Course Outline
Introduction
- Applications of predictive analytics in finance, healthcare, pharmaceuticals, automotive, aerospace, and manufacturing
Overview of Big Data concepts
Collecting data from diverse sources
Understanding data-driven predictive models
Overview of statistical and machine learning techniques
Case study: predictive maintenance and resource planning
Applying algorithms to large datasets using Hadoop and Spark
Predictive Analytics Workflow
Accessing and exploring data
Preprocessing data
Developing a predictive model
Training, testing, and validating datasets
Applying various machine learning approaches (time-series regression, linear regression, etc.)
Integrating the model into existing web applications, mobile devices, embedded systems, and more
Integrating Matlab and Simulink with embedded systems and enterprise IT workflows
Generating portable C and C++ code from MATLAB code
Deploying predictive applications to large-scale production systems, clusters, and cloud environments
Taking action based on analysis results
Next steps: Automatically responding to findings using Prescriptive Analytics
Closing remarks
Requirements
- Experience working with Matlab
- No prior experience in data science is required
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.
