Machine Learning algorithms are part of Artificial Intelligence (AI) that imitates the human learning process, which can be used for decision-making and problem-solving. ML algorithms are systems of problem-solving techniques that exhibit human-like learning capability. While humans learn through practice and experience, machines learn through data. ML algorithms have applications across various industries and different functional areas. The primary objective of ML is to assist in decision-making. Today, ML is used for driving innovation and as a competitive strategy by several organizations.
Supervised Learning Algorithms with Applications in Predictive Analytics:
Simple linear regression: coefficient of determination, significance tests, residual analysis, confidence and prediction intervals.
Supervised Learning Algorithms with Applications in Classification Problems:
Logistic and Multinomial Regression: Logistic function, estimation of probability using logistic regression, Deviance, Wald test, Hosmer Lemeshow test. Feature selection in logistic regression.
Managers and decision makers with roles in analytics and AI-based consulting in marketing, operations, supply chain management, finance, insurance, and general management in various industries should attend the course. The course is suitable for those who are already working on ML to enhance their knowledge and for those with analytical aptitude and would like to start a new career in Analytics.
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