$1,999

Professional XGBoost Cohort + Consult

Course
108 Lessons

6 weeks of professional tabular learning. Includes live Q&A. Homework and accountability. Includes private calls.

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Getting Started

Creating a GitHub Project to Store Your Homework

Weekly Instructions and Success Tips for Your XGBoost Course

Week 1: Python & Pandas Review

Peer Review

00-Intro.mov

01-Classes.mov

02-Pipelines.mov

03-Functions-Parameters.mov

04-Lambda.mov

05-Lambda-Pandas.mov

06-Parameters.mov

07-Unpacking.mov

08-Assign.mov

09-Plotting-Intro.mov

10-Line-Plots.mov

11-Bar-Plots.mov

12-Histograms.mov

13-Scatter-Plots.mov

14-PCA.mov

15-Conclusion.mov

Week 1: Homework & Cheatsheet

01-Homework.ipynb

01-pypd.ipynb

01-python-pandas.pdf

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Week 2: ML Fundamentals

201-Intro.mov

202-Regression.mov

203-Linear-Regression.mov

204-Tree-Models.mov

205-Feature-Engineering.mov

206-Pipelines.mov

207-Categorical-Data.mov

208-Missing-Data.mov

209-Python-Transformers.mov

210-Large-Pipelines.mov

211-Splitting-Data.mov

212-Conclusion.mov

Week 2: Notebooks

02-ml.ipynb

02-sklearn.pdf

02-Homework.ipynb

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Week 3: Tuning Models

301-Intro.mov

302-Basic-Model.mov

303-Overfitting-Evidence-Classification.mov

304-Objectives-Cls.mov

305-XGB-Hyperparameters.mov

306-Stepwise-Tuning-Cls.mov

307-Stepwise-Decision-Tree.mov

308-Regression-Data.mov

309-Default-XGB-Regression.mov

310-Overfitting-Reg.mov

312-Stepwise-Tuning-Reg.mov

311-Objectives-Reg.mov

313-Conclusion.mov

Week 3: Notebooks

03-xgboost.ipynb

03-Homework.ipynb

03-hyperparams.pdf

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Week 4: Model and Data Evaluation

402-Tuned-Classifier.mov

403-Classification-Metrics.mov

404-Confusion-Matrix.mov

405-ROC.mov

406-Precision-Recall-Curve.mov

407-Cost-Model.mov

408-Learning-Curve.mov

409-Missing-Values.mov

410-Duplicate-Values.mov

411-Outliers.mov

412-PCA.mov

413-Regression.mov

414-Correlation.mov

415-Residual-Plots.mov

416-Residual-Analysis.mov

417-Residual-Metrics.mov

418-Learning-Curve.mov

419-Missing-Values.mov

420-Duplicates.mov

421-Outliers.mov

422-PCA.mov

423-Conclusion.mov

Week 4: Notebooks

04-evaluation-quality.ipynb

04-evaluation.pdf

04-Homework.ipynb

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Week 5: Interactions and Constraints

501-Load-Regression.mov

502-Feature-Importance.mov

503-SHAP.mov

504-PDP.mov

505-Monotonic-Constraints.mov

506-Classification.mov

507-Feature-Importance.mov

508-SHAP.mov

509-Monotonic-Constraints.mov

510-Conclusion.mov

Week 5: Notebooks

05-Homework.ipynb

05-shap-constraints.ipynb

05-SHAP.pdf

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Week 6: Problematic Samples

601-Kfold.mov

602-PCA.mov

603-Dupes.mov

604-Sample-Weights.mov

605-Regression.mov

606-Kfold.mov

607-PCA.mov

608-Sample-Weights.mov

Week 6: Notebooks

06-Homework.ipynb

06-kfold.ipynb

06-kfold.pdf

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