Homework 1: Introduction to Machine Learning
- Submissions
- 1625
- Completion
- 68.9%
- Median score
- 7
- Lecture time
- 3.5h
- Homework time
- 1h
- Total time
- 5h
Machine Learning Zoomcamp 2025
Machine Learning Zoomcamp 2025 Dashboard
Course-level participation, homework, and project statistics.
Course overview
- Total enrollments
- 2359
- Project submissions
- 885
- Project completion rate
- 19.7%
- Average total score
- 23.2
- Median total score
- 7
- Overall completion rate
- 27.7%
- Graduates
- 400
Project outcomes
- Score to pass
- 7
- Passed
- 766
- Failed
- 119
- Median project score
- 23
Homework statistics
Median time and score by homework. Hover values on desktop for interquartile ranges.
| Homework | Submissions | Completion | Lecture time | Homework time | Total time | Median score |
|---|---|---|---|---|---|---|
| Homework 1: Introduction to Machine Learning | 1625 | 68.9% | 3.5h | 1h | 5h | 7 |
| Homework 2: Machine Learning for Regression | 1025 | 43.5% | 5h | 3h | 8h | 5 |
| Homework 3: Machine Learning for Classification | 856 | 36.3% | 4h | 3h | 7.2h | 5 |
| Homework 4: Evaluation Metrics for Classification | 680 | 28.8% | 4h | 3h | 8h | 5 |
| Homework 5: Deploying Machine Learning Models | 537 | 22.8% | 5h | 3h | 8h | 5 |
| Homework 6: Decision Trees and Ensemble Learning | 393 | 16.7% | 5h | 3h | 8h | 6 |
| Homework 8: Neural Networks and Deep Learning | 336 | 14.2% | 6h | 3h | 10h | 6 |
| Homework 9: Serverless Deep Learning | 249 | 10.6% | 4h | 3h | 7.8h | 5 |
| Homework 10: Kubernetes | 187 | 7.9% | 4h | 2h | 6h | 7 |
Homework 2: Machine Learning for Regression
- Submissions
- 1025
- Completion
- 43.5%
- Median score
- 5
- Lecture time
- 5h
- Homework time
- 3h
- Total time
- 8h
Homework 3: Machine Learning for Classification
- Submissions
- 856
- Completion
- 36.3%
- Median score
- 5
- Lecture time
- 4h
- Homework time
- 3h
- Total time
- 7.2h
Homework 4: Evaluation Metrics for Classification
- Submissions
- 680
- Completion
- 28.8%
- Median score
- 5
- Lecture time
- 4h
- Homework time
- 3h
- Total time
- 8h
Homework 5: Deploying Machine Learning Models
- Submissions
- 537
- Completion
- 22.8%
- Median score
- 5
- Lecture time
- 5h
- Homework time
- 3h
- Total time
- 8h
Homework 6: Decision Trees and Ensemble Learning
- Submissions
- 393
- Completion
- 16.7%
- Median score
- 6
- Lecture time
- 5h
- Homework time
- 3h
- Total time
- 8h
Homework 8: Neural Networks and Deep Learning
- Submissions
- 336
- Completion
- 14.2%
- Median score
- 6
- Lecture time
- 6h
- Homework time
- 3h
- Total time
- 10h
Homework 9: Serverless Deep Learning
- Submissions
- 249
- Completion
- 10.6%
- Median score
- 5
- Lecture time
- 4h
- Homework time
- 3h
- Total time
- 7.8h
Homework 10: Kubernetes
- Submissions
- 187
- Completion
- 7.9%
- Median score
- 7
- Lecture time
- 4h
- Homework time
- 2h
- Total time
- 6h
Assignment difficulty
Ranked by lowest median question score relative to the maximum achievable. Normalizing by question count makes homeworks of different lengths comparable; a lower percentage indicates a harder assignment.
| Rank | Homework | Median score | Score % | Completion |
|---|---|---|---|---|
| 1 | Homework 2: Machine Learning for Regression | 5 / 6 | 83.3% | 43.5% |
| 2 | Homework 3: Machine Learning for Classification | 5 / 6 | 83.3% | 36.3% |
| 3 | Homework 4: Evaluation Metrics for Classification | 5 / 6 | 83.3% | 28.8% |
| 4 | Homework 5: Deploying Machine Learning Models | 5 / 6 | 83.3% | 22.8% |
| 5 | Homework 8: Neural Networks and Deep Learning | 5 / 6 | 83.3% | 14.2% |
| 6 | Homework 9: Serverless Deep Learning | 5 / 6 | 83.3% | 10.6% |
| 7 | Homework 1: Introduction to Machine Learning | 7 / 7 | 100.0% | 68.9% |
| 8 | Homework 6: Decision Trees and Ensemble Learning | 6 / 6 | 100.0% | 16.7% |
| 9 | Homework 10: Kubernetes | 7 / 7 | 100.0% | 7.9% |
Question difficulty
Share of submitted answers that were correct, per question. A lower percentage points to a harder or more confusing question. Participation-only questions are excluded.
