Machine Learning Crash Course Curriculum

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Curriculum

Machine Learning Crash Course

Structured, hands-on learning path for Machine Learning Crash Course with detailed weekly outcomes and practical delivery.

Duration: 14 Weeks
Level: Intermediate
Study Time: 2 hours/week + labs
School: Hexadigitall Academy
14 WeeksIntermediateProject-Based

Welcome to Machine Learning Crash Course! 🎓

This curriculum for Machine Learning Crash Course follows a Bloom-aligned progression from high-impact fundamentals to delivery-ready execution, with weekly evidence, labs, and portfolio outputs matched to intermediate expectations.

Each week advances from comprehension and application toward evaluation and creation, ensuring progressive learning and capstone readiness.

Your success is our priority. By the end, you will produce portfolio-ready artifacts and confidently explain your technical decisions. You will graduate with a professionally curated portfolio that demonstrates scope, depth, and delivery quality. You will graduate with a professionally curated portfolio that demonstrates scope, depth, and delivery quality. You will graduate with a professionally curated portfolio that demonstrates scope, depth, and delivery quality. You will graduate with a professionally curated portfolio that demonstrates scope, depth, and delivery quality.

Prerequisites

  • Python programming proficiency: libraries (NumPy, Pandas, scikit-learn), data structures, and API usage
  • Statistics and probability fundamentals: distributions, hypothesis testing, and experimental design
  • Machine learning basics: supervised learning, hyperparameter tuning, and model evaluation metrics
  • Hands-on experience with notebooks (Jupyter), experiment tracking, and model versioning systems

Essential Resources

  • Model development workflow guides, hyperparameter tuning references, and experiment tracking templates
  • Feature engineering playbooks, model evaluation metrics library, and production deployment checklists
  • Research paper repository, implementation examples, and performance benchmarking tools

Complementary Courses

LLMs & Generative AI

Master fine-tuning, prompt engineering, and RAG architecture patterns

MLOps & Model Deployment

Learn model serving, A/B testing, and continuous model improvement workflows

Production AI Systems

Deepen model monitoring, drift detection, and operational governance

Learning Roadmap

  • Early Weeks: ML fundamentals, data preparation, and baseline models
  • Middle Weeks: Advanced model techniques, experimentation, and tuning
  • Late Weeks: Production deployment, monitoring, and continuous improvement

Detailed Weekly Curriculum

Each week includes outcomes and practical lab work aligned to the curriculum structure.

Week 1

Machine Learning Crash Course: ML Problem Framing and Baselines (Sprint 1)

2 hours + labs
Learning Outcomes
  • Identify the principles of Machine Learning Crash Course: ML Problem Framing and Baselines (Sprint 1) and link them to course outcomes in time-boxed sprints with rapid feedback loops.
  • Explain Machine Learning Crash Course: ML Problem Framing and Baselines (Sprint 1) in a guided scenario using realistic tools, constraints, and quality gates.
  • Apply trade-offs, risks, and decision points for Machine Learning Crash Course: ML Problem Framing and Baselines (Sprint 1), then record rationale for stakeholder review.
  • Document a portfolio-ready model evaluation brief for Machine Learning Crash Course: ML Problem Framing and Baselines (Sprint 1) with measurable success criteria and next actions.
Lab Exercise
  • Build a working Machine Learning Crash Course: ML Problem Framing and Baselines (Sprint 1) pipeline from dataset preparation through evaluation and reproducibility checks.
  • Measure Machine Learning Crash Course: ML Problem Framing and Baselines (Sprint 1) quality using task-appropriate metrics and perform controlled hyperparameter tuning.
  • Package Machine Learning Crash Course: ML Problem Framing and Baselines (Sprint 1) for serving or integration with monitoring hooks and rollback strategy.
Week 2

Machine Learning Crash Course: Feature Engineering and Data Pipelines (Sprint 1)

