In Education2.0, the next generation learning model gives aspirants a wing, implying the corporate world by keeping economic and financial problems with an empirical dimension. In contrast, today, machine learning methods may offer something of value. The courses designed covers the intersection of empirical methods in economics and machine learning, including regression analysis, natural language processing, and dimensionality reduction. This includes coverage of various discriminative deep learning models (DNNs, CNNs, LSTMs, and DQNs), generative machine learning models (GANs and VAEs), and tree-based models.

Courses designed for studies of organizations learning and experiencing management-related information system challenges have been updated regularly, and several new techniques have been added. These reality-based courses are designed to stimulate discussion among students and enable them to apply concepts in the LMS+API+LTI*; to real-life scenarios. The course's companion features lecture slides, a test bank, and other materials to enhance students' understanding.

*Learning Management System; application programming interface, learning tool interoperability.
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Training Courses We Offer

Introduction to Economics

Introduction to Economics

Economics Finance Statistics Investment

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Defining Managerial Economics

Defining Managerial Economics

Managerial Economics Capital & Investment

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Business Economics & Market Economy

Business Economics & Market Economy

Managerial Economics Capital & Investment

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Basic of Machine Learning

Introduction Machine Learning

ML Challenges & Application

Clustering In ML

Clustering In ML

Clustering Anomaly Detection Data requirements & pre-processing steps

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Learning Management System [LMS]

Application Programming Interface [API]

Learning Tool Interoperability [LTI]

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