The Kaggle Book

Preface
Part I: Introduction to Competitions
Introducing Kaggle and Other Data Science Competitions
	The rise of data science competition platforms
		The Kaggle competition platform
			A history of Kaggle
		Other competition platforms
	Introducing Kaggle
		Stages of a competition
		Types of competitions and examples
		Submission and leaderboard dynamics
			Explaining the Common Task Framework paradigm
			Understanding what can go wrong in a competition
		Computational resources
			Kaggle Notebooks
		Teaming and networking
		Performance tiers and rankings
		Criticism and opportunities
	Summary
Organizing Data with Datasets
	Setting up a dataset
	Gathering the data
	Working with datasets
	Using Kaggle Datasets in Google Colab
	Legal caveats
	Summary
Working and Learning with Kaggle Notebooks
	Setting up a Notebook
	Running your Notebook
	Saving Notebooks to GitHub
	Getting the most out of Notebooks
		Upgrading to Google Cloud Platform (GCP)
		One step beyond
	Kaggle Learn courses
	Summary
Leveraging Discussion Forums
	How forums work
	Example discussion approaches
	Netiquette
	Summary
Part II: Sharpening Your Skills for Competitions
Competition Tasks and Metrics
	Evaluation metrics and objective functions
	Basic types of tasks
		Regression
		Classification
		Ordinal
	The Meta Kaggle dataset
	Handling never-before-seen metrics
	Metrics for regression (standard and ordinal)
		Mean squared error (MSE) and R squared
		Root mean squared error (RMSE)
		Root mean squared log error (RMSLE)
		Mean absolute error (MAE)
	Metrics for classification (label prediction and probability)
		Accuracy
		Precision and recall
		The F1 score
		Log loss and ROC-AUC
		Matthews correlation coefficient (MCC)
	Metrics for multi-class classification
	Metrics for object detection problems
		Intersection over union (IoU)
		Dice
	Metrics for multi-label classification and recommendation problems
		MAP@{K}
	Optimizing evaluation metrics
		Custom metrics and custom objective functions
		Post-processing your predictions
			Predicted probability and its adjustment
	Summary
Designing Good Validation
	Snooping on the leaderboard
	The importance of validation in competitions
		Bias and variance
	Trying different splitting strategies
		The basic train-test split
		Probabilistic evaluation methods
			k-fold cross-validation
			Subsampling
			The bootstrap
	Tuning your model validation system
	Using adversarial validation
		Example implementation
		Handling different distributions of training and test data
	Handling leakage
	Summary
Modeling for Tabular Competitions
	The Tabular Playground Series
	Setting a random state for reproducibility
	The importance of EDA
		Dimensionality reduction with t-SNE and UMAP
	Reducing the size of your data
	Applying feature engineering
		Easily derived features
		Meta-features based on rows and columns
		Target encoding
		Using feature importance to evaluate your work
	Pseudo-labeling
	Denoising with autoencoders
	Neural networks for tabular competitions
	Summary
Hyperparameter Optimization
	Basic optimization techniques
		Grid search
		Random search
		Halving search
	Key parameters and how to use them
		Linear models
		Support-vector machines
		Random forests and extremely randomized trees
		Gradient tree boosting
			LightGBM
			XGBoost
			CatBoost
			HistGradientBoosting
	Bayesian optimization
		Using Scikit-optimize
		Customizing a Bayesian optimization search
		Extending Bayesian optimization to neural architecture search
		Creating lighter and faster models with KerasTuner
		The TPE approach in Optuna
	Summary
Ensembling with Blending and Stacking Solutions
	A brief introduction to ensemble algorithms
	Averaging models into an ensemble
		Majority voting
		Averaging of model predictions
		Weighted averages
		Averaging in your cross-validation strategy
		Correcting averaging for ROC-AUC evaluations
	Blending models using a meta-model
		Best practices for blending
	Stacking models together
		Stacking variations
	Creating complex stacking and blending solutions
	Summary
Modeling for Computer Vision
	Augmentation strategies
		Keras built-in augmentations
			ImageDataGenerator approach
			Preprocessing layers
		albumentations
	Classification
	Object detection
	Semantic segmentation
	Summary
Modeling for NLP
	Sentiment analysis
	Open domain Q&A
	Text augmentation strategies
		Basic techniques
		nlpaug
	Summary
Simulation and Optimization Competitions
	Connect X
	Rock-paper-scissors
	Santa competition 2020
	The name of the game
	Summary
Part III: Leveraging Competitions for Your Career
Creating Your Portfolio of Projects and Ideas
	Building your portfolio with Kaggle
		Leveraging Notebooks and discussions
		Leveraging Datasets
	Arranging your online presence beyond Kaggle
		Blogs and publications
		GitHub
	Monitoring competition updates and newsletters
	Summary
Finding New Professional Opportunities
	Building connections with other competition data scientists
	Participating in Kaggle Days and other Kaggle meetups
	Getting spotted and other job opportunities
		The STAR approach
	Summary (and some parting words)
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Index