README.md

#Machine learning resources: This repository holds the list of learning material that we are finding useful within the activities of the Sup'Com machine learning group. The resources range over MOOCs, blog articles, books, videos, papers, etc.

###Beginner - Machine learning:

• Udacity course: Intro to machine learning.

  • Link: https://www.udacity.com/course/intro-to-machine-learning--ud120
  • Description: presents an easy introduction to machine learning with hands on implementation using sklearn.

• Top 10 Data mining algorithms explained

  • Link: http://www.kdnuggets.com/2015/05/top-10-data-mining-algorithms-explained.html
  • Description: presents a non technical, easy to grasp, explanation of the top 10 data mining algorithms.

###Begineer - Data analysis

• 5 Data cleansing tools:
  • Link: http://www.datasciencecentral.com/profiles/blogs/5-data-cleansing-tools
  • Description: how to clean the data before perform analytics.
• What questions can data science answer:
  • Link: http://www.kdnuggets.com/2016/01/questions-data-science-answer.html
  • Description: simple formulation of the questions that data science can answer.

Practical - Data science/Machine learning tutorials

• Predicting Heart Disease With Hadoop, Spark and Python
  • Link: http://nasdag.github.io/blog/2016/01/02/predicting-heart-disease-with-hadoop-spark-and-python/
• Recognizing and Localizing Endangered Right Whales with Extremely Deep Neural Networks
  • Link: http://felixlaumon.github.io/2015/01/08/kaggle-right-whale.html
  • Description: Kaggle competition write up for second place model
• How We Approached The Allen A.I. Challenge on Kaggle
  • Link: http://blog.talla.com/2016/01/how-we-approached-the-allen-a-i-challenge-on-kaggle/
  • Description: kaggle competition write up

###Advanced - Deep learning:

• Deep learning simplified:
  • Link: https://www.youtube.com/channel/UC9OeZkIwhzfv-_Cb7fCikLQ/playlists:
  • Description: an easy to grasp simplification of deep learning concepts.
• Deep learning Demistified:
  • Link: https://www.youtube.com/watch?v=GlcnxUlrtek&list=PLK1aXp7PZ2tMrm1h_JDBcJDhIpbQZPOr1
  • Description: a tutorial series trying to demystify Deep learning concepts
• Deep learning in Nutshell: History and training
  • Link: http://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-history-training/
  • Description: Nvidia blog tutorial explaining deep learning
• Theoretical motivations for Deep learning
  • Link http://videolectures.net/deeplearning2015_bengio_theoretical_motivations/
• MIT press book on Deep learning (under preparation)
  • Link https://goodfeli.github.io/dlbook/
• Evaluation of Deep learning toolkits:
  • Link https://github.com/zer0n/deepframeworks/blob/master/README.md
• Yelp: How we use deep learning to classify photos:
  • Link: http://engineeringblog.yelp.com/2015/10/how-we-use-deep-learning-to-classify-business-photos-at-yelp.html
  • Description: Application of Deep learning for image classification.
• Using Convolutional Neural Networks (CNN) for Image Recognition
  • Link: http://ip.cadence.com/uploads/901/cnn_wp-pdf
  • Description: a Tutorial from Cadence systems on how to use CNN for traffic signs recognition

###Recommended readings:

• The Master Algorithm: "How the quest for the ultimate learning machine will remake our world" - Author, Pedro Domingos.

###Other ML learning lists:

• http://machinelearningmastery.com/best-machine-learning-resources-for-getting-started/