Third Semester
Machine Learning & Predictive Systems
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Number of Sections :
0
Duration :
0 Minutes
Training Level :
Professional
Price
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Training Path Description
Build machine learning models and predictive systems with real data, evaluating and interpreting performance practically.
Learn how to turn data into predictive decisions using machine learning and predictive systems. The course covers problem framing, data preparation, building forecasting models, and evaluating/improving them. You’ll explore key concepts like regression and classification, feature selection, validation techniques, and principles for interpreting results for business and industrial applications. By the end, you’ll be able to design a scalable predictive system and confidently communicate its outcomes.
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What Will You Gain?
Skills related to the course
- - Data preparation skills for training/validation (cleaning, transformations, splitting)
- - Using ML models for prediction and evaluating them
- - Feature selection and building effective pipelines
- - Interpreting performance metrics and diagnosing errors
- - Designing a deployable, improvable predictive system workflow
Training Path Objectives
- - Understand predictive problem types (regression/classification) and how to frame them
- - Prepare and clean data to make it model-ready
- - Build predictive models and evaluate accuracy using sound validation
- - Improve performance via hyperparameter tuning and feature selection
- - Interpret and communicate results to support decision-making
- Practical approach linking machine learning to real predictive system needs
- Step-by-step data prep, evaluation, and model improvement
- Emphasis on interpreting results and key performance metrics