2 hours + labs
Learning Outcomes
  • Identify the principles of Machine Learning Crash Course: Feature Engineering and Data Pipelines (Sprint 1) and link them to course outcomes in time-boxed sprints with rapid feedback loops.
  • Explain Machine Learning Crash Course: Feature Engineering and Data Pipelines (Sprint 1) in a guided scenario using realistic tools, constraints, and quality gates.
  • Apply trade-offs, risks, and decision points for Machine Learning Crash Course: Feature Engineering and Data Pipelines (Sprint 1), then record rationale for stakeholder review.
  • Document a portfolio-ready model evaluation brief for Machine Learning Crash Course: Feature Engineering and Data Pipelines (Sprint 1) with measurable success criteria and next actions.
Lab Exercise
  • Build a release workflow for Machine Learning Crash Course: Feature Engineering and Data Pipelines (Sprint 1) with automated checks, approvals, and artifact traceability.
  • Implement quality and security gates for Machine Learning Crash Course: Feature Engineering and Data Pipelines (Sprint 1) and enforce fail-fast criteria.
  • Execute a staged promotion for Machine Learning Crash Course: Feature Engineering and Data Pipelines (Sprint 1) and validate rollback safety under a controlled failure.
Week 3

Machine Learning Crash Course: Model Training and Evaluation (Sprint 1)

2 hours + labs
Learning Outcomes
  • Identify the principles of Machine Learning Crash Course: Model Training and Evaluation (Sprint 1) and link them to course outcomes in time-boxed sprints with rapid feedback loops.
  • Explain Machine Learning Crash Course: Model Training and Evaluation (Sprint 1) in a guided scenario using realistic tools, constraints, and quality gates.
  • Apply trade-offs, risks, and decision points for Machine Learning Crash Course: Model Training and Evaluation (Sprint 1), then record rationale for stakeholder review.
  • Document a portfolio-ready model evaluation brief for Machine Learning Crash Course: Model Training and Evaluation (Sprint 1) with measurable success criteria and next actions.
Lab Exercise
  • Build a working Machine Learning Crash Course: Model Training and Evaluation (Sprint 1) pipeline from dataset preparation through evaluation and reproducibility checks.
  • Measure Machine Learning Crash Course: Model Training and Evaluation (Sprint 1) quality using task-appropriate metrics and perform controlled hyperparameter tuning.
  • Package Machine Learning Crash Course: Model Training and Evaluation (Sprint 1) for serving or integration with monitoring hooks and rollback strategy.
Week 4

Machine Learning Crash Course: Experiment Tracking and Reproducibility (Sprint 1)

2 hours + labs
Learning Outcomes
  • Identify the principles of Machine Learning Crash Course: Experiment Tracking and Reproducibility (Sprint 1) and link them to course outcomes in time-boxed sprints with rapid feedback loops.
  • Explain Machine Learning Crash Course: Experiment Tracking and Reproducibility (Sprint 1) in a guided scenario using realistic tools, constraints, and quality gates.
  • Apply trade-offs, risks, and decision points for Machine Learning Crash Course: Experiment Tracking and Reproducibility (Sprint 1), then record rationale for stakeholder review.
  • Document a portfolio-ready model evaluation brief for Machine Learning Crash Course: Experiment Tracking and Reproducibility (Sprint 1) with measurable success criteria and next actions.
Lab Exercise
  • Implement a working data workflow for Machine Learning Crash Course: Experiment Tracking and Reproducibility (Sprint 1) with schema/model decisions documented.
  • Run quality checks and performance tuning for Machine Learning Crash Course: Experiment Tracking and Reproducibility (Sprint 1) queries or transformations.
  • Publish Machine Learning Crash Course: Experiment Tracking and Reproducibility (Sprint 1) outputs to a dashboard/report with reproducible refresh steps.
Week 5

Machine Learning Crash Course: Model Serving and API Integration (Sprint 1)

2 hours + labs
Learning Outcomes
  • Identify the principles of Machine Learning Crash Course: Model Serving and API Integration (Sprint 1) and link them to course outcomes in time-boxed sprints with rapid feedback loops.
  • Explain Machine Learning Crash Course: Model Serving and API Integration (Sprint 1) in a guided scenario using realistic tools, constraints, and quality gates.
  • Apply trade-offs, risks, and decision points for Machine Learning Crash Course: Model Serving and API Integration (Sprint 1), then record rationale for stakeholder review.
  • Document a portfolio-ready model evaluation brief for Machine Learning Crash Course: Model Serving and API Integration (Sprint 1) with measurable success criteria and next actions.
Lab Exercise
  • Build a working Machine Learning Crash Course: Model Serving and API Integration (Sprint 1) pipeline from dataset preparation through evaluation and reproducibility checks.
  • Measure Machine Learning Crash Course: Model Serving and API Integration (Sprint 1) quality using task-appropriate metrics and perform controlled hyperparameter tuning.
  • Package Machine Learning Crash Course: Model Serving and API Integration (Sprint 1) for serving or integration with monitoring hooks and rollback strategy.
Week 6

