Course Outline
Introduction
- Predictive analytics applications 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
Implementing algorithms on large datasets using Hadoop and Spark
Predictive Analytics Workflow
Accessing and exploring data
Preprocessing the data
Developing a predictive model
Training, testing, and validating a dataset
Applying various machine learning methods (e.g., time-series regression, linear regression)
Integrating models into existing web applications, mobile devices, embedded systems, etc.
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
Acting on the results of your analysis
Next steps: Automatically responding to findings using Prescriptive Analytics
Closing remarks
Requirements
- Experience with Matlab
- No prior background 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.
