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legolas140
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Description

How to Win a Data Science Competition: Learn from Top Kagglers

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Additional Materials and Links

Week 1

Recap of main ML algorithms

Overview of methods

Additional Tools

Software/Hardware requirements

StandCloud Computing:

AWS spot option:

Stack and packages:

Feature preprocessing and generation with respect to models

Feature preprocessing

Feature generation

Feature extraction from text and images

Feature extraction from text

Bag of words * Feature extraction from text with Sklearn * More examples of using Sklearn

Word2vec * Tutorial to Word2vec * Tutorial to word2vec usage * Text Classification With Word2Vec * Introduction to Word Embedding Models with Word2Vec

NLP Libraries * NLTK * TextBlob

Feature extraction from images

Pretrained models * Using pretrained models in Keras * Image classification with a pre-trained deep neural network

Finetuning * How to Retrain Inception's Final Layer for New Categories in Tensorflow * Fine-tuning Deep Learning Models in Keras

Week 2

Exploratory data analysis

Visualization tools

Others

Validation

Data leakages

Week 3

Metrics optimization

Classification

Ranking

Clustering

Week 4

Hyperparameter tuning

Tips and tricks

Advanced features II

Matrix Factorization:

t-SNE:

Interactions:

Ensembling

Week 5

Competitions go through

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.

Past solutions

  • http://ndres.me/kaggle-past-solutions/
  • https://www.kaggle.com/wiki/PastSolutions
  • http://www.chioka.in/kaggle-competition-solutions/
  • https://github.com/ShuaiW/kaggle-classification/

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