Machine Learning in Practice

Features, models, evaluation and responsible deployment with scikit-learn.

0.0 (0) 0 learners Intermediate English 20–25 hours Analysts / developers Dr. Sara Al-Mansouri

What you'll learn

Prepare features and pipelines
Train and compare models
Choose the right evaluation metric
Deploy and monitor models responsibly
Learn with the Smart Instructor

This course has its own AI knowledge space. The Smart Instructor understands the syllabus, lessons and trainer materials, answers from them with citations, generates practice and adapts difficulty.

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Curriculum

3 sections · 6 lessons · 3.5 hours · 1 assessments

Features and Pipelines Preview 6 min
Training and Comparing Models 6 min

Choosing Evaluation Metrics 6 min
Fairness and Model Cards 6 min
Deployment and Monitoring 6 min

Final Project: Build & Submit AI Lab 180 min

Description

A practical ML course: from a CSV to a trustworthy model, with the evaluation and monitoring habits employers expect.

Prerequisites: Python basics and Data Analysis with Python.

Skills you will build
Machine LearningPython ProgrammingStatistics

Final project (AI Lab)

Train and evaluate a learner drop-out prediction model, compare two algorithms, and write a one-page model card covering data, metrics, fairness checks and limitations.

Built in the AI Lab with your Smart Instructor, Coding, Debugging, Tutor and Assessment experts — evaluated against a rubric and reviewed by your trainer.

Your trainer

S
Dr. Sara Al-Mansouri
Artificial Intelligence
AI Researcher & Educator · Smart AI Academy lead

Sara leads applied AI programs and has trained thousands of engineers in machine learning, generative AI, RAG and multi-expert systems.

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$44.99
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  • 3.5 hours of content
  • Smart Instructor with cited answers
  • AI-generated practice & adaptive remediation
  • Verifiable certificate of completion
Part of learning paths
Data Analyst