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mlcourse.ai. Outro

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Some directions to choose when you have covered basics of Machine Learning:
- deep learning (cs231n)
- theory of ML
- more practice, Kaggle
- first job in Data Science
- pet project
Some words on the fall 2018 session of mlcourse.ai

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mlcourse.ai. Outro

  1. 1. mlcourse.ai Further steps when basic ML is covered
  2. 2. How it all started, spring 2017 520 started with A1 120 finished A10
  3. 3. Fall 2018 2200 started with A1 ~400 finished A10
  4. 4. What next? 1. New session - Feb 2019 More competitions! 2. #class_cs231n – from 24.12.18, Slack «Convolutional Neural Networks for Visual Recognition» Andrej Karpathy Fei-Fei Li Stanford
  5. 5. 3. Dive into theory of ML -> #edu_books -> #article_essence 4. More practice -> #kaggle_crackers -> #mlcourse_ai «Pattern Recognition & Machine Learning» (C. Bishop) «Deep Learning» (I. Goodfellow et.al.) 5. First job in DS –> #career What next?
  6. 6. What next? 6. Pet project https://digits-draw-recognize.herokuapp.com/
  7. 7. What next? 7. Support mlcourse.ai https://www.patreon.com/ods_mlcourse https://ko-fi.com/mlcourse_ai (monthly) (one-time) Tell your story and spread a word! Kaggle Forum
  8. 8. See you in #class_cs231n! Thanks to mlcourse.ai team!

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