Course Outline
Introduction
- Predictive analytics in finance, healthcare, pharmaceuticals, automotive, aerospace, and manufacturing for government applications
Overview of Big Data concepts for government operations
Capturing data from disparate sources for government use
What are data-driven predictive models for government decision-making?
Overview of statistical and machine learning techniques for government analysis
Case study: Predictive maintenance and resource planning in public sector operations
Applying algorithms to large data sets with Hadoop and Spark for government systems
Predictive Analytics Workflow for government processes
Accessing and exploring data for government use
Preprocessing the data for government applications
Developing a predictive model for government needs
Training, testing, and validating a data set for government accuracy
Applying different machine learning approaches (time-series regression, linear regression, etc.) for government analysis
Integrating the model into existing web applications, mobile devices, embedded systems, etc., for government operations
Matlab and Simulink integration with embedded systems and enterprise IT workflows for government systems
Creating portable C and C++ code from MATLAB code for government use
Deploying predictive applications to large-scale production systems, clusters, and clouds for government infrastructure
Acting on the results of your analysis for government decision-making
Next steps: Automatically responding to findings using Prescriptive Analytics for government operations
Closing remarks for government stakeholders
Requirements
- Experience with MATLAB for government applications
- No prior experience with data science is necessary
Testimonials (2)
basics and loved the prepared documents and exercises
Rekha Nallam - GE Medical Systems Polska Sp. z o.o.
Course - Introduction to Predictive AI
The many examples and the building of the code from start to finish.
