▲
0
0

report

Udemy - End-to-End Machine Learning - Business Problem to Production

seeders: 4
leechers: 13
Download torrent
Added 2 weeks ago by freecoursewb in Other
Download Fast Safe Anonymous
movies, software, shows...

Files

Udemy - End-to-End Machine Learning - Business Problem to Production (Size: 317 MB)
  Bonus Resources.txt 102.4 B
  Get Bonus Downloads Here.url 204.8 B
  ~Get Your Files Here !
  10 - Capstone
  37. Knowledge check - Worked solution walkthrough.html 19.7 KB
  38. Knowledge check - Where to go next in this catalogue.html 18.8 KB
  51. Capstone brief end to end on a new dataset.mp4 19.6 MB
  51. Capstone brief end to end on a new dataset_en-US.srt 9 KB
  Datasets
  accounts_raw.csv 4.3 MB
  figures.json 1.3 KB
  lab
  Lab_9.1.py 16.8 KB
  README.md 3.7 KB
  SOLUTION.md 6.2 KB
  capstone_template
  2 - Orientation
  1. Knowledge check - What this course is, and who it is for.html 18.7 KB
  2. Knowledge check - The machine learning lifecycle in one diagram.html 19.7 KB
  3. Knowledge check - Why most models never reach production.html 19.6 KB
  5. Setting up Python, Jupyter and scikit-learn.mp4 20.4 MB
  5. Setting up Python, Jupyter and scikit-learn_en-US.srt 9.1 KB
  6. The dataset we build on for the whole course.mp4 21 MB
  6. The dataset we build on for the whole course_en-US.srt 9.7 KB
  Datasets
  accounts_raw.csv 4.3 MB
  data_dictionary.csv 1.4 KB
  figures.json 1.3 KB
  lab
  3 - Business understanding
  4 - Data understanding
  10. Knowledge check - Data architecture and where data actually lives.html 20.2 KB
  11. Knowledge check - Centralising the data asset.html 19.5 KB
  12. Knowledge check - Data quality the six checks that catch most problems.html 20.4 KB
  13. Knowledge check - Leakage the failure that looks like success.html 20.4 KB
  15. Profiling a dataset you have never seen.mp4 14.9 MB
  15. Profiling a dataset you have never seen_en-US.srt 9.7 KB
  17. Hands-on exploratory analysis end to end.mp4 19.7 MB
  17. Hands-on exploratory analysis end to end_en-US.srt 9.3 KB
  Datasets
  accounts_month13.csv 1.1 MB
  accounts_raw.csv 4.3 MB
  data_dictionary.csv 1.4 KB
  figures.json 1.3 KB
  lab
  5 - Data preparation
  14. Knowledge check - Data preparation 101.html 20 KB
  15. Knowledge check - Missing values mechanisms and remedies.html 20.5 KB
  16. Knowledge check - Encoding categorical variables.html 19.5 KB
  17. Knowledge check - Scaling, and when it matters.html 19.5 KB
  18. Knowledge check - Class imbalance resampling and class weights.html 20.6 KB
  21. Hands-on imputation compared.mp4 21.1 MB
  21. Hands-on imputation compared_en-US.srt 9.4 KB
  25. Hands-on building a preprocessing pipeline.mp4 21 MB
  25. Hands-on building a preprocessing pipeline_en-US.srt 9.7 KB
  Datasets
  accounts_raw.csv 4.3 MB
  data_dictionary.csv 1.4 KB
  figures.json 1.3 KB
  lab
  6 - Feature engineering
  19. Knowledge check - What feature engineering actually is.html 20.4 KB
  20. Knowledge check - Features from time, text and categories.html 20.5 KB
  21. Knowledge check - Feature selection filter, wrapper, embedded.html 20.4 KB
  22. Knowledge check - The feature store, and why teams end up needing one.html 20.5 KB
  28. Hands-on engineering features that move the metric.mp4 18.4 MB
  28. Hands-on engineering features that move the metric_en-US.srt 9 KB
  30. Hands-on Boruta and recursive elimination.mp4 21.6 MB
  30. Hands-on Boruta and recursive elimination_en-US.srt 9.5 KB
  Datasets
  accounts_raw.csv 4.3 MB
  lab
  7 - Modelling
  23. Knowledge check - Building your first classification model.html 20.2 KB
  24. Knowledge check - Building your first regression model.html 20.3 KB
  25. Knowledge check - Cross-validation done properly.html 20.3 KB
  26. Knowledge check - Hyperparameter tuning without fooling yourself.html 20.3 KB
  27. Knowledge check - Ensembles bagging, boosting and stacking.html 20.3 KB
  34. Hands-on baseline before anything clever.mp4 18.6 MB
  34. Hands-on baseline before anything clever_en-US.srt 8.7 KB
  37. Hands-on tuning with grid and random search.mp4 20.7 MB
  37. Hands-on tuning with grid and random search_en-US.srt 9.4 KB
  Datasets
  accounts_raw.csv 4.3 MB
  lab
  8 - Evaluation
  28. Knowledge check - Choosing a metric that matches the decision.html 20.4 KB
  29. Knowledge check - Precision, recall and the cost of each error.html 20.2 KB
  30. Knowledge check - Calibration when a probability is not a probability.html 20.5 KB
  31. Knowledge check - Interpreting the model for a business audience.html 20.5 KB
  41. Hands-on threshold selection on a real cost curve.mp4 19.6 MB
  41. Hands-on threshold selection on a real cost curve_en-US.srt 9.6 KB
  44. Hands-on explaining a prediction with SHAP.mp4 22 MB
  44. Hands-on explaining a prediction with SHAP_en-US.srt 9.6 KB
  Datasets
  accounts_raw.csv 4.3 MB
  figures.json 1.3 KB
  lab
  9 - Into production
  32. Knowledge check - Deployment patterns batch, online, streaming, edge.html 20.2 KB
  33. Knowledge check - The handoff what engineering needs from you.html 19.4 KB
  34. Knowledge check - Load and performance testing a model service.html 19.6 KB
  35. Knowledge check - Model monitoring concept and data drift.html 20.4 KB
  36. Knowledge check - Retraining policy when, on what, and who decides.html 19.7 KB
  49. Hands-on a drift detector you can actually run.mp4 20.8 MB
  49. Hands-on a drift detector you can actually run_en-US.srt 9.3 KB
  Datasets
  accounts_month13.csv 1.1 MB
  accounts_raw.csv 4.3 MB
  figures.json 1.3 KB
  lab
  Lab_8.5.py 23 KB
  README.md 6.1 KB
  SOLUTION.md 6.3 KB
  benchmark.json 2 KB
  expected_output.txt 12.1 KB
  model_figures.json 3.3 KB
  Lab_7.3.py 12.1 KB
  Lab_7.6.py 15.3 KB
  README.md 2.8 KB
  SOLUTION.md 5.5 KB
  expected_output.txt 5.9 KB
  model_figures.json 3.3 KB
  Lab_6.3.py 11.3 KB
  Lab_6.6.py 15.6 KB
  README.md 5.3 KB
  SOLUTION.md 5.9 KB
  expected_output.txt 8 KB
  Lab_5.3.py 18.8 KB
  Lab_5.5.py 16.2 KB
  README.md 4.9 KB
  SOLUTION.md 7.1 KB
  expected_output.txt 9.8 KB
  Lab_4.3.py 19.5 KB
  Lab_4.7.py 16.9 KB
  README.md 3.7 KB
  SOLUTION.md 5.4 KB
  expected_output.txt 8.1 KB
  model_figures.json 3.3 KB
  Lab_3.3.py 11.9 KB
  Lab_3.5.py 17.5 KB
  README.md 3.5 KB
  SOLUTION.md 7.6 KB
  expected_output.txt 10.5 KB
  model_figures.json 3.3 KB
  4. Knowledge check - Starting from a business problem, not a dataset.html 20.1 KB
  5. Knowledge check - Identifying machine learning use cases.html 20.2 KB
  6. Knowledge check - When machine learning is the wrong tool.html 19.6 KB
  7. Knowledge check - Framing the target variable.html 20.2 KB
  8. Knowledge check - Defining success before you model.html 19.6 KB
  9. Knowledge check - Worked example scoping a churn problem.html 19.6 KB
  Lab_1.4.py 8.5 KB
  Lab_1.5.py 12.2 KB
  README.md 3.1 KB
  SOLUTION.md 5.9 KB
  expected_output.txt 6.6 KB
  model_figures.json 3.3 KB
  README.md 204.8 B
  benchmark.json 102.4 B
  expected_output.txt 10.3 KB
  make_dataset.py 921.6 B
  memo.md 1.7 KB
  monitor.py 716.8 B
  serve.py 614.4 B
  train.py 716.8 B

