Post by khatunejannat on Feb 15, 2024 3:05:30 GMT -5
In the last decade the volume of data generated daily has grown exponentially. The large number of new sensors available, the new connectivity capabilities and the use that users make of mobile phones and gadgets has created an immense amount of data that has required new technological solutions to be able to deal with them. SEAS teaches the Big Data and Machine Learning Course to introduce you to the knowledge about the bases of the techniques currently used in Big Data, Virtualization and Machine Learning. What is Big Data and how is it used? In the first part of the course we will see the causes that have motivated the appearance of what we know today as BigData technologies . The different types of databases, relational and non-relational, that exist and the uses, advantages and disadvantages of each of them will be explained. In addition, the bases of techniques such as MapReduce, which allows distributed data processing, will be presented.
These BigData technologies require servers to host them. Years ago, physical computers were required for each of the servers Cuba Email List that a company used, which made maintenance, replication, and scaling of services really complex and expensive. Virtualization allows you to create computers and servers within others. This allows multiple identical machines to be created in several seconds to meet a spike in request demand, a data processing packet, or respond to a denial of service attack. In the next unit, the differences between the different types of “virtualization” will be presented, the scenarios in which virtualization is proposed and used will be explained, and finally the data center infrastructures from which commercial virtualization services are provided will be studied.
And once we have the data, what do we do with it? This is the big question that many people ask. In order to even formulate the question, it is necessary to be clear about some statistical concepts . In the module we will review the main concepts that will help us understand the data, clean it, organize it and explore it visually to be able to carry out the first analyzes of the sample with which we want to work. Big Data and Machine Learning Course Current use of Marchine Learning Finally, the course presents the main Machine Learning techniques that are currently used. What is Machine Learning? It is an umbrella under which we can find a multitude of techniques, statistical models and algorithms that allow us to create models with the data we have available.
These BigData technologies require servers to host them. Years ago, physical computers were required for each of the servers Cuba Email List that a company used, which made maintenance, replication, and scaling of services really complex and expensive. Virtualization allows you to create computers and servers within others. This allows multiple identical machines to be created in several seconds to meet a spike in request demand, a data processing packet, or respond to a denial of service attack. In the next unit, the differences between the different types of “virtualization” will be presented, the scenarios in which virtualization is proposed and used will be explained, and finally the data center infrastructures from which commercial virtualization services are provided will be studied.
And once we have the data, what do we do with it? This is the big question that many people ask. In order to even formulate the question, it is necessary to be clear about some statistical concepts . In the module we will review the main concepts that will help us understand the data, clean it, organize it and explore it visually to be able to carry out the first analyzes of the sample with which we want to work. Big Data and Machine Learning Course Current use of Marchine Learning Finally, the course presents the main Machine Learning techniques that are currently used. What is Machine Learning? It is an umbrella under which we can find a multitude of techniques, statistical models and algorithms that allow us to create models with the data we have available.