- Scikit-Learn (or sklearn) library
- Overview of k-NN (sklearn's documentation)
- Overview of Linear Models (sklearn's documentation)
- Overview of Decision Trees (sklearn's documentation)
- Overview of algorithms and parameters in H2O documentation
- Vowpal Wabbit repository
- XGBoost repository
- LightGBM repository
- Interactive demo of simple feed-forward Neural Net
- Frameworks for Neural Nets: Keras,PyTorch,TensorFlow,MXNet, Lasagne
- Example from sklearn with different decision surfaces
- Arbitrary order factorization machines
- Basic SciPy stack (ipython, numpy, pandas, matplotlib)
- Jupyter Notebook
- Stand-alone python tSNE package
- Libraries to work with sparse CTR-like data: LibFM, LibFFM
- Another tree-based method: RGF (implemetation, paper)
- Python distribution with all-included packages: Anaconda
- Blog "datas-frame" (contains posts about effective Pandas usage)
- Preprocessing in Sklearn
- Andrew NG about gradient descent and feature scaling
- Feature Scaling and the effect of standardization for machine learning algorithms
- Discover Feature Engineering, How to Engineer Features and How to Get Good at It
- Discussion of feature engineering on Quora
Bag of words
Word2vec
- Tutorial to Word2vec
- Tutorial to word2vec usage
- Text Classification With Word2Vec
- Introduction to Word Embedding Models with Word2Vec
NLP Libraries
Pretrained models
Finetuning
- How to Retrain Inception's Final Layer for New Categories in Tensorflow
- Fine-tuning Deep Learning Models in Keras
- Perfect score script by Oleg Trott -- used to probe leaderboard
- Page about data leakages on Kaggle
- Evaluation Metrics for Classification Problems: Quick Examples + References
- Decision Trees: “Gini” vs. “Entropy” criteria
- Understanding ROC curves
- Learning to Rank using Gradient Descent -- original paper about pairwise method for AUC optimization
- Overview of further developments of RankNet
- RankLib (implemtations for the 2 papers from above)
- Learning to Rank Overview
- Tuning the hyper-parameters of an estimator (sklearn)
- Optimizing hyperparameters with hyperopt
- Complete Guide to Parameter Tuning in Gradient Boosting (GBM) in Python
- Far0n's framework for Kaggle competitions "kaggletils"
- 28 Jupyter Notebook tips, tricks and shortcuts
- Multicore t-SNE implementation
- Comparison of Manifold Learning methods (sklearn)
- How to Use t-SNE Effectively (distill.pub blog)
- tSNE homepage (Laurens van der Maaten)
- Example: tSNE with different perplexities (sklearn)
- Facebook Research's paper about extracting categorical features from trees
- Example: Feature transformations with ensembles of trees (sklearn)
- Kaggle ensembling guide at MLWave.com (overview of approaches)
- StackNet — a computational, scalable and analytical meta modelling framework (by KazAnova)
- Heamy — a set of useful tools for competitive data science (including ensembling)
You can often find a solution of the competition you're interested on its forum. Here we put links to collections of such solutions that will prove useful to you.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent