Mining Massive Datasets

  • Stanford University | Coursera
  • Topics include MapReduce, Web-link analysis, Data-streams, Locality-sensitive hashing, Computational advertising, Clustering, Recommender systems, Analysis of large graphs, Decision trees, Dimensionality reduction, Support-vector machines, and Frequent-itemset analysis.

Machine Learning, by Andrew Ng

  • Stanford University
  • This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition.

Natural Language Processing, by Dan Jurafsky and Chris Manning

  • Stanford University | Coursera
  • This course covers a broad range of topics in natural language processing, including word and sentence tokenization, text classification and sentiment analysis, spelling correction, information extraction, parsing, meaning extraction, and question answering..

Neural Networks for Machine Learning, by Geoffrey Hinton

  • University of Toronto | Coursera
  • Covered learning techniques for many different types of neural network including deep feed-forward networks, recurrent networks and Boltzmann Machines. It covered recent applications to speech, vision, and language, and used hands-on programming assignments.

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