TO ADAPT EASTERN PROVERB THE TRUMP CANT CHANGE UNTIL THE CITIZENS CHANGE
100 days of creative destruction i never expected as a citizen of Baltimore-DC ::monthly rachel alumni open learning reports 1 .... could the next 1361 days be the most exciting in the history of humanity as well as transforming wash dc 240 316 8157 - #theEconomist first 40 years archived http://project30000.blogspot.com
INDUSTRIAL REVOLUTION 4
Reason for optimism is leapfrogging - thats when a society/place that was excluded from industrial age networks leapfrogs an old system to a new one; about a third of the world never had wired telephone lines, now almost all have mobile (text version); more than a quarter of the world never had electricity grids, now microsolar is linking in;. Prior to 2017 only Jim Kim open spaced this debated in DC: let's hope all parents and youth do now from usa to china to Rome, from Scotland to Argentina, from Bangalore to Haiti. from . G1 G2. Join Valuetrue.com and QBG -does your place have a JYK to celebrate global youth? futures of Liberty 1 & education 1
1:08 #2030now 3.19
0:39 0.31 1:40 1:02 1.21
...joy jk search 1........ co
Which is your top 100 jim kim video vote for end-poverty tedx wcg..Jim Kim2030nowjimkim2transcripts.doc2030nowjimkim.doc, where world demands women manage poverty why not development?
http://www.tedxwbg.com/ Sources for millennials Happy 2015 dialogues of pih on 1 Ebola 2 how to leverage technology to radically engage patients on health care; UN is 2015 year of all change to sustainability goals... support economistmooc.blogspot.com

Thursday, July 19, 2012

example of a complete coursera course - while many courses need to be signed up for live some are also on permanent preview - isn't the style cool?- video instruction with occasional questions pausing video until you answer

Machine Learning

Andrew Ng, Associate Professor

Learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.


Video Lectures


I. Introduction (Week 1)



II. Linear Regression with One Variable (Week 1)



III. Linear Algebra Review (Week 1, Optional)



IV. Linear Regression with Multiple Variables (Week 2)



V. Octave Tutorial (Week 2)



VI. Logistic Regression (Week 3)



VII. Regularization (Week 3)



VIII. Neural Networks: Representation (Week 4)



IX. Neural Networks: Learning (Week 5)



X. Advice for Applying Machine Learning (Week 6)



XI. Machine Learning System Design (Week 6)



XII. Support Vector Machines (Week 7)



XIII. Clustering (Week 8)



XIV. Dimensionality Reduction (Week 8)



XV. Anomaly Detection (Week 9)



XVI. Recommender Systems (Week 9)



XVII. Large Scale Machine Learning (Week 10)



XVIII. Application Example: Photo OCR



XIX. Conclusion


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