Description


End-to-End Machine Learning: Business Problem to Production
https://WebToolTip.com
Published 10/2026

Created by Real Numbers

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch

Level: All Levels | Genre: eLearning | Language: English | Duration: 54 Lectures ( 1h 26m ) | Size: 302.6 MB
One bank churn problem, from vague request to monitored production model. Python, scikit-learn, fully reproducible.
What you'll learn

⚡ Turn a vague business request into a modelling problem: define the target, set the outcome window, and agree what success means before writing any code

⚡ Diagnose and repair a genuinely dirty extract - duplicates, impossible values, mixed date formats and missing data - in the order that does not corrupt the fix

⚡ Detect target, group and preparation leakage, and explain why a feature that lifts AUC to 0.991 has to be thrown away rather than shipped

⚡ Choose a metric and a decision threshold from the cost of each error, and show a sponsor the model's value in pounds against a do-nothing baseline

⚡ Build leak-proof scikit-learn pipelines with cross-validation and honest tuning, and know when a score improvement is smaller than the fold-to-fold noise

⚡ Hand a model to engineering with a real interface contract, then monitor it for concept drift that no distribution test will ever detect
Requirements

❗ You can read Python and have used pandas a little. You do not need to be fluent, and every notebook runs top to bottom unmodified

❗ School-level algebra. No calculus and no linear algebra background is assumed anywhere

❗ A laptop that runs Python 3.10 or later. Everything is CPU-only - no GPU, no cloud account, no paid service

❗ No prior machine learning experience. The lifecycle, the vocabulary and the dataset are all built from scratch in the opening sections

Related Torrents

torrent name size uploader age seed leech
Udemy - End Impostor Syndrome in seven steps Posted by freecoursewb in Other
3.5 GB freecoursewb 3 months 1 11
Udemy - React, Redux and Next.js End-to-End Projects Course Posted by freecoursewb in Other
299.1 MB freecoursewb 5 months 1 2
Udemy - Agentic Workflows - Design End-to-End AI Processes Posted by freecoursewb in Other
1.4 GB freecoursewb 7 months 10 1
Udemy - Modern Revenue Cycle Management (RCM) - End-to-End Strategy Posted by freecoursewb in Other
2.3 GB freecoursewb 7 months 4 0
Udemy - SAP FSCM Collections Management End User Business Process Posted by freecoursewb in Other
837.2 MB freecoursewb 8 months 0 2