What is supervised machine learning and how does it relate to unsupervised machine learning? A problem that sits in between supervised and unsupervised learning called semi-supervised learning.The main difference between these types is the level of availability of ground truth data, which is prior knowledge of what the output of the model should be for a given input.

Supervised learning aims to learn a function that, given a sample of data and desired outputs, approximates a function that maps inputs to outputs.
Semi-supervised learning aims to label unlabeled data points using knowledge learned from a small number of labeled data points.
Unsupervised learning does not have (or need) any labeled outputs, so its goal is to infer the natural structure present within a set of data points.

1. Supervised Machine Learning

Supervised learning is the process where you have input variables (X) and an output variable (Y) and you use an algorithm to learn the mapping function from the input to the output.
Y = f(X)
The goal is to approximate the mapping function so well that when you have new input data (X) that you can predict the output variables (Y ) for that data. It is called supervised learning because the process of an algorithm learning from the training dataset can be thought of as a teacher supervising the learning process. We know the correct answers; the algorithm iteratively makes predictions on the training data and is corrected by the teacher.

Supervised learning problems further grouped into regression and classification problems.
Classification: A classification problem is when the output variable is a category, such as red or blue or disease and no disease.
Regression: A regression problem is the output variable is a real value, such as dollars or weight.

Some popular examples of supervised machine learning algorithms are:
Linear regression for regression problems.
Random forest for classification and regression problems.
Support vector machines for classification problems.

Strengths: Outputs always have a probabilistic interpretation, and the algorithm can be regularized to avoid overfitting.
Weaknesses: Logistic regression may underperform when there are multiple or non-linear decision boundaries. This method is not flexible, so it does not capture more complex relationships.

2. Unsupervised Machine Learning

Unsupervised learning is a machine learning technique, where you do not need to supervise the model. Instead, you need to allow the model to work on its own to discover information. It mainly deals with the unlabeled data. Algorithms are left to their own devises to discover and present the interesting structure in the data. Unsupervised learning is very useful in exploratory analysis because it can automatically identify structure in data. For example, if an analyst were trying to segment consumers, unsupervised clustering methods would be a great starting point for their analysis. In situations where it is either impossible or impractical for a human to propose trends in the data, unsupervised learning can provide initial insights that can then be used to test individual hypotheses.

Unsupervised learning problems can be further grouped into clustering and association problems.
Clustering: Clustering is an important concept when it comes to unsupervised learning. It mainly deals with finding a structure or pattern in a collection of uncategorized data. Clustering algorithms will process your data and find natural clusters(groups) if they exist in the data.
Association: An association rule learning problem is where you want to discover rules that describe large portions of your data. This unsupervised technique is about discovering exciting relationships between variables in large databases.

Some popular examples of unsupervised learning algorithms are:
k-means for clustering problems.
Apriori algorithm for association rule learning problems.

3. Semi-supervised Machine Learning

The biggest difference between supervised and unsupervised machine learning is: Supervised machine learning algorithms are trained on datasets that include labels added by a machine learning engineer or data scientist that guide the algorithm to understand which features are important to the problem at hand. This is a very costly process, especially when dealing with large volumes of data.Unsupervised machine learning algorithms, on the other hand, are trained on unlabeled data and must determine feature importance on their own based on inherent patterns in the data, The most basic disadvantage of any Unsupervised Learning is that it’s application spectrum is limited.

To counter these disadvantages, the concept of Semi-Supervised Learning was introduced. In this type of learning, the algorithm is trained upon a combination of labeled and unlabeled data. Typically, this combination will contain a very small amount of labeled data and a very large amount of unlabeled data. This is useful for a few reasons. First, the process of labeling massive amounts of data for supervised learning is often prohibitively time-consuming and expensive. What’s more, too much labeling can impose human biases on the model. That means including lots of unlabeled data during the training process actually tends to improve the accuracy of the final model while reducing the time and cost spent building it. You can use unsupervised learning techniques to discover and learn the structure in the input variables. You can also use supervised learning techniques to make best guess predictions for the unlabeled data, feed that data back into the supervised learning algorithm as training data and use the model to make predictions on new unseen data.

Practical applications of Semi-Supervised Learning:-
Speech Analysis: Since labeling of audio files is a very intensive task, Semi-Supervised learning is a very natural approach to solve this problem.
Internet Content Classification: Labeling each webpage is an impractical and unfeasible process and thus uses Semi-Supervised learning algorithms.
Protein Sequence Classification: Since DNA strands are typically very large in size, the rise of Semi-Supervised learning has been imminent in this field.

Good Reads and References :-
https://www.geeksforgeeks.org/ml-semi-supervised-learning/
https://blogs.oracle.com/datascience/what-is-semi-supervised-learning
https://www.guru99.com/supervised-vs-unsupervised-learning.html