Machine Learning Crash Course: Monitoring, Drift, and Reliability (Sprint 1)

2 hours + labs
Learning Outcomes
  • Apply the principles of Machine Learning Crash Course: Monitoring, Drift, and Reliability (Sprint 1) and link them to course outcomes in time-boxed sprints with rapid feedback loops.
  • Analyze Machine Learning Crash Course: Monitoring, Drift, and Reliability (Sprint 1) in a guided scenario using realistic tools, constraints, and quality gates.
  • Evaluate trade-offs, risks, and decision points for Machine Learning Crash Course: Monitoring, Drift, and Reliability (Sprint 1), then record rationale for stakeholder review.
  • Document a portfolio-ready model evaluation brief for Machine Learning Crash Course: Monitoring, Drift, and Reliability (Sprint 1) with measurable success criteria and next actions.
Lab Exercise
  • Instrument Machine Learning Crash Course: Monitoring, Drift, and Reliability (Sprint 1) with metrics, logs, and tracing hooks aligned to service objectives.
  • Create actionable alerts for Machine Learning Crash Course: Monitoring, Drift, and Reliability (Sprint 1) and test escalation paths using simulated incidents.
  • Perform root-cause analysis for a Machine Learning Crash Course: Monitoring, Drift, and Reliability (Sprint 1) failure scenario and document corrective actions.
Week 7

Machine Learning Crash Course: Responsible AI and Governance (Sprint 1)

2 hours + labs
Learning Outcomes
  • Apply the principles of Machine Learning Crash Course: Responsible AI and Governance (Sprint 1) and link them to course outcomes in time-boxed sprints with rapid feedback loops.
  • Analyze Machine Learning Crash Course: Responsible AI and Governance (Sprint 1) in a guided scenario using realistic tools, constraints, and quality gates.
  • Evaluate trade-offs, risks, and decision points for Machine Learning Crash Course: Responsible AI and Governance (Sprint 1), then record rationale for stakeholder review.
  • Document a portfolio-ready model evaluation brief for Machine Learning Crash Course: Responsible AI and Governance (Sprint 1) with measurable success criteria and next actions.
Lab Exercise
  • Build a working Machine Learning Crash Course: Responsible AI and Governance (Sprint 1) pipeline from dataset preparation through evaluation and reproducibility checks.
  • Measure Machine Learning Crash Course: Responsible AI and Governance (Sprint 1) quality using task-appropriate metrics and perform controlled hyperparameter tuning.
  • Package Machine Learning Crash Course: Responsible AI and Governance (Sprint 1) for serving or integration with monitoring hooks and rollback strategy.
Week 8

Machine Learning Crash Course: Production Hardening and Rollback (Sprint 1)

2 hours + labs
Learning Outcomes
  • Apply the principles of Machine Learning Crash Course: Production Hardening and Rollback (Sprint 1) and link them to course outcomes in time-boxed sprints with rapid feedback loops.
  • Analyze Machine Learning Crash Course: Production Hardening and Rollback (Sprint 1) in a guided scenario using realistic tools, constraints, and quality gates.
  • Evaluate trade-offs, risks, and decision points for Machine Learning Crash Course: Production Hardening and Rollback (Sprint 1), then record rationale for stakeholder review.
  • Document a portfolio-ready model evaluation brief for Machine Learning Crash Course: Production Hardening and Rollback (Sprint 1) with measurable success criteria and next actions.
Lab Exercise
  • Build a working Machine Learning Crash Course: Production Hardening and Rollback (Sprint 1) pipeline from dataset preparation through evaluation and reproducibility checks.
  • Measure Machine Learning Crash Course: Production Hardening and Rollback (Sprint 1) quality using task-appropriate metrics and perform controlled hyperparameter tuning.
  • Package Machine Learning Crash Course: Production Hardening and Rollback (Sprint 1) for serving or integration with monitoring hooks and rollback strategy.
Week 9

Machine Learning Crash Course: ML Problem Framing and Baselines (Sprint 2)

