Machine Learning Basics
Enough ML to work alongside it and ship features that use it.
Backend Engineer track
Junior
Write correct code, ship small changes safely, ask good questions.
Nothing here yet.
Mid-level
Own a feature end to end without hand-holding.
- Deep LearningNeural networks with many layers.
- Machine LearningSoftware that learns patterns from data instead of following explicit rules.
- ModelThe learned function that turns inputs into predictions.
- Neural NetworkLayers of weighted connections that learn from data.
- Supervised LearningLearning from labeled examples.
- Training vs InferenceBuilding a model vs using it to make predictions.
Senior
Own a system, its failure modes, and its trade-offs.
- Classification vs RegressionPredicting categories vs predicting numbers.
- FeatureAn input variable a model uses.
- Model ServingDeploying a model behind an API.
- OverfittingA model memorizing its training data instead of generalizing.
- Recommendation SystemSuggesting items based on behavior and similarity.
- Train / Test SplitHolding out data to evaluate a model honestly.
- Unsupervised LearningFinding structure in unlabeled data.
Staff
Shape how many teams build, across systems.
Nothing here yet.
Principal
Set technical direction for the organization.
Nothing here yet.
Data Analyst track
Junior
Write correct SQL, build trusted dashboards, ask good questions.
Nothing here yet.
Mid-level
Own an analysis end to end, from vague question to recommendation.
- Machine LearningSoftware that learns patterns from data instead of following explicit rules.
- ModelThe learned function that turns inputs into predictions.
- OverfittingA model memorizing its training data instead of generalizing.
- Train / Test SplitHolding out data to evaluate a model honestly.
Senior
Own experimentation and metrics design; call out bad numbers.
Nothing here yet.
Staff
Shape how the organization measures and decides.
Nothing here yet.
Principal
Set measurement strategy across the company.
Nothing here yet.
Data Engineer track
Junior
Build and fix pipelines from clear specs; write correct SQL.
Nothing here yet.
Mid-level
Own pipelines and models end to end, including their quality.
- Deep LearningNeural networks with many layers.
- Machine LearningSoftware that learns patterns from data instead of following explicit rules.
- ModelThe learned function that turns inputs into predictions.
- Neural NetworkLayers of weighted connections that learn from data.
- Supervised LearningLearning from labeled examples.
- Training vs InferenceBuilding a model vs using it to make predictions.
Senior
Design the platform's storage, processing and modeling choices.
- Classification vs RegressionPredicting categories vs predicting numbers.
- FeatureAn input variable a model uses.
- Model ServingDeploying a model behind an API.
- OverfittingA model memorizing its training data instead of generalizing.
- Recommendation SystemSuggesting items based on behavior and similarity.
- Train / Test SplitHolding out data to evaluate a model honestly.
- Unsupervised LearningFinding structure in unlabeled data.
Staff
Shape how the whole organization produces and uses data.
Nothing here yet.
Principal
Set data strategy and architecture across the company.
Nothing here yet.
Frontend Engineer track
Junior
Build UI that works, ship small changes safely, ask good questions.
Nothing here yet.
Mid-level
Own a feature end to end without hand-holding.
- Deep LearningNeural networks with many layers.
- Machine LearningSoftware that learns patterns from data instead of following explicit rules.
- ModelThe learned function that turns inputs into predictions.
- Neural NetworkLayers of weighted connections that learn from data.
- Supervised LearningLearning from labeled examples.
- Training vs InferenceBuilding a model vs using it to make predictions.
Senior
Own an app's architecture, performance, and failure modes.
- Classification vs RegressionPredicting categories vs predicting numbers.
- OverfittingA model memorizing its training data instead of generalizing.
- Recommendation SystemSuggesting items based on behavior and similarity.
- Train / Test SplitHolding out data to evaluate a model honestly.
- Unsupervised LearningFinding structure in unlabeled data.
Staff
Shape how many teams build, across apps.
Nothing here yet.
Principal
Set technical direction for the organization.
Nothing here yet.