Difference between RDBMS and Excel RDBMS 1. To communicate with RDBMS we use SQL 2. RDBMS maintain a database 3. It is a secured 4. It follows EFCODD Rules 5. It can store Meta Data 6. It follows relational Model Excel 1. We don’t use any language to communicate with excel 2. Does not maintain Continue reading
Programming Language
EFCODD Rules
EFCODD Rules • Data should be a single value attribute • The data entered into a cell must be single value data/automatic data • We can store the data into multiple values • We can establish the connection by using key value attribute • To validate the data we can assign datatypes and constraint • Continue reading
Definition of DBMS and RDBMS
Definition of DBMS and RDBMS DBMS: [Database Management System] is a software which maintains and manages the database, DBMS provides two important features 1. Security: to protect the data 2. Authorization: it is used to access the data. To communicate with DBMS software we use query language Types of DBMS 1. Network DBMS 2. Hardware Continue reading
Difference between DBMS and RDBMS
Difference between DBMS and RDBMS DBMS: 1. Data base management system 2. Store the data in the form of files 3. Hierarchical arrangement of data 4. Allow one user at a time 5. Does not use acid form of storage 6. Manage the data in a computer 7. Does support integrity constraints 8. Cannot be Continue reading
Extensible Markup Language
Extensible Markup Language Xml (Extensible Markup Language) XML stands for extensible markup language, XML is sued to provide on environment to create a cross platform compatibility, cross platform it is not a specific to only software and hardware and any other computer architecture, XML is also Called as mother language by uing which we can Continue reading
Designing A Learning System in Machine Learning
Designing A Learning System in Machine Learning Designing a Learning System In order to get a successful learning system, it should be designed for a proper design, several steps should be followed. Why we need to follow steps because in order to get perfect and efficient system. Steps: Choosing a training example Choosing the target Continue reading
Well posed Machine Learning
Well posed Machine Learning Well posed Machine Learning Simply to say precisely it is nothing but learning by experience. An agent or a Person solves a problem or Task T, Performance P, gain some experience E. By doing this task T with performance P this agent will give some experience is denoted by E. Continue reading
Prospect and issues in Machine Learning
Prospect and issues in Machine Learning Perspectives of machine learning Perspectives of machine learning searching a very large space of possible hypothesis ( it can be a explanation for anything) to determine one that best fits the observed data and any prior knowledge help by learner Example • It represented algorithms Choosing a merge sort Continue reading
Machine Learning Important
machine Learning important Why machine Learning is important Think of machine learning like this as a human. Machine Learning is comprised of algorithms that will teach compiler to Perform some tasks that human begins do naturally on daily basis. Some of the task that cannot be defined well, like recognising the people to recognise the Continue reading
Machine Learning Introduction
Machine Learning Machine Learning Introduction is a field of study that gives computer and capability to learn without being explicitly programmed. Because if a computer being explicitly programmed and it is doing a particular task that is not a big thing ever computer does that, it takes set of instructions and does some particular to Continue reading