/Last-Minute-Notes-of-Machine-learning-and-Deep-learning

Last Minute Note of Machine learning and Deep learning by Jason Brownlee

Last Minute Notes of Machine learning and Deep learning By Jason Brownlee

All Article Source : https://machinelearningmastery.com

  1. Mini Course of Machine learning

  2. Crash Course in Python for Machine Learning Developers

  3. Statistics for Machine Learning

  4. Linear Algebra for Machine Learning

  5. How to Think About Machine Learning

  6. How to Get Better Deep Learning Results

  7. Python Machine Learning Mini-Course

  8. Crash Course in Recurrent Neural Networks for Deep Learning

  9. Crash Course in Convolutional Neural Networks for Machine Learning

  10. Crash Course On Multi-Layer Perceptron Neural Networks

  11. Super Fast Crash Course in R (for developers)

  12. How to Get Started With Deep Learning for Computer Vision

  13. How to Get Started with Deep Learning for Time Series Forecasting (7-Day Mini-Course)

  14. How to Get Started with Deep Learning for Natural Language Processing (7-Day Mini-Course)

  15. Mini-Course on Long Short-Term Memory Recurrent Neural Networks with Keras

  16. Applied Deep Learning in Python Mini-Course

  17. CNN Long Short-Term Memory Networks

  18. Common Pitfalls In Machine Learning Projects

  19. Practical Deep Learning for Coders (Review)

Large Scale Machine Learning Courses (OR) Machine learning Courses for Large Dataset

