Course Outline
Introduction
- Predictive analytics applications in finance, healthcare, pharmaceuticals, automotive, aerospace, and manufacturing sectors
Understanding Big Data concepts
Data capture from various sources
Defining data-driven predictive models
Survey of statistical and machine learning techniques
Case study: predictive maintenance and resource planning
Implementing algorithms on large datasets using Hadoop and Spark
Predictive Analytics Workflow
Data access and exploration
Data preprocessing
Predictive model development
Training, testing, and validating datasets
Utilizing various machine learning approaches (such as time-series regression, linear regression, etc.)
Integrating models into existing web applications, mobile devices, embedded systems, and more
Integration of Matlab and Simulink with embedded systems and enterprise IT workflows
Generating portable C and C++ code from MATLAB
Deploying predictive applications to large-scale production systems, clusters, and cloud environments
Implementing actions based on analysis results
Future steps: Automated responses to findings using Prescriptive Analytics
Conclusion
Requirements
- Proficiency in using Matlab
- No prior experience in 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.
