Domain 05
Machine Learning (ML)
The full workflow, not just the model: framing the problem, preparing the data, training, and being honest about how well the result actually works.
Learning objectives
What you will be able to do.
- Frame a business problem as a learning problem
- Prepare, clean and engineer features from real data
- Train and tune models appropriate to the task
- Evaluate results and diagnose failure
- Package a model so something else can use it
Who this is for
- Learners comfortable with Python who want to build predictive systems
- Students with a mathematics or statistics background
Prerequisites: Python fundamentals are expected
Tools and technologies
- Python
- Pandas
- NumPy
- scikit-learn
- Matplotlib
- Jupyter
- Git
Roles this prepares you for
- Machine Learning Engineer
- ML Associate
- Data Scientist
Curriculum
5 modules, in the order they build on each other.
Each module ends in something you have made work, not just something you have watched.
- 01
Data preparation
- Cleaning and missing data
- Feature engineering
- Encoding and scaling
- Splits, and how leakage happens
- 02
Core algorithms
- Linear and logistic regression
- Trees and ensembles
- Clustering
- Choosing an algorithm for the problem
- 03
Training and tuning
- Loss functions and optimisation
- Cross-validation
- Hyperparameter tuning
- Overfitting and regularisation
- 04
Evaluation
- Metrics beyond accuracy
- Confusion matrices and thresholds
- Error analysis
- Reporting results honestly
- 05
Deployment
- Serialising a model
- Serving predictions
- Monitoring drift
- Reproducible pipelines
Practical work
What you could build in this domain.
Project briefs are agreed with your mentor at the start, so the work suits the level you are actually at.
- 01
A churn prediction model with a documented evaluation
- 02
A recommendation prototype
- 03
A regression model with feature-importance analysis
Programs
How to study Machine Learning.
The same domain, in the format that fits your situation.
Machine Learning Internship
Train, evaluate and iterate on a model against a real problem statement, with mentor review of your methodology.
- Data preparation and feature engineering
- Model selection and training
- Evaluation metrics and error analysis
- Reporting results honestly, including what did not work
You finish with: A trained model with an evaluation report and a reproducible pipeline.
AI & Machine Learning Career Program
AI fundamentals and applied machine learning, taken through the full workflow from problem framing to an evaluated, deployable model.
- AI fundamentals and where ML actually fits
- Feature engineering and the training loop
- Evaluation, error analysis and iteration
- Serving a model and monitoring it
- Mock assessments, interview practice and portfolio review
You finish with: An end-to-end ML project with a documented evaluation and a served model.
Keep looking
Other domains.
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