The CertNexus AI Practitioner (CAIP / AIP‑210) 5‑day course covers the full end‑to‑end AI/ML workflow, from business problem framing through to MLOps and model deployment.
1. Framing and Identifying AI/ML Solutions
- Identify business problems suitable for AI/ML
- Distinguish AI, ML, and data science roles
- Understand ethical considerations and governance
- Select appropriate ML approaches for different problem types
2. Data Preparation & Feature Engineering
- Prepare, clean, and transform raw data
- Handle missing values, outliers, and scaling
- Encode categorical variables
- Engineer and select features for ML
- Understand data distributions and statistical foundations
3. Building & Evaluating Machine Learning Models
This is the largest section of the course.
Regression
- Build linear regression models
- Build regularised regression models (Lasso, Ridge)
- Build forecasting models
Classification
- Logistic regression
- k‑Nearest Neighbour (k‑NN)
- Decision trees
- Random forests
- Support Vector Machines (SVMs)
Unsupervised Learning
- Clustering (e.g., k‑means)
Deep Learning
- Build artificial neural networks
- Understand activation functions, layers, and training cycles
Model Evaluation & Tuning
- Train/validation/test splits
- Hyperparameter tuning
- Avoiding overfitting
- Performance metrics for regression, classification, and clustering
4. MLOps & Operationalising AI Systems
- Automate ML workflows
- Integrate models into production systems
- Monitor model performance
- Detect and manage model drift
- Maintain pipelines and retrain models
🧭 What You Will Be Able to Do by the End
- Solve business problems using AI/ML
- Prepare and analyse data
- Build, tune, and evaluate ML models
- Deploy and maintain ML systems in production