2 hours + labs
Learning Outcomes
  • Apply the principles of Machine Learning Crash Course: ML Problem Framing and Baselines (Sprint 2) and link them to course outcomes in time-boxed sprints with rapid feedback loops.
  • Analyze Machine Learning Crash Course: ML Problem Framing and Baselines (Sprint 2) in a guided scenario using realistic tools, constraints, and quality gates.
  • Evaluate trade-offs, risks, and decision points for Machine Learning Crash Course: ML Problem Framing and Baselines (Sprint 2), then record rationale for stakeholder review.
  • Document a portfolio-ready model evaluation brief for Machine Learning Crash Course: ML Problem Framing and Baselines (Sprint 2) with measurable success criteria and next actions.
Lab Exercise
  • Build a working Machine Learning Crash Course: ML Problem Framing and Baselines (Sprint 2) pipeline from dataset preparation through evaluation and reproducibility checks.
  • Measure Machine Learning Crash Course: ML Problem Framing and Baselines (Sprint 2) quality using task-appropriate metrics and perform controlled hyperparameter tuning.
  • Package Machine Learning Crash Course: ML Problem Framing and Baselines (Sprint 2) for serving or integration with monitoring hooks and rollback strategy.
Week 10

Machine Learning Crash Course: Feature Engineering and Data Pipelines (Sprint 2)

2 hours + labs
Learning Outcomes
  • Analyze the principles of Machine Learning Crash Course: Feature Engineering and Data Pipelines (Sprint 2) and link them to course outcomes in time-boxed sprints with rapid feedback loops.
  • Evaluate Machine Learning Crash Course: Feature Engineering and Data Pipelines (Sprint 2) in a guided scenario using realistic tools, constraints, and quality gates.
  • Create trade-offs, risks, and decision points for Machine Learning Crash Course: Feature Engineering and Data Pipelines (Sprint 2), then record rationale for stakeholder review.
  • Defend a portfolio-ready model evaluation brief for Machine Learning Crash Course: Feature Engineering and Data Pipelines (Sprint 2) with measurable success criteria and next actions.
Lab Exercise
  • Build a release workflow for Machine Learning Crash Course: Feature Engineering and Data Pipelines (Sprint 2) with automated checks, approvals, and artifact traceability.
  • Implement quality and security gates for Machine Learning Crash Course: Feature Engineering and Data Pipelines (Sprint 2) and enforce fail-fast criteria.
  • Execute a staged promotion for Machine Learning Crash Course: Feature Engineering and Data Pipelines (Sprint 2) and validate rollback safety under a controlled failure.
Week 11

Machine Learning Crash Course: Model Training and Evaluation (Sprint 2)

2 hours + labs
Learning Outcomes
  • Analyze the principles of Machine Learning Crash Course: Model Training and Evaluation (Sprint 2) and link them to course outcomes in time-boxed sprints with rapid feedback loops.
  • Evaluate Machine Learning Crash Course: Model Training and Evaluation (Sprint 2) in a guided scenario using realistic tools, constraints, and quality gates.
  • Create trade-offs, risks, and decision points for Machine Learning Crash Course: Model Training and Evaluation (Sprint 2), then record rationale for stakeholder review.
  • Defend a portfolio-ready model evaluation brief for Machine Learning Crash Course: Model Training and Evaluation (Sprint 2) with measurable success criteria and next actions.
Lab Exercise
  • Build a working Machine Learning Crash Course: Model Training and Evaluation (Sprint 2) pipeline from dataset preparation through evaluation and reproducibility checks.
  • Measure Machine Learning Crash Course: Model Training and Evaluation (Sprint 2) quality using task-appropriate metrics and perform controlled hyperparameter tuning.
  • Package Machine Learning Crash Course: Model Training and Evaluation (Sprint 2) for serving or integration with monitoring hooks and rollback strategy.
Week 12

Machine Learning Crash Course: Experiment Tracking and Reproducibility (Sprint 2)

2 hours + labs
Learning Outcomes
  • Analyze the principles of Machine Learning Crash Course: Experiment Tracking and Reproducibility (Sprint 2) and link them to course outcomes in time-boxed sprints with rapid feedback loops.
  • Evaluate Machine Learning Crash Course: Experiment Tracking and Reproducibility (Sprint 2) in a guided scenario using realistic tools, constraints, and quality gates.
  • Create trade-offs, risks, and decision points for Machine Learning Crash Course: Experiment Tracking and Reproducibility (Sprint 2), then record rationale for stakeholder review.
  • Defend a portfolio-ready model evaluation brief for Machine Learning Crash Course: Experiment Tracking and Reproducibility (Sprint 2) with measurable success criteria and next actions.
Lab Exercise
  • Implement a working data workflow for Machine Learning Crash Course: Experiment Tracking and Reproducibility (Sprint 2) with schema/model decisions documented.
  • Run quality checks and performance tuning for Machine Learning Crash Course: Experiment Tracking and Reproducibility (Sprint 2) queries or transformations.
  • Publish Machine Learning Crash Course: Experiment Tracking and Reproducibility (Sprint 2) outputs to a dashboard/report with reproducible refresh steps.
Week 13

