Introduction To machine Learning | letsbug

R Language

•  R is a programming language and software environment for statistical analysis, graphics representation and reporting.
• R is freely available under the GNU General Public License, and pre-compiled binary version are provided for various operating systems like Linux, Window and Mac.
• R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently developed by the R Development Core Team.

Machine Learning

• Machine Learning must be one of the fastest growing fields in computer science. It is not only the data is continuously getting "bigger" but also the theory to process it and turn it into knowledge.
• There are several ways to implement machine learning techniques, however the most commonly used ones are supervised and unsupervised learning.

Supervised Learning And Unsupervised Learning

• Supervised Learning is all about operating to a known expectation and in this case, what needs to as "labeled" datasets. It includes Support Vector Machines(SVMs), Naive Bayes classifiers etc.
• Unsupervised Learning accept unlabeled data and attempt to group observations into categories based on underlying similarities in input features, Cluster analysis, K-means clustering etc. are all examples of unsupervised machine learning.
• Cluster analysis or clustering is the task to grouping a set of objects in such a way that objects in the same group ( called a cluster ) are more similar (in some sense ) to each other than to those in other groups(clusters).
• K-Nearest Neighbors(KNN) is a supervised learning algorithm. The output depends on whether k-NN is used for classification ( the output is a class membership. An  object is classified by a majority vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors ( k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbors) or regression (the output is the property value for the object. This value is the average of the values of its k nearest neighbors).
• Classification, also called categorization, is machine learning technique that uses known data to determine how the new data should be classified  into a set of existing labels/classes/categories.
• Naive Bayes classifier is a simple technique for constructing classifiers. A Bayes classifier constructs models to classify problem instances. These classifications are made using the available data.