Data Science & MLIntermediate
Machine Learning Fundamentals
Covers the entire machine learning pipeline with Python and scikit-learn: data preparation, model selection, training, evaluation, and systematic improvement. You will build regression, classification, and ensemble models on real datasets, and develop the habits that separate experimental code from production-ready work.
Tools & Technologies
Pythonscikit-learnpandasmatplotlib
Course Curriculum
1
ML Foundations
- Supervised vs unsupervised learning
- Bias-variance tradeoff
- Train, validation, and test splits — and why they matter
2
Regression Models
- Linear and polynomial regression
- Regularisation with Ridge and Lasso
- Evaluation metrics: RMSE, MAE, R²
3
Classification Models
- Logistic regression, decision trees, random forests
- Gradient boosting with XGBoost
- Confusion matrices, precision, recall, F1
4
Model Improvement
- Cross-validation
- Hyperparameter tuning with GridSearchCV and RandomizedSearchCV
- Feature engineering and scikit-learn pipelines
What's Included
Live instructor-led session
Small cohort
Course materials pack (slides, code, datasets)
Certificate of completion
14-day email support
£2,230Early bird
£1,495Save £735
per person
14 hours (2 days)
Next: Tue, 7 Apr 2026
6 seats remaining
Intermediate level
6/12 seats filled
Completion certificate included