55375AC: Fundamentals of Machine Learning

Course Description

SkillDynamo’s 55375AC: Fundamentals of Machine Learning course develops practical, role-relevant capability in understanding the machine-learning lifecycle from problem framing and data preparation through modeling, evaluation, responsible deployment, monitoring, and governance. The learning path progresses from essential concepts and planning to implementation, validation, troubleshooting, and operational use.

Learners work through realistic scenarios, instructor-led demonstrations, applied exercises, and evidence-based reviews. Each topic is connected to workplace responsibilities, common risks, quality controls, secure working practices, and measurable outcomes.

The content is independently written for SkillDynamo using the supplied source page as the title, update-date, and topic-alignment reference. Learners finish with an applied scenario, documented validation evidence, and a prioritized improvement plan.

What You Will Learn

• Explain and apply AI and machine-learning terminology within 55375AC: Fundamentals of Machine Learning.
• Configure, validate, and troubleshoot supervised, unsupervised, and reinforcement learning, problem framing and success criteria, and fairness, transparency, privacy, and accountability using documented quality and governance controls.
• Explain and apply data collection, quality, and cleaning within 55375AC: Fundamentals of Machine Learning.
• Configure, validate, and troubleshoot feature selection and engineering, training, validation, and test sets, and overfitting, leakage, and reproducibility using documented quality and governance controls.
• Explain and apply regression and prediction within 55375AC: Fundamentals of Machine Learning.
• Configure, validate, and troubleshoot binary and multiclass classification, clustering and segmentation, and algorithm selection and practical trade-offs using documented quality and governance controls.
• Explain and apply metrics and model evaluation within 55375AC: Fundamentals of Machine Learning.
• Configure, validate, and troubleshoot deployment and inference concepts, drift, monitoring, and retraining, and documentation, controls, and improvement planning using documented quality and governance controls.

SkillDynamo Training Approach

SkillDynamo delivers this course through instructor-led demonstrations, hands-on exercises, scenario analysis, validation checklists, structured troubleshooting, solution reviews, and a final improvement plan. Learners repeatedly practice planning, implementation, evidence collection, testing, correction, documentation, and communication so the skills transfer confidently into real workplace situations.