Intermediate · Data, Analytics & ML
NEWI4Machine Learning Foundations for Professionals
Understand how machines learn, and build, evaluate, and explain your own predictive models.
Who it's for
Analysts, engineers, and professionals with basic Python or spreadsheet analytics experience.
You'll walk away with
An end-to-end predictive model notebook with business interpretation
Understand and apply core machine learning techniques to build, evaluate, and interpret predictive models for real business problems.
Format
- Lectures + guided coding labs
Assessment
Pre- and post-tests; capstone model and presentation
Tools & platforms
- Pythonpandasscikit-learnJupyter / Google Colab
Programme Curriculum
What you'll be able to do
- Explain supervised and unsupervised learning and when to use each
- Prepare data and engineer useful features
- Train and evaluate regression, classification, and clustering models
- Interpret results and communicate model limitations to stakeholders
Your learning journey
Module 1
Machine Learning Essentials
- The ML WorkflowCRISP-DM; framing ML problems; Python, pandas, and Jupyter essentials
- Exploratory Data AnalysisData quality; visualization; feature engineering
Module 2
Supervised Learning
- Regression & ClassificationLinear and logistic regression; decision trees; random forests; gradient boosting
- Model EvaluationRMSE; precision, recall, and F1; ROC-AUC; cross-validation
Module 3
Unsupervised Learning & Beyond
- Finding Structure in DataClustering; dimensionality reduction; anomaly detection
- A First Look at Neural NetworksFrom perceptrons to deep learning; when deep learning is worth it
Module 4
Responsible Modeling & Capstone
- Trustworthy ModelsOverfitting and data leakage; explainability (SHAP); fairness checks
- CapstoneBuild, evaluate, and present a model for a business problem