AI & MLNew cohort every six weeks
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AI & MLIntermediate
Applied Machine Learning
For learners who know Python and want to use machine learning properly: framing problems, preparing data, choosing and evaluating models, and deploying them behind an API. Every module ends with a notebook reviewed by a mentor.
- Level
- Intermediate
- Weekly effort
- 8–10 hrs/week
- Format
- Live cohort
- Next batch
- New cohort every six weeks
What you'll be able to do
- Frame a business question as a supervised or unsupervised learning problem
- Clean, explore and engineer features from messy tabular data
- Train and tune models with scikit-learn and gradient boosting
- Build image classifiers with convolutional networks in PyTorch
- Evaluate models honestly with the right metrics and validation
- Serve a model behind a FastAPI endpoint and monitor it
Syllabus
5 modules, each with hands-on assignments reviewed by a mentor.
- 01
Python for data work
- NumPy and vectorised thinking
- pandas for cleaning and joins
- Plotting to find patterns
- Reproducible notebooks
- 02
Core machine learning
- Regression and classification
- Train, validation and test splits
- Bias, variance and regularisation
- Cross-validation
- 03
Models that win on tabular data
- Decision trees and random forests
- Gradient boosting with XGBoost
- Feature engineering
- Handling class imbalance
- 04
Deep learning and computer vision
- Neural network building blocks
- Convolutional networks in PyTorch
- Transfer learning
- Image augmentation with OpenCV
- 05
Evaluation and deployment
- Choosing metrics that match the problem
- Experiment tracking with MLflow
- Serving with FastAPI
- Monitoring for drift
Capstone project
A deployed computer vision service
Train an image classifier on a public dataset, document your evaluation, and deploy it as an API with a simple web front end. You defend your metric choices in a review session.
Tools you'll use
PythonNumPypandasscikit-learnXGBoostPyTorchOpenCVFastAPIMLflow
Who it's for
- Students who have completed a Python course
- Data analysts moving into modelling
- Software developers adding ML to their skill set
What's included
- Live sessions twice a week, recorded
- Mentor review on every module notebook
- GPU notebook environment during the course
- Lifetime access to course material
- Certificate on capstone completion
Questions About This Course
School-level algebra and basic statistics are enough to start. We teach the linear algebra and probability you need alongside the code.
No. Training runs in a hosted notebook environment with GPU access for the duration of the course.
Yes. You should be able to write functions, loops and work with lists and dictionaries. If not, start with the Data Analytics course.
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Enrol
Join the next Applied Machine Learning batch
- A counsellor calls within one working day
- Confirm your batch before paying anything
- New cohort every six weeks, seats capped per batch