Study big data and MACHINE LEARNING

R-ALGO Engineering Big Data provides articles on Artificial Intelligence, Algorithms, Big Data, Data Science, and Machine Learning. Also, R-ALGO Engineering Big Data provides R Tutorials on how to implement Machine Learning Algorithms with provided datasets.

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Big Data Topics

R-ALGO Engineering Big Data

Algorithms are tools used to solve problems inserted into machines and computers. Basically, algorithms are unambiguous pathways to answer a question. These functions use formal logic and break questions down into a series of basic steps. They are at the heart of computer processing and can be expanded to cover millions of data points. When found in machine learning and artificial intelligence, they are tools used to produce output from a set of input. In an artificial neural network, algorithms are attached to nodes. The algorithms often do not change as the network learns and grows. Instead, the algorithm is weighted differently as time goes on and the machine learns.

Learn about artificial intelligence at R-Algo Engineering Big Data

Artificial intelligence, also known as AI, is a step above basic computer technology. Traditional computers simply process the data inputted into them to form outputs. They are limited by the information implanted by the operator. Artificial intelligence works beyond the operator. It uses neural networks, programs, and algorithms to act independently of human beings. Artificial intelligence can produce work from either supervised or unsupervised learning programs. The artificial intelligence program is able to produce test runs thousands of times faster than operators can upload information and tweak variables. It is an incredibly efficient way to perfect the machine learning process. AI makes the impossible, possibly for machines to learn from humans by experience. Machines are able to adjust to new inputs and the output are tasks similar to a human output.

Learn data science and machine learning with R tutorials at R-Algo Engineering Big Data Blog

Big data is a broad term that can mean a large variety of datasets. These large volumes of datasets can be structured and unstructured. At its heart, big data refers to amounts of data that cannot easily be processed by human beings. In part of analyzing the data, patterns must be found to create a connection across the data using machine learning algorithms or data visualization. Big data may involve quantitative or qualitative data of all kinds. Qualitative data often must be converted into quantitative data by the process of coding. Big data differs from traditional data in its effectiveness over a large scale. With typical data interpreted by humans, more data means slower analysis. Numbers can only be crunched over a long period of time. Big data requires more and more data to make accurate predictions. There are more detailed means and clearer regression patterns. It can expand far beyond the possibilities of people or simple computer calculations.

Learn data science and data mining with R tutorials at R-Algo Engineering Big Data

Data mining is the use of computer programs to analyze large data sets. Big data and data mining go hand in hand. These programs use algorithms to make sense of thousands or millions of entries. They may work with both quantitative and qualitative data. Data mining utilizes the information of big data and becomes more productive as data totals increase over time. The process identifies patterns, outliers, and statistical attributes to the data set. This information may also be relevant for predictions into the future. More data means a better prediction of how a big data spread will develop in any number of fields. All of this work can be uncovered using a basic data mining program. Data mining will help the process of connecting patterns and correlations within data to predict an outcome within a given algorithm.

Learn data science and machine learning with R tutorials at R-Algo Engineering Big Data

Data science is the study of the structure and relationships of data. Data science also helps solve complex problems with the blend of machine learning algorithms and technology. The field can be either abstract or practical. However, practical data science is often applicable through data mining and artificial intelligence. It involves looking at different relationships and detecting patterns throughout a massive array of data. The studies of data science bring in information from a number of different fields. Statistics plays a key role. It helps to identify percentages and guide predictions in the development of data. Formal logic and computer science also help guide data science. Computers help to organize and analyze data with the support of formal logic. The procedures of data science make sense of data in a disparate number of situations.

Learn data science and machine learning with R tutorials at R-Algo Engineering Big Data

Machine learning involves learning procedures by computers outside of the actions of operators. It is the process by which artificial intelligence gains more information over time. Computers are allowed to respond to changing circumstances and learn over time. This learning may be in the form of artificial neural networks where nodes are weighted differently over time. Algorithms are the basis by which these machines learn and process data. Learning occurs on several scales as the computers involve shift their focus to process available information. Machine learning algorithms are based on pattern recognition methods and how computers can perform tasks without being programmed. Machine learning will revolutionize what we can do and what we thought was impossible, will become possible.

R Tutorials can help build your data science and machine learning skills by utilizing machine learning algorithms.

Some of the basic to advanced algorithms we cover are:

  • Apriori
  • Artificial Neural Networks
  • Decision Trees
  • K Means Clustering
  • K-nearest Neighbors (KNN)
  • Linear Regression
  • Logistic Regression
  • Naive Bayes Classifier
  • Random Forests

United States Shark Attack Data Analysis with R

In this R Tutorial, we will complete data analysis of confirmed unprovoked United States shark attacks from 1837 until July 26, 2018.  The shark attack data will be analyzed based on total occurrences in each state based in the U.S. and will graphically be displayed. Also, below are the libraries that

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Halloween Costume Names Text Analysis with Wordcloud in R

In this R tutorial, we will determine the most common Halloween costumes names by using a list of over 5000 Halloween costumes submitted by people.  The costumes will be classified by text analysis and visualize the common names using Wordcloud. Cleaning the Halloween Costume Names Below are sample outputs that are used

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Florida Shark Attack Data Visualization with maps and ggplot in R

In this R Tutorial, we will complete data analysis and data visualization with ggplot, maps and mapdata of Florida shark attacks from 1882 until July 28, 2018.  The shark attack data will be analyzed based on total occurrences in the state of Florida and will graphically be displayed using maps

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The Power of Supercomputers in Big Data Technology

Individuals often think of supercomputers when they consider big data and algorithms. They contemplate the largest and most powerful computers in the world processing billions of numbers and then creating a valuable output that a company then uses. While this illustration is close to the ideal of the big data-supercomputer

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Medical Care Expense Analysis and Linear Regression in R

In this R tutorial, we will be analyzing and visualizing medical care expenses. For this tutorial, we will be using the lm() package to fit a linear regressional model to data in R. Health insurance companies must make money to stay afloat. In order to do so, it must collect more in yearly

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White Wine Quality Analysis with Regression and Model Trees in R

In this R tutorial, we will be estimating the quality of wines with regression trees and model trees. Machine learning has been used to discover key differences in the chemical composition of wines from different regions or to identify the chemical factors that lead a wine to taste sweeter. In

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