Merge remote-tracking branch 'upstream/master'

blogs.md

@@ -8,13 +8,13 @@ Podcasts
 
 [The O'Reilly Data Show](http://radar.oreilly.com/tag/oreilly-data-show-podcast)
 
-[Partially Derivative](http://www.partiallyderivative.com/)
+[Partially Derivative](http://partiallyderivative.com/)
 
 [The Talking Machines](http://www.thetalkingmachines.com/)
 
-[The Data Skeptic](http://dataskeptic.com/)
+[The Data Skeptic](https://dataskeptic.com/)
 
-[Linear Digressions](https://www.udacity.com/podcasts/linear-digressions)
+[Linear Digressions](http://benjaffe.github.io/linear-digressions-site/)
 
 [Data Stories](http://datastori.es/)
 
@@ -22,9 +22,13 @@ Podcasts
 
 [Not So Standard Deviations](http://simplystatistics.org/2015/09/17/not-so-standard-deviations-the-podcast/)
 
+[TWIMLAI](https://twimlai.com/shows/)
+
 Data Science / Statistics
 -------------------------
 
+https://jeremykun.com/
+
 http://iamtrask.github.io/
 
 http://blog.explainmydata.com/
@@ -37,9 +41,9 @@ http://www.evanmiller.org/
 
 http://jakevdp.github.io/
 
-http://blog.yhathq.com/
+http://blog.yhat.com/
 
-http://blog.wesmckinney.com/
+http://wesmckinney.com
 
 http://www.overkillanalytics.net/
 
@@ -47,19 +51,17 @@ http://newton.cx/~peter/
 
 http://mbakker7.github.io/exploratory_computing_with_python/
 
-http://sebastianraschka.com/articles.html
+https://sebastianraschka.com/blog/index.html
 
 http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/
 
 http://colah.github.io/
 
-http://snippyhollow.github.io/
-
 http://www.thomasdimson.com/
 
 http://blog.smellthedata.com/
 
-http://sebastianraschka.com/
+https://sebastianraschka.com/
 
 http://dogdogfish.com/
 
@@ -71,16 +73,14 @@ http://bugra.github.io/
 
 http://opendata.cern.ch/
 
-http://alexanderetz.com/
+https://alexanderetz.com/
 
 http://www.sumsar.net/
 
-http://countbayesie.com
+https://www.countbayesie.com
 
 http://karpathy.github.io/
 
-http://blog.dato.com/
-
 http://blog.kaggle.com/
 
 http://www.danvk.org/
@@ -89,7 +89,7 @@ http://hunch.net/
 
 http://www.randalolson.com/blog/
 
-http://www.johndcook.com/blog/r_language_for_programmers/
+https://www.johndcook.com/blog/r_language_for_programmers/
 
 http://www.dataschool.io/
 
@@ -100,9 +100,9 @@ http://www.sumsar.net/
 
 http://allendowney.blogspot.ca/
 
-http://healthyalgorithms.com/
+https://healthyalgorithms.com/
 
-http://petewarden.com/
+https://petewarden.com/
 
 http://mrtz.org/blog/
 
@@ -110,4 +110,4 @@ http://mrtz.org/blog/
 Security Related
 ----------------
 
-http://jordan-wright.github.io/blog/
+http://jordan-wright.com/blog/

books.md

@@ -2,37 +2,35 @@ The following is a list of free, open source books on machine learning, statisti
 
