https://github.com/ashishpatel26/last-minute-notes-of-machine-learning-and-deep-learning

Last Minute Note of Machine learning and Deep learning by Jason Brownlee
https://github.com/ashishpatel26/last-minute-notes-of-machine-learning-and-deep-learning

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

• Host: GitHub
• URL: https://github.com/ashishpatel26/last-minute-notes-of-machine-learning-and-deep-learning
• Owner: ashishpatel26
• Created: 2019-06-20T13:11:54.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2019-08-06T06:46:01.000Z (almost 6 years ago)
• Last Synced: 2025-01-20T05:11:07.383Z (5 months ago)
• Size: 4.88 KB
• Stars: 86
• Watchers: 2
• Forks: 59
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

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

**All Article Source** : [**https://machinelearningmastery.com**](https://machinelearningmastery.com/)

1. [**Mini Course of Machine learning**](https://machinelearningmastery.com/machine-learning-algorithms-mini-course/)

2. [**Crash Course in Python for Machine Learning Developers**](https://machinelearningmastery.com/crash-course-python-machine-learning-developers/)

3. **[Statistics for Machine Learning](https://machinelearningmastery.com/statistics-for-machine-learning-mini-course/)** 

4. **[Linear Algebra for Machine Learning](https://machinelearningmastery.com/linear-algebra-machine-learning-7-day-mini-course/)**

5. **[How to Think About Machine Learning](https://machinelearningmastery.com/think-machine-learning/)**

6. [**How to Get Better Deep Learning Results**](https://machinelearningmastery.com/better-deep-learning-neural-networks-crash-course/)

7. **[Python Machine Learning Mini-Course](https://machinelearningmastery.com/python-machine-learning-mini-course/)**

8. **[Crash Course in Recurrent Neural Networks for Deep Learning](https://machinelearningmastery.com/crash-course-recurrent-neural-networks-deep-learning/)**

9. **[Crash Course in Convolutional Neural Networks for Machine Learning](https://machinelearningmastery.com/crash-course-convolutional-neural-networks/)**

10. **[Crash Course On Multi-Layer Perceptron Neural Networks](https://machinelearningmastery.com/neural-networks-crash-course/)**

11. [**Super Fast Crash Course in R (for developers)**](https://machinelearningmastery.com/r-crash-course-for-developers/)

12. **[How to Get Started With Deep Learning for Computer Vision](https://machinelearningmastery.com/how-to-get-started-with-deep-learning-for-computer-vision-7-day-mini-course/)**

13. **[How to Get Started with Deep Learning for Time Series Forecasting (7-Day Mini-Course)](https://machinelearningmastery.com/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)](https://machinelearningmastery.com/crash-course-deep-learning-natural-language-processing/)**

15. **[Mini-Course on Long Short-Term Memory Recurrent Neural Networks with Keras](https://machinelearningmastery.com/long-short-term-memory-recurrent-neural-networks-mini-course/)**

16. **[Applied Deep Learning in Python Mini-Course](https://machinelearningmastery.com/applied-deep-learning-in-python-mini-course/)**

17. **[CNN Long Short-Term Memory Networks](https://machinelearningmastery.com/cnn-long-short-term-memory-networks/)**

18. **[Common Pitfalls In Machine Learning Projects](https://machinelearningmastery.com/common-pitfalls-machine-learning-projects/)**

19. **[Practical Deep Learning for Coders (Review)](https://machinelearningmastery.com/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