Machine Learning using Python (Udemy) | Certification 🎓
Decision Trees / Random Forest / Ada & Gradient Boosting...
- Palmer Penguins notebook
- Banknote Authentication (refer above notebook)
- Rock Density Xray (regression) (refer above notebook)
- Mushroom dataset (refer above notebook)
- Telco Customer Churn notebook
...XGBoost
- Car Sales Price Prediction notebook
- Santander Customer Transaction Prediction notebook
- Titanic notebook
Neural Networks
- Tweet Classification (bi-directional LSTM) notebook
- Fake News detection notebook
- Telco Churn notebook
- Customer Attrition (simple NN) - proprietary dataset notebook
SVM classification / regression: includes building pipeline
- Cement slump dataset notebook
- Wine Fraud dataset (refer above notebook)
KNN classification: includes building pipeline
- Gene expression/ Cancer dataset notebook
- Mine/ Rock dataset (refer above notebook)
Logistic regression and regularization: includes threshold tuning
- Hearing test notebook
- Heart disease (refer above notebook)
- Online advertising (refer above notebook)
- IRIS (refer above notebook)
- Titanic (refer above notebook, +XGBoost notebook)
Linear regression and regularization:
NLP / Naive Bayes
- Large Movie Review dataset notebook
Recommender System
- Grouplens Movie Rating dataset notebook
Other examples
- PCA based approach to finding relative value trades on Treasury Yield Curve notebook
- Imbalanced learning on Customer Attrition dataset (proprietary) with 1:50 imbalance - using techniques from data resampling, cost-sensitive learning, ensemble-learning and anomaly algorithm approaches notebook
References & Fun reads:
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XGBoost Tuning - Owen Zhang's utube and slides, MLM post, Tianqi Chen talk and ppt, Martin Jullum
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TensorFlow - playground
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Trees
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Suppor Vector Machines
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Imbalanced Learning
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Misc
- Imbalanced learning - different approaches
- Cross Validation - scoring objects, different approaches
- Feature Engineering - MLM article, HSE lectures
- Kaggle winners - Heritage Provider Network
- Statistics - P-hacking 538
- Technical Papers - separate repo
- Python stylyguides - Google guide, PEP8, YAPF
- Applied ML at Facebook - pdf, link