 ## Machine-Learning / Data Mining
 
+* [Real World Machine Learning](https://www.manning.com/books/real-world-machine-learning) [Free Chapters]
 * [An Introduction To Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) - Book + R Code
 * [Elements of Statistical Learning](http://statweb.stanford.edu/~tibs/ElemStatLearn/) - Book
 * [Probabilistic Programming & Bayesian Methods for Hackers](http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/) - Book + IPython Notebooks
-* [Think Bayes](http://www.greenteapress.com/thinkbayes/) - Book + Python Code
+* [Think Bayes](http://greenteapress.com/wp/think-bayes/) - Book + Python Code
 * [Information Theory, Inference, and Learning Algorithms](http://www.inference.phy.cam.ac.uk/mackay/itila/book.html)
 * [Gaussian Processes for Machine Learning](http://www.gaussianprocess.org/gpml/chapters/)
 * [Data Intensive Text Processing w/ MapReduce](http://lintool.github.io/MapReduceAlgorithms/)
 * [Reinforcement Learning: - An Introduction](http://webdocs.cs.ualberta.ca/~sutton/book/ebook/the-book.html)
 * [Mining Massive Datasets](http://infolab.stanford.edu/~ullman/mmds/book.pdf)
 * [A First Encounter with Machine Learning](https://www.ics.uci.edu/~welling/teaching/273ASpring10/IntroMLBook.pdf)
-* [Pattern Recognition and Machine Learning](http://www.rmki.kfki.hu/~banmi/elte/Bishop%20-%20Pattern%20Recognition%20and%20Machine%20Learning.pdf)
+* [Pattern Recognition and Machine Learning](http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf)
 * [Machine Learning & Bayesian Reasoning](http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/090310.pdf)
 * [Introduction to Machine Learning](http://alex.smola.org/drafts/thebook.pdf) - Alex Smola and S.V.N. Vishwanathan
 * [A Probabilistic Theory of Pattern Recognition](http://www.szit.bme.hu/~gyorfi/pbook.pdf)
 * [Introduction to Information Retrieval](http://nlp.stanford.edu/IR-book/pdf/irbookprint.pdf)
-* [Forecasting: principles and practice](http://otexts.com/fpp/)
-* [Practical Artificial Intelligence Programming in Java](http://www.markwatson.com/opencontent_data/JavaAI3rd.pdf)
-* [Introduction to Machine Learning](http://arxiv.org/pdf/0904.3664v1.pdf) - Amnon Shashua
+* [Forecasting: principles and practice](https://www.otexts.org/fpp/)
+* [Practical Artificial Intelligence Programming in Java](https://www.saylor.org/site/wp-content/uploads/2011/11/CS405-1.1-WATSON.pdf)
+* [Introduction to Machine Learning](https://arxiv.org/pdf/0904.3664v1.pdf) - Amnon Shashua
 * [Reinforcement Learning](http://www.intechopen.com/books/reinforcement_learning)
 * [Machine Learning](http://www.intechopen.com/books/machine_learning)
 * [A Quest for AI](http://ai.stanford.edu/~nilsson/QAI/qai.pdf)
-* [Introduction to Applied Bayesian
-Statistics and Estimation for
-Social Scientists](http://faculty.ksu.edu.sa/69424/us_BOOk/Introduction%20to%20Applied%20Bayesian%20Statistics.pdf)
-* [Bayesian Modeling, Inference
-and Prediction](http://users.soe.ucsc.edu/~draper/draper-BMIP-dec2005.pdf)
+* [Introduction to Applied Bayesian Statistics and Estimation for Social Scientists](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.177.857&rep=rep1&type=pdf) - Scott M. Lynch
+* [Bayesian Modeling, Inference and Prediction](https://users.soe.ucsc.edu/~draper/draper-BMIP-dec2005.pdf)
 * [A Course in Machine Learning](http://ciml.info/)
 * [Machine Learning, Neural and Statistical Classification](http://www1.maths.leeds.ac.uk/~charles/statlog/)
 * [Bayesian Reasoning and Machine Learning](http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.HomePage) Book+MatlabToolBox
 * [R Programming for Data Science](https://leanpub.com/rprogramming)
-* [Data Mining - Practical Machine Learning Tools and Techniques](http://www.cse.hcmut.edu.vn/~chauvtn/data_mining/Texts/%5B7%5D%20Data%20Mining%20-%20Practical%20Machine%20Learning%20Tools%20and%20Techniques%20%283rd%20Ed%29.pdf) Book
+* [Data Mining - Practical Machine Learning Tools and Techniques](http://cs.du.edu/~mitchell/mario_books/Data_Mining:_Practical_Machine_Learning_Tools_and_Techniques_-_2e_-_Witten_&_Frank.pdf) Book
 
 ## Deep-Learning
 
@@ -52,21 +50,21 @@ and Prediction](http://users.soe.ucsc.edu/~draper/draper-BMIP-dec2005.pdf)
 ## Neural Networks
 
 * [A Brief Introduction to Neural Networks](http://www.dkriesel.com/_media/science/neuronalenetze-en-zeta2-2col-dkrieselcom.pdf)
+* [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/)
 
