updated links + CS229

README.md

@@ -62,7 +62,7 @@ Out of personal preference and need for focus, I geared the original curriculum
 (http://www.quora.com/What-are-some-good-resources-for-learning-about-numerical-analysis)
 
 * **Linear Algebra & Programming**
- * Linear Algebra [Khan Academy / Videos](https://www.khanacademy.org/math/linear-algebra)
+ * Linear Algebra [Khan Academy / Videos](http://bit.ly/khanlinalg)
  * Linear Algebra / Levandosky [Stanford / Book ```$10```](http://amzn.to/1kIfmmI)
  * Linear Programming (Math 407) [University of Washington / Course](http://bit.ly/course-uw-linearprogramming)
  * The Manga Guide to Linear Algebra [Book ```$19```](http://amzn.to/1n4hM5l)
@@ -81,7 +81,7 @@ Out of personal preference and need for focus, I geared the original curriculum
 
 ### Computing
 
-Get your environment up and running with the [Data Science Toolbox](http://datasciencetoolbox.org)
+Get your environment up and running with the [Data Science Toolbox](http://bit.ly/datascitoolbox)
 
 * **Algorithms**
  * Algorithms Design & Analysis I [Stanford / Coursera](http://bit.ly/coursera-algo)
@@ -93,7 +93,7 @@ Get your environment up and running with the [Data Science Toolbox](http://datas
 
 * **Databases**
  * Introduction to Databases [Stanford / Online Course](https://bit.ly/introdatabases)
- * SQL School [Mode Analytics / Tutorials](http://sqlschool.modeanalytics.com/)
+ * SQL School [Mode Analytics / Tutorials](http://bit.ly/modesqlschool)
  * SQL Tutorials [SQLZOO / Tutorials](http://bit.ly/tut-sqlzoo)
 
 * **Data Mining**
@@ -107,36 +107,37 @@ _OSDSM Specialization: [Web Scraping & Crawling](https://github.com/datasciencem
 
  _Foundational & Theoretical_
  * Machine Learning [Ng Stanford / Coursera](http://bit.ly/stanford-ml)
- * A Course in Machine Learning [UMD / Digital Book](http://ciml.info/)
+ * Machine Learning [Stanford CS 229](http://bit.ly/stanfordcs229)
+ * A Course in Machine Learning [UMD / Digital Book](http://bit.ly/22WyV3N)
  * The Elements of Statistical Learning / Stanford [Digital](http://bit.ly/ebook-elemstatlearn) & [Book ```$80```](http://amzn.to/1hmyKry) & [Study Group](http://www.reddit.com/r/eosl)
  * Machine Learning [Caltech / Edx](http://bit.ly/caltech-ml)
 
  _Practical_
  * Programming Collective Intelligence [Book ```$27```](http://amzn.to/1mqxYqW)
- * Machine Learning for Hackers [ipynb / digital book](http://nbviewer.ipython.org/github/carljv/Will_it_Python/blob/master/MLFH/CH1/chapter1.ipynb)
+ * Machine Learning for Hackers [ipynb / digital book](http://bit.ly/mlforhackers)
  * Intro to scikit-learn, SciPy2013 [youtube tutorials](http://bit.ly/scikit-video-tuts)
 
 * **Probabilistic Modeling**
  * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](http://bit.ly/ipnb-probabilisticprogramming)
  * Probabilistic Graphical Models [Stanford / Coursera](http://bit.ly/stanford-pgm)
 
 * **Deep Learning (Neural Networks)**
- * Neural Networks [Andrej Karpathy / Python Walkthrough](http://karpathy.github.io/neuralnets/)
+ * Neural Networks [Andrej Karpathy / Python Walkthrough](http://bit.ly/karpathyneuralnets)
  * Neural Networks [U Toronto / Coursera](http://bit.ly/utoronto-neuralnets)
 
 * **Social Network & Graph Analysis**
  * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](http://bit.ly/stanford-socialeconnetworks)
  * Social Network Analysis for Startups [Book ```$22```](http://amzn.to/1jySCCT)
 
 * **Natural Language Processing**
- * From Languages to Information / Stanford CS147 [Materials](http://web.stanford.edu/class/cs124/)
+ * From Languages to Information / Stanford CS147 [Materials](http://bit.ly/nlpcs124)
  * NLP with Python (NLTK library) [Digital](http://bit.ly/ebook-nltk), [Book ```$36```](http://amzn.to/1iMrDIp)
 
 * **Analysis**
  * Python for Data Analysis [Book ```$24```](http://amzn.to/Q2pI5I)
  * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://bit.ly/cal-course-bigdatatwitter)
  * Exploratory Data Analysis [Tukey / Book ```$81```](http://amzn.to/1kNUEPa)
- * An Example Data Science Process [ipynb](http://nbviewer.ipython.org/github/Jay-Oh-eN/happy-healthy-hungry/blob/master/h3.ipynb)
+ * An Example Data Science Process [ipynb](http://bit.ly/ipydsprocess)
 
 ### Data Design
 
@@ -174,7 +175,7 @@ Installing Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPyt
 
 [Command Line Install Script](https://github.com/fonnesbeck/ScipySuperpack) for Scientific Python Packages
 
- * [Pandas Cookbook](https://github.com/jvns/pandas-cookbook) (data structure library)
+ * [Pandas Cookbook](http://bit.ly/jvnspandascookbook) (data structure library)
 
 _More Libraries can be found in the ["awesome machine learning"](https://github.com/josephmisiti/awesome-machine-learning#python) repo & in related [specializations](https://github.com/datasciencemasters/go/blob/master/specializations.md)_
 
@@ -200,12 +201,12 @@ _More Libraries can be found in the ["awesome machine learning"](https://github.
    * [twython](http://bit.ly/py-twython) - Python wrapper for the Twitter API 
 
  * **Visualization Packages**
-   * [matplotlib](http://www.ast.uct.ac.za/~sarblyth/pythonGuide/PythonPlottingBeginnersGuide.pdf) - well-integrated with analysis and data manipulation packages like numpy and pandas
+   * [matplotlib](http://bit.ly/matplotlib-docs) - well-integrated with analysis and data manipulation packages like numpy and pandas
    * [Orange](http://bit.ly/software-orangeviz) - Open source data visualization and analysis for novice and experts. Data mining through visual programming or Python scripting. Components for machine learning. Add-ons for bioinformatics and text mining
 
 * **iPython Data Science Notebooks**
  * [Data Science in IPython Notebooks](http://bit.ly/ipynb-ds) (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)
- * [A Gallery of Interesting IPython Notebooks - Pandas for Data Analysis](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks#pandas-for-data-analysis)
+ * [A Gallery of Interesting IPython Notebooks - Pandas for Data Analysis](http://bit.ly/ipyfordataanalysis)
 
 #### Datasets are now [here](http://bit.ly/osdsm-datasets)
 
@@ -249,10 +250,6 @@ Non-Open-Source books, courses, and resources are noted with ```$```.
 
 ## Contribute
 
-Please Contribute Your Ideas -- **this is Open Source!**
-
-Please **showcase your own specialization & transcript** by submitting a markdown file pull request in the ```/transcripts``` directory with your name! eg [```clare-corthell-2014.md```](http://bit.ly/U6yVMU)
+Please Contribute -- **this is Open Source!**
 
 [Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)
-
-Email me at [[email protected]](mailto:[email protected])