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Applied Machine Learning | Incognition
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Applied Machine Learning
Learn how machine learning is applied to different problem domains and deployed in production
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50 lessons
Chapter 1: Explainable AI
0 / 10 lessons
1
Black-Box Models
2
Global vs Local Explanations
3
Permutation Importance
4
Partial Dependence
5
Global Surrogate Models
6
Shapley Values
7
Local Surrogates (LIME)
8
Counterfactual Explanations
9
Correlated Features Mislead
10
Readable Models First
Chapter 2: Time Series Machine Learning
0 / 10 lessons
1
Random Splits Leak the Future
2
Naive Forecasts
3
Lag Features
4
Rolling-Window Features
5
Holiday and Event Features
6
Trees and Trends
7
Walk-Forward Validation
8
Forecast Error Metrics
9
Forecast Horizons
10
Multi-Step Forecasting
Chapter 3: Recommendation Systems
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1
The User-Item Matrix
2
Sparsity
3
The Popularity Baseline
4
Content-Based Filtering
5
User-Based Collaborative Filtering
6
Item-Based Collaborative Filtering
7
The Cold-Start Problem
8
Matrix Factorisation
9
Precision and Recall at k
10
Filter Bubbles and Diversity
Chapter 4: Anomaly Detection
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1
What Counts as an Anomaly
2
Why Anomaly Labels Are Scarce
3
Learning What Normal Looks Like
4
Per-Group Baselines
5
Distance-Based Detection
6
Local Outlier Factor
7
Isolation Forests
8
Anomalies in Time Series
9
Alert Budgets
10
From Alerts to Labels
Chapter 5: Production Machine Learning
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1
The Machine Learning Lifecycle
2
Saving and Loading a Model
3
Pipelines
4
Batch vs Real-Time Prediction
5
Data Drift vs Concept Drift
6
Detecting Input Drift
7
Retraining Strategies
8
Versioning and Reproducibility
9
A/B Testing a Model
10
Human Review of Predictions
