
There are several disadvantages to both supervised learning and unsupervised learning. The greatest and most evident disadvantage of supervised learning is the fact that most data is unlabelled. To make supervised learning work on a set of data, all of the data often has to be extracted and hand-labeled, which is an exacting process and might nullify all the benefits of using machine learning on your data you.
Unsupervised learning does not require labeled data, but the base of potential applications for purely unsupervised learning is, unfortunately, rather limited.
Semi-supervised learning is a type of machine learning that provides a great middle path between supervised learning and unsupervised learning. Admittedly, semi-supervised learning veers a bit toward the supervised end of the machine learning spectrum. The prerequisite for any semi-supervised learning model is a set of unlabelled data, out of which a minor amount of data has been extracted and manually labeled.
This is a significant benefit over a purely supervised model, in which all the data needs to be labeled. Hence, semi-supervised learning is associated with savings of cost as well as time. As compared to an unsupervised model, a supervised model, if used with even a small amount of labeled data, can reduce computational resources and improvements in the model’s accuracy.
The Assumptions of Unsupervised Learning
When any use of unlabeled data is involved, it must be associated in some way with the underlying data. When using a semi-supervised machine learning model, certain assumptions about data are made. These assumptions are the following.
Continuity Assumption: This is an assumption that points on a scatter plot representing all of the data closer to each other are more likely to have the same label. This is also a major assumption generally used for supervised learning models. This assumption makes it easy for the semi-supervised model to form legible decision boundaries.
Cluster Assumption: This assumes that data has a natural predilection to form clusters and that data points that are a part of the same cluster have the same label. However, a caveat to this assumption is that two or more clusters may also have data that belongs to the same label. This assumption is of great use in clustering algorithms. This is very similar to the previous assumption and may be treated as a special case of the continuity assumption. The cluster assumption is of great use when the determination of decision boundaries is required, similar to the continuity assumption.
Manifold Assumption: This assumes that the dimensions of the input space’s manifold are significantly higher than that on which the data lies. Once this assumption has been made, he labeled, and unlabelled data can be learned as per the common manifold. Once the manifold has been established, densities and distance among points of the data can be measured. This is a useful assumption when the number of dimensions in the data is very high and iterates that the number of dimensions that govern data categorization into different labels will be comparatively lower.
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Applications of Semi-Supervised Learning
A major complaint with unsupervised learning is that the number of potential applications is rather low. The results obtained through an unsupervised model can often be rather redundant or unusable. In comparison, semi-supervised learning does have a robust set of applications where it can be utilized.
The Classification of Content on the Internet: The internet is a vast trove of web pages, and it cannot be expected that every page will be labeled and have all the data for the field that you desire. However, at the same time, it is true that over the years, some minority of web pages will have been labeled for one dimension or the other.
This can be used for the classification of web pages. A set of labeled web pages can be used to predict the label of all the other web pages that you need. Several search engines use a semi-supervised learning model to label and rank web pages in their search results, including Google.
Image and Audio Analysis: The analysis of images and audio is among the most common uses of semi-supervised learning models. This type of data is typically unlabelled. Human expertise can label a minor proportion of the data instead of classifying each image or piece of audio for a particular field over days and months. Once this small proportion of data has been classified, you can simply utilize the trained algorithm to classify all the other data that you have.
Classification of Protein Sequences: This is a relatively new application of semi-supervised learning. Protein sequences contain many amino acids, and it is impractical to analyze every protein sequence and classify it as one type or the other. This task can be easily completed with the use of semi-supervised learning. All you require is a database of already sequined proteins, and the model itself can sequence the rest.
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Semi-supervised learning offers great moderation among the advantages and disadvantages of supervised and unsupervised learning. It also ensures that a large amount of generated or available data can be used in one model or the other to obtain meaningful insights. The usage of this type of model is only likely to increase in the coming years.
Machine learning is one of the most influential technologies in the world. That’s a big reason why it is so popular nowadays.
Many industries employ machine learning for different purposes so the demand increases day by day. If you would like to know more about careers in Machine Learning and Artificial Intelligence, check out IIIT-B and upGrad’s PG Diploma in Machine Learning and AI Program.