DofE AI Gold Award
A 24-week, 6-month Skills section route covering Python machine learning pipelines, feature engineering and a data challenge portfolio project.
24 weeks6 monthsLive onlinePortfolio
Gold Skills Section
DofE AI Gold Award
Gold Outcomes
Students finish with practical Python machine learning experience and a portfolio project that demonstrates sustained applied AI work.
Python ML Pipelines
Students use NumPy, Pandas, Matplotlib and Scikit-learn to build and evaluate model workflows.
PythonScikit-learn
Model Selection
Logistic regression, KNN, decision trees, random forests, clustering and PCA are used in context.
ModelsEvaluation
Portfolio Sprint
The final five-week data challenge project ties data cleaning, features, modelling and results into one showcase.
Data challengePortfolio
Core Curriculum: Weeks 1 to 19
A comprehensive study of machine learning algorithms, covering supervised learning, ensembles, clustering and dimensionality reduction.
1
Applied Machine Learning
Moving from visual blocks to structured programmatic development environments.
2
Python Packages for ML
Using NumPy, Pandas, Matplotlib and Scikit-learn in an end-to-end setup.
3
Logistic Regression Theory
Binary classification, log-odds and the sigmoid activation mapping.
4
Logistic Regression Python
Writing and testing a programmatic binary classification boundary.
5
Regularisation
Preventing overfitting and underfitting with L1 and L2 penalties.
6
K-Nearest Neighbours Theory
Instance-based classification, Euclidean distances and the role of K.
7
KNN in Python
Constructing a KNN classification map over complex feature spreads.
8
KNN Data Imputation
Using KNN models to analyse and fill missing rows in messy datasets.
Project milestone
9
Decision Trees Theory
Gini impurity, entropy, information gain and algorithmic node splits.
10
Decision Trees in Python
Parsing, building and pruning decision tree architectures.
11
Cross-Validation
Using K-fold strategies to evaluate generalisation across test shards.
12
Random Forests Theory
Ensemble modelling, bootstrap aggregating and tree voting.
13
Random Forests Practical
Building predictive ensemble architectures that improve validation accuracy.
14
Feature Engineering
Scaling, normalisation and encoding categorical features for better models.
15
K-Means Theory
Unsupervised learning, centroids and groups inside unlabelled datasets.
16
K-Means Practical
Finding cluster counts with elbow-method and silhouette metrics.
17
PCA Theory
Eigenvalues, eigenvectors, variance retention and dimensionality reduction.
18
PCA Practical
Compressing multi-dimensional data into principal components with Scikit-learn.
19
K-Means plus PCA
Combining PCA and K-Means for faster clustering on complex datasets.
Project milestone
Data Challenge Portfolio Project: Weeks 20 to 24
A five-session applied project sprint where students source a challenge, engineer features, ensemble models and prepare a final portfolio showcase.
20
Data Challenge Project Day 1
Launching the portfolio milestone, sourcing a challenge and setting baselines.
Project milestone
21
Data Challenge Project Day 2
Exploratory data analysis, trend plots and handling structural null values.
Project milestone
22
Data Challenge Project Day 3
Feature transformation, engineered variables and model selection.
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23
Data Challenge Project Day 4
Ensembling estimators, tuning hyperparameters and optimisation checks.
Project milestone
24
Data Challenge Project Day 5
Submission pipeline, results dashboard and final Gold portfolio showcase.
Project milestone
Questions Parents Ask
Clear answers for families deciding whether this route fits their child's current coding level, goals and schedule.
Is Gold suitable for beginners?
Gold is not the beginner route. It is best for students ready for Python programming, structured data work and more demanding applied machine learning.
What is the Gold final project?
The final stage is a guided data challenge portfolio project where students apply the full workflow: data exploration, feature engineering, model selection, tuning, submission and results presentation.
Does Gold meet the longer DofE commitment?
Gold is structured as a 24-week, 6-month route, matching the longer Skills section commitment with sustained weekly technical development.
Ready to Start Gold?
Check the live timetable or book a trial lesson before joining the Gold applied machine learning cohort.