 ## Probability & Statistics
 
 * [Think Stats](http://www.greenteapress.com/thinkstats/) - Book + Python Code
 * [From Algorithms to Z-Scores](http://heather.cs.ucdavis.edu/probstatbook) - Book
 * [The Art of R Programming](http://heather.cs.ucdavis.edu/~matloff/132/NSPpart.pdf) - Book (Not Finished)
-* [All of Statistics](http://www.ucl.ac.uk/~rmjbale/Stat/wasserman2.pdf)
-* [Introduction to statistical thought](https://www.math.umass.edu/~lavine/Book/book.pdf)
+* [Introduction to statistical thought](http://people.math.umass.edu/~lavine/Book/book.pdf)
 * [Basic Probability Theory](http://www.math.uiuc.edu/~r-ash/BPT/BPT.pdf)
-* [Introduction to probability](http://math.dartmouth.edu/~prob/prob/prob.pdf) - By Dartmouth College
+* [Introduction to probability](https://math.dartmouth.edu/~prob/prob/prob.pdf) - By Dartmouth College
 * [Principle of Uncertainty](http://uncertainty.stat.cmu.edu/wp-content/uploads/2011/05/principles-of-uncertainty.pdf)
-* [Probability & Statistics Cookbook](http://matthias.vallentin.net/probability-and-statistics-cookbook/)
+* [Probability & Statistics Cookbook](http://statistics.zone/)
 * [Advanced Data Analysis From An Elementary Point of View](http://www.stat.cmu.edu/~cshalizi/ADAfaEPoV/ADAfaEPoV.pdf)
 * [Introduction to Probability](http://athenasc.com/probbook.html) -  Book and course by MIT
-* [The Elements of Statistical Learning: Data Mining, Inference, and Prediction.](http://statweb.stanford.edu/~tibs/ElemStatLearn/) -Book  
+* [The Elements of Statistical Learning: Data Mining, Inference, and Prediction.](http://statweb.stanford.edu/~tibs/ElemStatLearn/) -Book
 * [An Introduction to Statistical Learning with Applications in R](http://www-bcf.usc.edu/~gareth/ISL/) - Book
 * [Learning Statistics Using R](http://health.adelaide.edu.au/psychology/ccs/teaching/lsr/)
 * [Introduction to Probability and Statistics Using R](https://cran.r-project.org/web/packages/IPSUR/vignettes/IPSUR.pdf) - Book
@@ -79,6 +77,6 @@ and Prediction](http://users.soe.ucsc.edu/~draper/draper-BMIP-dec2005.pdf)
 
 * [Linear Algebra Done Wrong](http://www.math.brown.edu/~treil/papers/LADW/book.pdf)
 * [Linear Algebra, Theory, and Applications](https://math.byu.edu/~klkuttle/Linearalgebra.pdf)
-* [Convex Optimization](http://www.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf)
+* [Convex Optimization](http://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf)
 * [Applied Numerical Computing](http://www.seas.ucla.edu/~vandenbe/103/reader.pdf)
-* [Applied Numerical Linear Algebra](http://uqu.edu.sa/files2/tiny_mce/plugins/filemanager/files/4281667/hamdy/hamdy1/cgfvnv/hamdy2/h1/h2/h3/h4/h5/h6/Applied%20Numerical%20Linear%20.pdf)
+* [Applied Numerical Linear Algebra](http://egrcc.github.io/docs/math/applied-numerical-linear-algebra.pdf)

courses.md

@@ -0,0 +1,11 @@
+The following is a list of free or paid online courses on machine learning, statistics, data-mining, etc.
+
+## Machine-Learning / Data Mining
+
+* [Artificial Intelligence (Columbia University)](https://www.edx.org/course/artificial-intelligence-ai-columbiax-csmm-101x) - free
+* [Machine Learning (Columbia University)](https://www.edx.org/course/machine-learning-columbiax-csmm-102x) - free
+* [Machine Learning (Stanford University)](https://www.coursera.org/learn/machine-learning) - free
+* [Neural Networks for Machine Learning (University of Toronto)](https://www.coursera.org/learn/neural-networks) - free
+* [Machine Learning Specialization (University of Washington)](https://www.coursera.org/specializations/machine-learning) - Courses: Machine Learning Foundations: A Case Study Approach, Machine Learning: Regression, Machine Learning: Classification, Machine Learning: Clustering & Retrieval, Machine Learning: Recommender Systems & Dimensionality Reduction,Machine Learning Capstone: An Intelligent Application with Deep Learning; free
+* [Machine Learning Course (2014-15 session) (by Nando de Freitas, University of Oxford)](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/) - Lecture slides and video recordings.
+* [Learning from Data (by Yaser S. Abu-Mostafa, Caltech)](http://www.work.caltech.edu/telecourse.html) - Lecture videos available

meetups.md

@@ -0,0 +1,11 @@
+The following is a list of free-to-attend meetups and local events on Machine Learning
+
+- [India](#india)
+    -   [Bangalore](#bangalore)
+
+<a name="india"></a>
+## India
+
+<a name="bangalore"></a>
+### Bangalore
+* [Bangalore Machine Learning Meetup (BangML)](https://www.meetup.com/BangML/)