AI & Machine Learning Course Outline for Human Resources Management
Artificial Intelligence and Machine Learning has revolutionized the world of work and no profession has been spared such that either you learn to leverage AI to do your work or it will replace you. The HR management is no exception. This AI and Machine learning course is offered to equip HR professionals to be up to date with the ever changing digital landscape and improve their productivity . .
Course summary
Course Title: AI & Machine Learning for HR Professionals
Target audience: HR business partners, talent acquisition specialists, people analytics teams, HR managers, rewards & workforce planning professionals
Prerequisites: basic HR domain knowledge and Excel competency; recommended: basic statistics literacy. No prior programming required for an introductory track; programming introduced progressively on intermediate/advanced tracks.
Course outcomes: Design and evaluate data-driven HR solutions, build simple predictive models (attrition, performance), apply NLP to CVs and JD text, automate routine HR tasks, create people-analytics dashboards, and apply fairness, privacy, and legal controls.
Study Options: 4 week physical training or 2 week physical training and 2 week online training
Course outline
1.Orientation, data literacy & HR use cases
– Objectives: map AI opportunities across HR (TA, L&D, retention, rewards), data sources (ATS, LMS, payroll, engagement).
– Lab/Demo: data inventory and a simple KPI dashboard in Excel/Power BI.
2. Data foundations for HR analytics
– Data types (transactional, survey, unstructured text), data quality, identity resolution, privacy basics.
– Lab: clean and join ATS + payroll + performance datasets (Excel/pandas).
3. Exploratory analysis & HR reporting
– Descriptive stats, cohort and survival analysis, segmentation, visualization best practices.
– Lab: build turnover cohorts and visualizations; compute retention rates.
4. Statistics & experimentation for people decisions
– Sampling, hypothesis testing, A/B testing basics, measuring lift and significance in interventions.
– Lab: design and analyze an engagement survey experiment.
5. Predictive modeling: attrition & performance
– Features for people models, handling time-to-event (survival) vs classification, model selection and validation.
– Lab: build and evaluate an attrition prediction model (logistic regression / random forest).
6. NLP for HR: resumes, job descriptions, chats and surveys
– Text preprocessing, resume parsing, skill extraction, job-skill matching, job description bias detection.
– Lab: extract skills from resumes, match candidates to roles, detect gendered language in JDs.
7. Talent acquisition automation & candidate scoring
– Applicant screening pipelines, automated interview scheduling, chatbots, fairness considerations in ranking.
– Lab: build a rule-based candidate screener and prototype chatbot flow (no-code or simple Python).
8. Learning & career pathway analytics
– Skill gap analysis, recommendation systems for learning, career-path modeling.
– Lab: create a skill map and recommend L&D actions based on role profiles.
9. Compensation, workforce planning & optimization
– Predictive staffing, demand-supply modeling, pay equity analysis and simulation.
– Lab: run pay-equity regression and scenario workforce planning.
10. Anomaly detection & fraud prevention in payroll/expense
– Unsupervised techniques for outliers, policy exceptions, and suspicious patterns.
– Lab: detect anomalous payroll/expense items using isolation forest / z-scores.
11. Explainability, fairness, legal & ethical frameworks
– Bias sources in HR data, EEOC/anti-discrimination rules, GDPR/CCPA considerations, algorithmic fairness metrics, mitigation strategies.
– Lab: audit a hiring model for disparate impact and document mitigation actions.
12. Deployment, monitoring, governance & change management
– Operationalization, MLOps basics, monitoring (data drift), user adoption, stakeholder communication, documentation and audit trails.
– Capstone: end-to-end project presentation with governance plan.
Hands-on labs and tools
– No-code/low-code: Power BI/Tableau, H2O Driverless AI (demo), Azure Cognitive Services, UiPath for simple automation.
– Coding (optional tracks): Python with pandas, scikit-learn, statsmodels, transformers (Hugging Face), spaCy, SHAP.
– Databases & querying: SQL for HR datasets.
– NLP/OCR: spaCy, Hugging Face, Tesseract or cloud OCR (AWS Textract / Azure Form Recognizer).
– MLOps/Deployment: Docker basics, simple REST API deployment, basic monitoring with logging.
Assessment and deliverables
– Weekly labs and short quizzes.
– Two mini-projects (example: resume-skill extractor; attrition model + business case).
– Capstone project: end-to-end solution (data ingestion, model, dashboard, business case, governance & fairness audit).
– Deliverables include reproducible notebooks or low-code workflows, a slide deck for stakeholders, and a one-page governance checklist.
Sample practical projects / case studies
– Candidate matching pipeline: parse CVs, map skills to JD, rank candidates, and flag top matches for recruiter review.
– Attrition early-warning system: predict resignations and recommend targeted retention interventions with estimated ROI.
– Job description analyzer: detect biased language, suggest neutral alternatives, and measure impact on candidate diversity.
– Pay equity audit: regression-based analysis controlling for role/experience, detect pay gaps and propose remediation.
– Learning recommender: suggest courses based on skill gaps and career aspirations.
– Chatbot for HR inquiries: prototype an FAQ bot that classifies and routes queries, logs interactions for analytics.
Ethics, privacy & legal considerations (emphasize throughout)
– Data minimization, consent, purpose limitation, retention policies.
– Avoid using protected attributes directly; consider proxies and the risk of indirect discrimination.
– Regulatory awareness: EEOC, GDPR, CCPA, local labor laws — involve legal and compliance early.
– Document model decisions, maintain audit trails, and keep humans in the loop for high-stakes decisions.
Evaluation of model fairness and interpretability
– Fairness metrics: demographic parity, equal opportunity, disparate impact ratio.
– Interpretability tools: SHAP, LIME, feature importance, counterfactual explanations.
– Bias mitigation practices: re-sampling, re-weighting, constrained optimization, post-processing.
Recommended datasets and anonymization
– Synthetic or anonymized ATS and payroll records for labs.
– Public datasets (for practice): HR analytics datasets (IBM HR Attrition), anonymized salary surveys, public resume/LinkedIn extracts (ensure licensing).
– Teach de-identification techniques and synthetic data generation for safe practice.
Suggested readings & resources
– People Analytics books and resources (e.g., Ben Waber, Emma Weber).
– Fairness toolkits: IBM AI Fairness 360, Google What-If Tool, Microsoft’s Fairlearn.
– Practical ML resources: Python for Data Analysis (Wes McKinney), Hands-On Machine Learning (Aurélien Géron).
– Legal guides: employer data privacy compliance checklists, EEOC technical guidance.
Instructional design tips for HR organizations
– Start with high-impact, low-risk pilots (reporting automation, resume parsing, scheduling bots).
– Involve cross-functional teams: HR, legal/compliance, IT, and people-analytics.
– Prioritize explainability and human review for all hiring and promotion models.
– Measure business KPIs (time-to-fill, quality-of-hire, turnover reduction) to justify scaling.
– Provide change management and training for HR staff to interpret model outputs and manage exceptions.
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If you’d like, I can:
– Convert this into a detailed week-by-week 8–12 week syllabus with estimated hours, slide topics, and lab instructions.
– Produce a capstone project brief with datasets and a marking rubric tailored to recruiting or retention focus.
– Create a 1–2 day hands-on workshop agenda for HR leaders (non-technical).