What is Artificial Intelligence?
Artificial Intelligence a.k.a. A.I. is the science and engineering of making intelligent computer programs or machines. Artificial intelligence algorithms span several different branches of computer science and mathematics including: pattern recognition, predictive modeling, text mining and search, genetic programming, heuristics, inference, and ontology, and data analytics.
Artificial intelligence is commonly referred to as machine learning and was originally developed to enable computers to learn. Today, the technology is based on a number of advanced mathematical methods for optimization, regression and classification and finds application in a wide variety of fields including: gaming, speech recognition, computer vision, expert systems, heuristic classification, medical diagnostics, and credit card fraud.
What is Predictive Modelling?
Predictive modeling is the area of data analytics concerned with forecasting probabilities and trends. A predictive model is made up of a number of predictors, or variables, that are likely to influence future behavior or results. In marketing, for example, a customer's gender, age, and purchase history might predict the likelihood of a future sale.
Predictive modeling techniques are often iterative involving the collection of data, the formulation of a statistical model, and the approximation of an outcome. The process is refined and validated as more data becomes available. The model may employ a simple linear equation or a complex artificial intelligence algorithm, mapped out by sophisticated software.
Predictive modeling algorithms are used widely in information technology (IT). Applications of predictive modeling include: spam filtering, customer relationship management (CRM), capacity planning, disaster recovery, engineering, meteorology, insurance risk, credit score, and marketing.
What is Data Analytics?
Data analytics is the science or process of inspecting, cleaning, transforming, and modeling data with the goal of highlighting useful information. Data analytics software is used in many industries to allow companies and organization to make better business decisions and in the sciences to verify or disprove existing models or theories.
Data analytics is distinguished from data mining by the scope, purpose and focus of the analysis. Data miners sort through huge data sets using sophisticated machine learning algorithms to identify undiscovered patterns and establish hidden relationships. Data analytics focuses on inference, the process of deriving a conclusion based solely on what is already known by the researcher.
What is Gradient Boosting?
Gradient boosting is a machine learning technique for regression problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. It builds the model in a stage-wise fashion like other boosting methods do, and it generalizes them by allowing optimization of an arbitrary differentiable loss function. Gradient boosting method can be also used for classification problems by reducing them to regression with a suitable loss function.
Related Reading: What is Neural Networks?
Related Reading: How AI is used to make Intelligent Homes?
Artificial Intelligence a.k.a. A.I. is the science and engineering of making intelligent computer programs or machines. Artificial intelligence algorithms span several different branches of computer science and mathematics including: pattern recognition, predictive modeling, text mining and search, genetic programming, heuristics, inference, and ontology, and data analytics.
Artificial intelligence is commonly referred to as machine learning and was originally developed to enable computers to learn. Today, the technology is based on a number of advanced mathematical methods for optimization, regression and classification and finds application in a wide variety of fields including: gaming, speech recognition, computer vision, expert systems, heuristic classification, medical diagnostics, and credit card fraud.
What is Predictive Modelling?
Predictive modeling is the area of data analytics concerned with forecasting probabilities and trends. A predictive model is made up of a number of predictors, or variables, that are likely to influence future behavior or results. In marketing, for example, a customer's gender, age, and purchase history might predict the likelihood of a future sale.
Predictive modeling techniques are often iterative involving the collection of data, the formulation of a statistical model, and the approximation of an outcome. The process is refined and validated as more data becomes available. The model may employ a simple linear equation or a complex artificial intelligence algorithm, mapped out by sophisticated software.
Predictive modeling algorithms are used widely in information technology (IT). Applications of predictive modeling include: spam filtering, customer relationship management (CRM), capacity planning, disaster recovery, engineering, meteorology, insurance risk, credit score, and marketing.
What is Data Analytics?
Data analytics is the science or process of inspecting, cleaning, transforming, and modeling data with the goal of highlighting useful information. Data analytics software is used in many industries to allow companies and organization to make better business decisions and in the sciences to verify or disprove existing models or theories.
Data analytics is distinguished from data mining by the scope, purpose and focus of the analysis. Data miners sort through huge data sets using sophisticated machine learning algorithms to identify undiscovered patterns and establish hidden relationships. Data analytics focuses on inference, the process of deriving a conclusion based solely on what is already known by the researcher.
What is Gradient Boosting?
Gradient boosting is a machine learning technique for regression problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. It builds the model in a stage-wise fashion like other boosting methods do, and it generalizes them by allowing optimization of an arbitrary differentiable loss function. Gradient boosting method can be also used for classification problems by reducing them to regression with a suitable loss function.
Related Reading: What is Neural Networks?
Related Reading: How AI is used to make Intelligent Homes?
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