This is going to be purely technical as you would learn about how Neural Networks are classified. So, let’s get started!
SUPERVISED LEARNING
A Data Mining task of inferring a function from labeled training data. In Supervised Learning, each Example is a pair, consisting of an Input object (typically a Vector) and the desired Output value (also called the Supervisory Signal).
A supervised learning algorithm analyses the training data and produces an inferred function, which can be used for mapping new examples. An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances. This requires the learning algorithm to generalise from the training data to unseen situations in a “reasonable” way.
We provided the system with Right answers as training samples, and in turn, it produces more right answers based on this training data. In short, we are given a data set and already know what our correct output should look like, having the idea that there is a relationship between the input and the output.
Supervised learning problems are categorised into “regression” and “classification” problems. In a regression problem, we are trying to predict results within a continuous output, meaning that we are trying to map input variables to some continuous function. Moreover, in a classification problem, we are instead trying to predict results in a discrete output. In other words, we are trying to map input variables into discrete categories.
Regression
Given a picture of a person, we have to predict their age on the basis of the given picture
Classification
Given a patient with a tumor, we have to predict whether the tumor is malignant or benign.
SUPERVISED LEARNING ALGORITHMS
All classification and regression algorithms come under supervised learning.
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- Logistic Regression
- Decision trees
- Support vector machine (SVM)
- k-Nearest Neighbours
- Naive Bayes
- Random forest
- Linear regression
- Polynomial regression
- SVM for regression
UNSUPERVISED LEARNING
A type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. The most common unsupervised learning method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or grouping in data.
Unsupervised learning allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don’t necessarily know the effect of the variables.
Furthermore, we can derive this structure by clustering the data based on relationships among the variables in the data. With unsupervised learning, there is no feedback based on the prediction results.
Clustering
Take a collection of 1,000,000 different genes, and find a way to automatically group these genes into groups that are somehow similar or related by different variables, such as lifespan, location, roles, and so on.
Non-clustering
The “Cocktail Party Algorithm”, allows you to find structure in a chaotic environment. (i.e. identifying individual voices and music from a mesh of sounds at a cocktail party).
UNSUPERVISED LEARNING ALGORITHMS
All clustering algorithms come under unsupervised learning algorithms.
- K – means clustering
- Hierarchical clustering
- Hidden Markov models
SEMI-SUPERVISED MACHINE LEARNING
Problems where you have a large amount of input data (X) and only some of the data is labeled (Y) are called semi-supervised learning problems. These problems sit in between both supervised and unsupervised learning.
A good example is a photo archive where only some of the images are labeled, (e.g. dog, cat, person) and the majority are un-labeled.
Many real world machine learning problems fall into this area. This is because it can be expensive or time-consuming to label data as it may require access to domain experts. Whereas un-labeled data is cheap and easy to collect and store.
Unsupervised learning techniques can be used to discover and learn the structure in the input variables, whereas Supervised learning techniques can be used to make best guess predictions for the un-labeled data, feed that data back into the supervised learning algorithm as training data and use the model to make predictions on new unseen data.
SUMMARY:
- Supervised: All data is labeled and the algorithms learn to predict the output from the input data.
- Unsupervised: All data is un-labeled and the algorithms learn to inherent structure from the input data.
- Semi-supervised: Some data is labeled but most of it is un-labeled and a mixture of supervised and unsupervised techniques can be used.
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