Personal Information
Entreprise/Lieu de travail
Stockholm, Sweden Sweden
Profession
Machine Learning Engineer / Data Scientist
Secteur d’activité
Finance / Banking / Insurance
Site Web
http://eyad.ninja
À propos
Artificial Intelligence, Data Mining, Scientific Applications, Databases, Data Analysis, Statistical Analysis Pythonista, Big Data, Data Science, Machine Learning, Deep Learning
- Présentations
- Documents
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What's Next: Social Media Trends 2020
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Scala Data Pipelines for Music Recommendations
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10 more lessons learned from building Machine Learning systems
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Everyday I'm Shuffling - Tips for Writing Better Spark Programs, Strata San Jose 2015
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Intro To MongoDB
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Kaggle Winning Solution Xgboost algorithm -- Let us learn from its author
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word2vec, LDA, and introducing a new hybrid algorithm: lda2vec
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Feature Engineering
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Building a real time, solr-powered recommendation engine
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Tips for data science competitions
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10 Lessons Learned from Building Machine Learning Systems
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Open Source Tools & Data Science Competitions
odsc
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General Tips for participating Kaggle Competitions
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Statistical Machine Learning for Text Classification with scikit-learn and NLTK
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Understanding Random Forests: From Theory to Practice
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Lesson 35: Game Theory and Linear Programming
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How to become a data scientist in 6 months
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Kaggle Otto Challenge: How we achieved 85th out of 3,514 and what we learnt
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il y a 8 ans
Deep Learning for Recommender Systems
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il y a 6 ans
Tips and tricks to win kaggle data science competitions
Darius Barušauskas
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il y a 7 ans