Course · Interview foundations
Machine Learning for Data Engineers
Free machine learning course: the ML workflow, regression, decision trees, random forests and boosting, evaluation metrics and data leakage, with runnable code.
- Lessons
- 5
- Interview questions
- 0
- Projects & case studies
- 0
- Reading time
- ~1 h
Your progress
Saved in this browser onlyCourse structure
- 1 lessonStart hereThe complete overview of the course in one read.
- 1 lessonBeginnerCore concepts you will use every day.
- 3 lessonsIntermediatePatterns used in production pipelines.
Practise
Lessons
Work through the lessons in order. Completed lessons show a tick; lessons you have opened are outlined.
Start here
The complete overview of the course in one read.
Beginner
Core concepts you will use every day.
Intermediate
Patterns used in production pipelines.
- Decision Trees, Random Forests and Gradient BoostingHow decision trees, random forests and gradient boosting work, why a deep tree overfits, and how they compare with logistic regression, with runnable Python.
- Evaluation Metrics: Precision, Recall, ROC AUC and ThresholdsHow to evaluate a classification model: confusion matrix, precision, recall, ROC AUC, and choosing a decision threshold for the business, with runnable Python.
- Overfitting, Cross-Validation and Data LeakageWhy models look better in testing than in real use: overfitting, cross-validation for honest scores, and data leakage, with a runnable leaky-feature example.
Resources
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