| Homework | Question | Correct | Answers | % correct |
|---|---|---|---|---|
| Homework 1: Introduction to Machine Learning | Records count | 1588 | 1625 | 97.7% |
| Fuel types | 1560 | 1625 | 96.0% | |
| Missing values | 1503 | 1625 | 92.5% | |
| Max fuel efficiency | 1562 | 1625 | 96.1% | |
| Median value of horsepower | 1188 | 1625 | 73.1% | |
| Sum of weights | 1454 | 1625 | 89.5% | |
| Homework 2: Machine Learning for Regression | Missing values | 1008 | 1025 | 98.3% |
| Median for horse power | 979 | 1025 | 95.5% | |
| Filling NAs | 715 | 1025 | 69.8% | |
| Best regularization | 600 | 1025 | 58.5% | |
| RMSE Standard Deviation | 725 | 1025 | 70.7% | |
| Evaluation on test | 822 | 1025 | 80.2% | |
| Homework 3: Machine Learning for Classification | Mode for industry | 821 | 856 | 95.9% |
| Biggest correlation | 693 | 856 | 81.0% | |
| Biggest MI | 778 | 856 | 90.9% | |
| Accuracy | 701 | 856 | 81.9% | |
| Feature selection | 479 | 856 | 56.0% | |
| Parameter tuning | 602 | 856 | 70.3% | |
| Homework 4: Evaluation Metrics for Classification | ROC AUC feature importance | 608 | 680 | 89.4% |
| Model AUC | 649 | 680 | 95.4% | |
| Precision and recall | 379 | 680 | 55.7% | |
| F1 score | 516 | 680 | 75.9% | |
| 5-Fold CV standard deviation | 411 | 680 | 60.4% | |
| Best C | 506 | 680 | 74.4% | |
| Homework 5: Deploying Machine Learning Models | Lead score (v1) | 493 | 537 | 91.8% |
| Lead score (v2) | 487 | 537 | 90.7% | |
| Docker image size | 492 | 537 | 91.6% | |
| Lead score (v3) | 186 | 537 | 34.6% | |
| Homework 6: Decision Trees and Ensemble Learning | Most important featute | 374 | 393 | 95.2% |
| RMSE on validation | 366 | 393 | 93.1% | |
| Number of estimators | 351 | 393 | 89.3% | |
| Best max_depth | 326 | 393 | 83.0% | |
| Most important feature | 357 | 393 | 90.8% | |
| XGBoost eta | 319 | 393 | 81.2% | |
| Homework 8: Neural Networks and Deep Learning | Loss | 302 | 336 | 89.9% |
| Number of parameters | 307 | 336 | 91.4% | |
| Median of training accuracy for all the epochs | 272 | 336 | 81.0% | |
| Standard deviation of training loss for all the epochs | 214 | 336 | 63.7% | |
| Mean of test loss for all the epochs | 289 | 336 | 86.0% | |
| Average of test accuracy for the last 5 epochs | 296 | 336 | 88.1% | |
| Homework 9: Serverless Deep Learning | Output name | 233 | 249 | 93.6% |
| Image size | 237 | 249 | 95.2% | |
| First R value after pre-processing | 227 | 249 | 91.2% | |
| Model output | 170 | 249 | 68.3% | |
| Docker image size | 149 | 249 | 59.8% | |
| Model output (docker) | 157 | 249 | 63.1% | |
| Homework 10: Kubernetes | Probability of conversion | 166 | 187 | 88.8% |
| Smallest deployable compute unit | 176 | 187 | 94.1% | |
| Service type | 175 | 187 | 93.6% | |
| Command | 178 | 187 | 95.2% |
Submission timing
When homework submissions arrive relative to the deadline, across all assignments.
- A week or more early
- 525 (8.9%)
- 3-7 days early
- 1170 (19.9%)
- 1-3 days early
- 1650 (28.0%)
- Final day
- 1161 (19.7%)
- After the deadline
- 1382 (23.5%)
Engagement over time
Homework submissions per week, across all assignments. Bars are scaled to the busiest week.
- Sep 15, 2025
- 232
- Sep 22, 2025
- 516
- Sep 29, 2025
- 1035
- Oct 06, 2025
- 1028
- Oct 13, 2025
- 827
- Oct 20, 2025
- 638
- Oct 27, 2025
- 464
- Nov 03, 2025
- 339
- Nov 10, 2025
- 38
- Nov 17, 2025
- 19
- Nov 24, 2025
- 72
- Dec 01, 2025
- 253
- Dec 08, 2025
- 241
- Dec 15, 2025
- 127
- Dec 22, 2025
- 29
- Dec 29, 2025
- 16
- Jan 05, 2026
- 14
Graduates
400 students completed the course and received certificates.