Minor changes to README.md's grammar

I've corrected a few comma errors and properly formatted some nouns (e.g., correcting "Scipy" to "SciPy" or italicizing a television show name), thus making the text impervious to assaults from future grammarians.

Author: ajduberstein

README.md

@@ -32,13 +32,13 @@ Interactive notebooks + examples can be downloaded by cloning! )
 * [**Prologue:**](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Prologue/Prologue.ipynb) Why we do it.
 
 * [**Chapter 1: Introduction to Bayesian Methods**](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter1_Introduction/Chapter1_Introduction.ipynb)
-    Introduction to the philosophy and practice of Bayesian methods and answering the question "What is probabilistic programming?" Examples include:
-    - Inferring human behaviour changes from text message rates.
+    Introduction to the philosophy and practice of Bayesian methods and answering the question, "What is probabilistic programming?" Examples include:
+    - Inferring human behaviour changes from text message rates
 
 * [**Chapter 2: A little more on PyMC**](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter2_MorePyMC/MorePyMC.ipynb)
     We explore modeling Bayesian problems using Python's PyMC library through examples. How do we create Bayesian models? Examples include:
-    - Detecting the frequency of cheating students, while avoiding liars.
-    - Calculating probabilities of the Challenger space-shuttle disaster.
+    - Detecting the frequency of cheating students, while avoiding liars
+    - Calculating probabilities of the Challenger space-shuttle disaster
 
 * [**Chapter 3: Opening the Black Box of MCMC**](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter3_MCMC/IntroMCMC.ipynb)
     We discuss how MCMC operates and diagnostic tools. Examples include:
@@ -50,21 +50,21 @@ Interactive notebooks + examples can be downloaded by cloning! )
     - How to sort Reddit comments from best to worst (not as easy as you think)
 
 * [**Chapter 5: Would you rather lose an arm or a leg?**](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter5_LossFunctions/LossFunctions.ipynb)
-    The introduction of Loss functions and their (awesome) use in Bayesian methods.  Examples include:
-    - Solving the Price is Right's Showdown
+    The introduction of loss functions and their (awesome) use in Bayesian methods.  Examples include:
+    - Solving the *Price is Right*'s Showdown
     - Optimizing financial predictions
-    - Winning solution to the Kaggle Dark World's competition.
+    - Winning solution to the Kaggle Dark World's competition
 
 * [**Chapter 6: Getting our *prior*-ities straight**](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter6_Priorities/Priors.ipynb)
     Probably the most important chapter. We draw on expert opinions to answer questions. Examples include:
     - Multi-Armed Bandits and the Bayesian Bandit solution.
-    - what is the relationship between data sample size and prior?
-    - estimating financial unknowns using expert priors.
+    - What is the relationship between data sample size and prior?
+    - Estimating financial unknowns using expert priors
 
-    We explore useful tips to be objective in analysis, and common pitfalls of priors. 
+    We explore useful tips to be objective in analysis as well as common pitfalls of priors. 
 
 * **Chapter X1: Bayesian methods in Machine Learning and Model Validation** 
-    We explore how to resolve the overfitting problem plus popular ML methods. Also included are probablistic explainations of Ridge Regression and LASSO Regression.
+    We explore how to resolve the overfitting problem plus popular ML methods. Also included are probablistic explainations of ridge regression and LASSO regression.
     - Tim Saliman's winning solution to Kaggle's *Don't Overfit* problem 
 
 * **Chapter X2: More PyMC Hackery**
@@ -86,7 +86,7 @@ The book can be read in three different ways, starting from most recommended to
 chapters in your browser *plus* edit and run the code provided (and try some practice questions). This is the preferred option to read
 this book, though it comes with some dependencies. 
     -  IPython 0.13 is a requirement to view the ipynb files. It can be downloaded [here](http://ipython.org/)
-    -  For Linux users, you should not have a problem installing Numpy, Scipy, Matplotlib and PyMC. For Windows users, check out [pre-compiled versions](http://www.lfd.uci.edu/~gohlke/pythonlibs/) if you have difficulty. 
+    -  For Linux users, you should not have a problem installing NumPy, SciPy, Matplotlib and PyMC. For Windows users, check out [pre-compiled versions](http://www.lfd.uci.edu/~gohlke/pythonlibs/) if you have difficulty. 
     -  In the styles/ directory are a number of files (.matplotlirc) that used to make things pretty. These are not only designed for the book, but they offer many improvements over the default settings of matplotlib and the IPython notebook.
     -  while technically not required, it may help to run the IPython notebook with `--pylab inline` if you encounter runtime errors.
 2. The second, preferred, option is to use the nbviewer.ipython.org site, which display IPython notebooks in the browser ([example](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter1_Introduction/Chapter1_Introduction.ipynb)).
@@ -99,7 +99,7 @@ Installation and configuration
 ------
 If you would like to run the IPython notebooks locally, (option 1. above), you'll need to install the following:
 -  IPython 0.13 is a requirement to view the ipynb files. It can be downloaded [here](http://ipython.org/ipython-doc/dev/install/index.html)
--  For Linux users, you should not have a problem installing Numpy, Scipy and PyMC. For Windows users, check out [pre-compiled versions](http://www.lfd.uci.edu/~gohlke/pythonlibs/) if you have difficulty. 
+-  For Linux users, you should not have a problem installing NumPy, SciPy and PyMC. For Windows users, check out [pre-compiled versions](http://www.lfd.uci.edu/~gohlke/pythonlibs/) if you have difficulty. 
    - also recommended, for data-mining exercises, are [PRAW](https://github.com/praw-dev/praw) and [requests](https://github.com/kennethreitz/requests). 
 
 -  In the styles/ directory are a number of files that are customized for the notebook. 
@@ -120,11 +120,11 @@ Authors submit content or revisions using the GitHub interface.
 
 -  The current chapter list is not finalized. If you see something that is missing (MCMC, MAP, Bayesian networks, good prior choices, Potential classes etc.),
 feel free to start there. 
--  Cleaning up Python code and making code more PyMC-esque.
+-  Cleaning up Python code and making code more PyMC-esque
 -  Giving better explanations
 -  Spelling/grammar mistakes
 -  Suggestions
--  Contributing to the IPython notebook styles.
+-  Contributing to the IPython notebook styles
 
 
 ####Commiting