Course Outline
Introduction
- Predictive analytics in finance, healthcare, pharmaceuticals, automotive, aerospace, and manufacturing
Overview of Big Data concepts
Capturing data from disparate sources
What are data-driven predictive models?
Overview of statistical and machine learning techniques
Case study: predictive maintenance and resource planning
Applying algorithms to large data sets with Hadoop and Spark
Predictive Analytics Workflow
Accessing and exploring data
Preprocessing the data
Developing a predictive model
Training, testing and validating a data set
Applying different machine learning approaches (time-series regression, linear regression, etc.)
Integrating the model into existing web applications, mobile devices, embedded systems, etc.
Matlab and Simulink integration with embedded systems and enterprise IT workflows
Creating portable C and C++ code from MATLAB code
Deploying predictive applications to large-scale production systems, clusters, and clouds
Acting on the results of your analysis
Next steps: Automatically responding to findings using Prescriptive Analytics
Closing remarks
Requirements
- Experience with Matlab
- No previous experience with data science is required
Testimonials (5)
Difficult topics presented in simple, user-friendly way
Marcin - GE Medical Systems Polska Sp. z o.o.
Course - Introduction to Predictive AI
Guillaume's availability, attentiveness, and interpersonal skills. He was able to adapt to our quite specific needs.
Ugo Imbert - Michelin
Course - Basic MATLAB Programming
Machine Translated
Gradual introduction and application of methods
Aurelien Briffaz - CIRAD
Course - Introduction au Machine Learning avec MATLAB
Machine Translated
the matter was well presented and in an orderly manner.
Marylin Houle - Ivanhoe Cambridge
Course - Introduction to R with Time Series Analysis
Richard's training style kept it interesting, the real world examples used helped to drive the concepts home.