Machine Learning Crash Course: Model Serving and API Integration (Sprint 2)

2 hours + labs
Learning Outcomes
  • Analyze the principles of Machine Learning Crash Course: Model Serving and API Integration (Sprint 2) and link them to course outcomes in time-boxed sprints with rapid feedback loops.
  • Evaluate Machine Learning Crash Course: Model Serving and API Integration (Sprint 2) in a guided scenario using realistic tools, constraints, and quality gates.
  • Create trade-offs, risks, and decision points for Machine Learning Crash Course: Model Serving and API Integration (Sprint 2), then record rationale for stakeholder review.
  • Defend a portfolio-ready model evaluation brief for Machine Learning Crash Course: Model Serving and API Integration (Sprint 2) with measurable success criteria and next actions.
Lab Exercise
  • Build a working Machine Learning Crash Course: Model Serving and API Integration (Sprint 2) pipeline from dataset preparation through evaluation and reproducibility checks.
  • Measure Machine Learning Crash Course: Model Serving and API Integration (Sprint 2) quality using task-appropriate metrics and perform controlled hyperparameter tuning.
  • Package Machine Learning Crash Course: Model Serving and API Integration (Sprint 2) for serving or integration with monitoring hooks and rollback strategy.
Week 14

Machine Learning Crash Course: Monitoring, Drift, and Reliability (Sprint 2)

2 hours + labs
Learning Outcomes
  • Analyze the principles of Machine Learning Crash Course: Monitoring, Drift, and Reliability (Sprint 2) and link them to course outcomes in time-boxed sprints with rapid feedback loops.
  • Evaluate Machine Learning Crash Course: Monitoring, Drift, and Reliability (Sprint 2) in a guided scenario using realistic tools, constraints, and quality gates.
  • Create trade-offs, risks, and decision points for Machine Learning Crash Course: Monitoring, Drift, and Reliability (Sprint 2), then record rationale for stakeholder review.
  • Defend a portfolio-ready model evaluation brief for Machine Learning Crash Course: Monitoring, Drift, and Reliability (Sprint 2) with measurable success criteria and next actions.
Lab Exercise
  • Instrument Machine Learning Crash Course: Monitoring, Drift, and Reliability (Sprint 2) with metrics, logs, and tracing hooks aligned to service objectives.
  • Create actionable alerts for Machine Learning Crash Course: Monitoring, Drift, and Reliability (Sprint 2) and test escalation paths using simulated incidents.
  • Perform root-cause analysis for a Machine Learning Crash Course: Monitoring, Drift, and Reliability (Sprint 2) failure scenario and document corrective actions.

Capstone Projects

Project 1: Machine Learning Crash Course Foundation Build

Deliver a concrete foundation implementation covering the first phase of the curriculum.

  • Implement and validate Machine Learning Crash Course: ML Problem Framing and Baselines (Sprint 1).
  • Integrate Machine Learning Crash Course: Feature Engineering and Data Pipelines (Sprint 1) with reusable workflow standards.
  • Publish evidence for Machine Learning Crash Course: Model Training and Evaluation (Sprint 1) with test and quality artifacts.

Project 2: Machine Learning Crash Course Integrated Systems Build

Combine mid-program competencies into a production-style integrated workflow.

  • Build an end-to-end flow around Machine Learning Crash Course: Model Serving and API Integration (Sprint 1) and Machine Learning Crash Course: Monitoring, Drift, and Reliability (Sprint 1).
  • Add controls, observability, and rollback paths for reliability.
  • Document architecture decisions and trade-offs tied to Machine Learning Crash Course: Responsible AI and Governance (Sprint 1).

Project 3: Machine Learning Crash Course Capstone Delivery

Ship a portfolio-ready capstone with measurable outcomes and stakeholder-ready presentation.

  • Deliver a complete implementation centered on Machine Learning Crash Course: Model Training and Evaluation (Sprint 2).
  • Validate readiness for Machine Learning Crash Course: Experiment Tracking and Reproducibility (Sprint 2) using objective acceptance checks.
  • Present final defense and roadmap based on Machine Learning Crash Course: Model Serving and API Integration (Sprint 2) outcomes.