Artificial Intelligence has been developed to a level that it is no longer necessary to explicitly program computers to do certain tasks. It is in itself enough to create a program that learns on its own by analyzing data, reading patterns, and improving from experience. This is what is called ‘Machine Learning.’
The most powerful form of Machine Learning that is being used today is ‘Deep learning,’ which employs what is called a neural network. A neural network is essentially many complex mathematical equations based on a large quantity of data, similar to how a human mind works. As and when new data is provided to such computers, they adapt to the data automatically.
Since 1997, Facts.net is a biggest online facts collection site, ever-growing knowledge base for the world’s most random and interesting facts. We have thousands of facts about nearly anything you could think of, from the cutest animals to the most dangerous crimes, there really is something to please everyone here.
Machine Learning Algorithms have been around for a long time now, but the computers have just recently become fast enough to be able to handle complex tasks. Machine Learning algorithms are often categorized into supervised or unsupervised.
Various Categories of Machine Learning
There are basically four types of machine learning algorithms viz. Supervised Learning, Unsupervised Learning, Semi-supervised Learning, and Reinforcement Learning.
- Supervised Machine Learning: In case of Supervised Machine Learning, humans provide labeled examples of both the input and the desired output. Supervised machine learning is used when previous practices or actions can predict future events that are most likely to occur. For instance, such an algorithm can be used to predict what credit card usages are fraudulent and which borrower is likely to repay their loans.
- Unsupervised Machine Learning: In an Unsupervised Machine Learning algorithm, no labels are used. The algorithm does not figure out what the right output is, but read the data, analyses it, and draws inferences to explain hidden structures in the data.
- Semi-supervised Machine Learning: Between Supervised and Unsupervised machine learning, lays Semi-supervised Machine Learning where desired outputs for only some inputs are given. Semi-supervised algorithms are ideally considered to be the best among all for the purpose of model building.
- Reinforcement Machine Learning: In Reinforcement learning, the computer interacts with its environment by actions and discovers errors or rewards. Like a trial and error method of learning, the machine automatically learns the ideal behavior.
Widespread Usage
We come across Machine Learning algorithms in our everyday lives in the customized newsfeed on almost all social media sites and on E-commerce websites, where the concept is particularly used to predict member preferences. Computers no longer require being taught to perform complex tasks like text translation or image recognition. They learn by themselves. And this is all due to the powerful concept of Machine Learning.
Such algorithms are widely being used across different industry verticals. Machine learning is employed by the financial services sector to identify possible fraud and important data insights, by the healthcare industry to track patient’s real-time stats and to help doctors identify any abnormalities for better diagnostics. The tech giant, Google is using a Reinforcement machine learning algorithm for its self-drive cars, while the Stockbrokers use it to buy or sell stocks based on latest trends.
Future of AI and Machine Learning
Future of Machine Learning shines bright. It already has numerous uses in various fields and spheres of life. In the future, machine learning will assist humans in even more complex of tasks eliminating any scope of human error. What humans take years to perfect, such algorithms can do it in hours. Machine learning has surely come a long way, but it still has a good way to go much ahead.
Data Science, Big Data and Hadoop
We produce around 2.5 Quintillion Bytes of data in a day, and that is over 50000 GB of data every hour. All this data would go to waste if not put to some productive use. However, if this data is processed and analyzed aptly, it can prove to be immensely useful and help businesses in cutting down their expenses, increase profits, and serve their customers better. This is where the Big Data Hadoop comes into play!
Big Data can be defined as a set of large quantities of complex data, which cannot be processed by using traditional computers to recognize patterns and trends in them. Hadoop is a Java (and not OLAP, Online Analytical Processing) based, an open-source program built for storing and processing large quantities of Big Data in an efficient and cost-effective manner.
How Big Data Hadoop Works?
The Big Data Hadoop program can run applications on systems with thousands of hardware nodes and has a capacity to process thousands of terabytes of data. It is immensely powerful as it runs on a distributed file system that facilitates very high-speed data transfer from one node to the other and allows it to carry on processing even in case of a node failure, thereby decreasing the risk of losing data unexpectedly owing to any system malfunction. Since its initial release in 2012, Big Data Hadoop has constantly been updated and improved upon.
Key Elements of Big Data Hadoop
Big Data Hadoop is composed of a number of functional modules. They are:
- Hadoop Distribution File System (HDFS): The HDFS was inspired by Google’s paper on GFS. To achieve high bandwidth between the nodes, HDFS breaks files into blocks and stores them on numerous commodity servers.
- Hadoop Common: Hadoop Common provides the framework for other modules. This module contains Java libraries and stores utilities needed by other modules to function.
- Hadoop YARN: Yet Another Resource Negotiator (YARN) manages computer resources (clusters) and scheduling for user applications.
- Hadoop MapReduce: This module enables Hadoop to do the parallel computation to tackle large quantities of data by mapping all the data and reducing them to results.
On the whole, if you see, Big Data Hadoop is not only capable of processing terabytes of data in minutes, but also effectively minimizes risks of any data loss. The added advantage that one gets with Big Data Hadoop is the cost affordability. All these positives make Big Data Hadoop stand as one of the most powerful data processing software in the market today. You can visit RemoteDBA.com to get more information on Hadoop application.