  1. Large Scale Learning (EECS6898,Columbia, 2010): http://www.sanjivk.com/EECS6898/lectures.html
  2. Large Scale Learning (CMSC 3590, U of Chicago, 2009): http://ttic.uchicago.edu/~gregory/courses/LargeScaleLearning/
  3. Models of Computation for Massive Data (CS7960, U of Utah, 2010) : http://www.cs.utah.edu/~jeffp/teaching/cs7960.html
  4. Parallel Distributed Processing (85-419, CMU, 2010): http://www.cnbc.cmu.edu/~plaut/IntroPDP/index.html
  5. Machine Learning ( COMS4771, Columbia, 2008): http://hunch.net/~coms-4771/lectures.html
  6. Machine Learning (CS4780, Cornell, 2009): http://www.cs.cornell.edu/Courses/cs4780/2009fa/
  7. Machine Learning (10-701, CMU, 2011): http://www.cs.cmu.edu/~awm/10701/
  8. Machine Learning (CS590, Purdue, 2010): http://www.stat.purdue.edu/~vishy/introml/introml.html
  9. Advanced Machine Learning (CS253, Caltech, 2010): http://www.cs.caltech.edu/courses/cs253
  10. Advanced Machine Learning (COMS6772, Columbia, 2010): http://www.cs.columbia.edu/~jebara/6772/solutions.html
  11. Advanced Machine Learning (CS6784, Cornell, 2010): http://www.cs.cornell.edu/Courses/cs6784/2010sp/
  12. Advanced Machine Learning (CSC2535, U of Toronto, 2010): http://www.cs.toronto.edu/~hinton/csc2535/lectures.html
  13. Statistical Learning Theory (9.520, MIT, 2011): http://www.mit.edu/~9.520/
  14. Computational Learning Theory (Comp 150AML, Tufts, 2008): http://www.cs.tufts.edu/~roni/Teaching/CLT/
  15. Large-Scale Simultaneous Inference (Stats 329, Stanford, 2010): http://www-stat.stanford.edu/~omkar/329/
  16. Inference, Estimation and Information Processing (EE378, Stanford, 2011): http://www.stanford.edu/class/ee378/reading.html
  17. Statistical Signal Processing B (EE378B, Stanford, 2011): http://www.stanford.edu/class/ee378B/refs.html
  18. Statistical Machine Learning (Domke, RIT, 2011): http://people.rit.edu/jcdicsa/courses/SML/
  19. Unsupervised Learning (CSE291, UCSD, 2011): http://cseweb.ucsd.edu/classes/sp11/cse291-d/#syllabus
  20. Adaptive Neural Networks (EE373B, Stanford, 2009): http://www.stanford.edu/class/ee373b/
  21. Optimization (10725, CMU, 2010): http://select.cs.cmu.edu/class/10725-S10/schedule.html
  22. Convex Optimization I (EE364A, Stanford, 2011): http://www.stanford.edu/class/ee364a/
  23. Convex Optimization II (EE364B, Stanford, 2011): http://www.stanford.edu/class/ee364b
  24. Dealing with Massive Data (COMS6998,Columbia, 2010): http://www.cs.columbia.edu/~coms699812/
  25. Algorithms for Massive Data Sets (CS369, Stanford, 2009):http://www.stanford.edu/class/cs369m/
  26. Algorithms for Massive Data Sets (CS493, Princeton, 2002): http://www.cs.princeton.edu/courses/archive/spring02/cs493/schedule.html
  27. Algorithms for Massive Data Sets (Gørtz, Witt & Bille, DTU, 2011): https://massivedatasets.wordpress.com/
  28. Data Mining: Learning from Large Data Sets (Krause, ETH, 2011): http://las.ethz.ch/courses/datamining-s11/
  29. From Languages to Information (CS124, Stanford, 2011):http://www.stanford.edu/class/cs124/
  30. Data-Intensive Information Processing Applications (Lin, UMD, 2010): http://www.umiacs.umd.edu/~jimmylin/cloud-2010-Spring/syllabus.html
  31. Advanced Algorithm Design (CS521, Princeton, 2006): http://www.cs.princeton.edu/courses/archive/fall06/cos521/
  32. Approximation algorithms (CS598, UIUC, 2011): http://www.cs.illinois.edu/class/sp11/cs598csc/
  33. Data Stream Algorithms (Muthukrishnan, 2009): http://www.cs.mcgill.ca/~denis/notes09.pdf
  34. Information Theory (EE376, Stanford, 2011): http://classx.stanford.edu/View/Subject.php?SubjectID=2011_Q1_EE376_Lec
  35. Lectures on Statistical Modeling Theory (Rissanen): http://www.mdl-research.org/pub/lectures.pdf
  36. Multimedia Databases and Data Mining (15-826, CMU, 2010): http://www.cs.cmu.edu/~christos/courses/826.S10/schedule.html
  37. Distributed Systems (CS525, UIUC, 2011): http://www.cs.uiuc.edu/class/sp11/cs525/sched.htm
  38. Distributed Systems (6.824, MIT, 2011): http://pdos.csail.mit.edu/6.824/schedule.html
  39. Distributed Systems Courses: http://the-paper-trail.org/blog/?page_id=152
  40. Parallel Computing Courses: http://www.cs.rit.edu/~ncs/parallel.html#courses
  41. Applications of Parallel computers (CS267, U.C. Berkeley, 2011): http://www.cs.berkeley.edu/~demmel/cs267_Spr11/
  42. Programming Massively Parallel Processors with CUDA (CS193G, Stanford) : http://itunes.apple.com/itunes-u/programming-massively-parallel/id384233322#ls=1
  43. Parallel Algorithms (15-499, CMU, 2009): http://www.cs.cmu.edu/afs/cs/academic/class/15499-s09/www/
  44. Advanced Methods in Matrix Computations: Iterative Methods (CS 336, Stanford, 2006): http://www.stanford.edu/class/cme324/
  45. Parallel Numerical Algorithms (CS 554, UIUC, 2008): http://www.cse.illinois.edu/courses/cs554/notes/index.html
  46. Scientific Computing for Engineers (CS 594, UTK, 2011): http://web.eecs.utk.edu/~dongarra/WEB-PAGES/cs594-2008.htm
  47. Algorithms in the "Real World" (15-853, CMU, 2010): http://www.cs.cmu.edu/afs/cs/project/pscico-guyb/realworld/www/
  48. Sublinear Algorithms (6.896, MIT, 2010): http://stellar.mit.edu/S/course/6/fa10/6.896/materials.html
  49. Large-Scale Simultaneous Inference (Stats 329, Stanford, 2010): http://www-stat.stanford.edu/~omkar/329/
  50. Communication-Avoiding Algorithms (CS294, Berkeley, 2011): http://www.cs.berkeley.edu/~odedsc/CS294/
  51. Modeling Data with Uncertainty (Seminar, U of Utah, 2010): http://www.cs.utah.edu/~suresh/mediawiki/index.php/Algorithms_Seminar/Fall10#Schedule