ABOUT EVENT

Machine learning is a process where a system learns with experiences without being explicitly programmed. It is the scientific study of algorithms and statistical models that computer system uses to perform some specific task without using explicit programs, relying on some patterns and inference instead. ML can be seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as “training data”, in order to make predictions or decisions to perform the task. In machine learning there are two phase:

1. Training phase:- where we train our model with the help of datasets .In this phase we provide the output attribute to train the model.
2. Testing phase:- Where we test our model that either it is working properly or not. In this phase we do not provide the output attribute to test our model.

So basically we can say that ML works with the help of two phases for efficient result. First we train our model and then we test that. If our model gives accurate result in testing phase than we can easily use our proposed mode for future data analysis.

For the data analysis R Studio will be used. It is a free and open source IDE (integrated development environment) for R programming language. R provides a wide variety of statistical and graphical techniques. R Studio provides a friendly environment to the user in which required packages can be installed easily. A person having a little knowledge of programming can easily use this.

No SQL is used for storing and handling the database at backed because of its highly reliability, flexibility and availability.

AIM of the Events

1. The Objective of this workshop is to provide students an insight of machine learning.
2. After this workshop students will come to learn about how different tools used in machine learning.
3. With the help of this workshop student will learn how R tool can be used for data analysis and how various machine learning algorithm are implemented using R language.
4. This workshop provide an exposure to students about practical working tools in machine learning.
5. How to use tools of machine learning in research field.

Content of the Event

1. Data types
2. Operator
3. Data frames
4. Array and matrix
5. Import data in R environment

Various machine learning algorithms for data analysis, regression