Question

Business analytics MBA-

There are a number of learning scenarios or types of learning algorithms, that can be used depending on whether a target variable is available and how much labeled data can be used. These approaches include supervised, unsupervised, and semi-supervised learning. Explain the difference between each type of machine learning. Give an example of how each is used. Write your responses in detail with examples. Be sure to identify the source of your example in your posting. Your initial post should be of minimum 250 words.

Answer 1

Supervised learning:

Supervised learning is one of the most basic types of machine learning. In this type, the machine learning algorithm is trained on labeled data. Even though the data needs to be labeled accurately for this method to work, supervised learning is extremely powerful when used in the right circumstances.

In supervised learning, the Machine Learning algorithm is given a small training dataset to work with. This training dataset is a smaller part of the bigger dataset and serves to give the algorithm a basic idea of the problem, solution, and data points to be dealt with. The training dataset is also very similar to the final dataset in its characteristics and provides the algorithm with the labeled parameters required for the problem.

Unsupervised Learning:

Unsupervised machine learning holds the advantage of being able to work with unlabeled data. This means that human labor is not required to make the dataset machine-readable, allowing much larger datasets to be worked on by the program.

Unsupervised learning does not have labels to work off of, resulting in the creation of hidden structures.The creation of these hidden structures is what makes unsupervised learning algorithms versatile. Instead of a defined and set problem statement, unsupervised learning algorithms can adapt to the data by dynamically changing hidden structures. This offers more post-deployment development than supervised learning algorithms.

Semi-supervised learning:

Semi-supervised learning combines a small amount of labeled data with a large amount of unlabeled data during training. Semi-supervised learning falls between unsupervised learning (with no labeled training data) and supervised learning (with only labeled training data).

Unlabeled data, when used in conjunction with a small amount of labeled data, can produce considerable improvement in learning accuracy. The acquisition of labeled data for a learning problem often requires a skilled human agent or a physical experiment. The cost associated with the labeling process thus may render large, fully labeled training sets infeasible, whereas acquisition of unlabeled data is relatively inexpensive. In such situations, semi-supervised learning can be of great practical value. Semi-supervised learning is also of theoretical interest in machine learning and as a model for human learning.

Difference between each type of machine learning:

Supervised Learning	UnSupervised Learning	Semi-Supervised Learning
Lables data which makes life easy for algorithms to learn and predict data.	Unlabled data only analyzed.Learning happens without supervison.	Some data is labled some is not labled.Uses unsupervised methods to improve supervised algorithms.
Methods are Classification and Regression.	Methods are Clustering and Association.	Methods are Graph-based methods,Hauristic approaches.
Mapping functions takes input and matches to output then creates target function.	Inputs are used to create the data.	Combination of both processes.
Gives direct feedback.	No feedback is given.	Reduces iteration of feedback.

Examples of Supervised Learning:

BioInformatics,Speech Recognition,Spam Detection,Spam Detection,Object-Recognition for Vision.

Examples of UnSupervised Learning:

Finding customer segments,Reducing the complexity of a problem,Feature selection.

Examples of Semi-Supervised Learning:

Classifier for Images,Speech Analysis,Internet Content Classification,Protein Sequence Classification.