Type-os and added great links to learning more about Machine Learning

Author: MarkyV

CONTRIBUTING.md

@@ -4,9 +4,9 @@ Contributing code
 
 **Note: This document is just to get started, visit [**Contributing
 page**](http://scikit-learn.org/stable/developers/index.html#coding-guidelines)
-for the full contributor's guide. Make sure to read it carefully to make
+for the full contributor's guide. Please be sure to read it carefully to make
 the code review process go as smoothly as possible and maximize the
-likelihood of your contribution to get merged.**
+likelihood of your contribution being merged.**
 
 How to contribute
 -----------------
@@ -29,7 +29,7 @@ GitHub:
 
    and start making changes. Never work in the ``master`` branch!
 
-4. Work on this copy, on your computer, using Git to do the version
+4. Work on this copy on your computer using Git to do the version
    control. When you're done editing, do:
 
           $ git add modified_files
@@ -43,8 +43,8 @@ Finally, go to the web page of the your fork of the scikit-learn repo,
 and click 'Pull request' to send your changes to the maintainers for
 review. request. This will send an email to the committers.
 
-(If any of the above seems like magic to you, then look up the [Git documentation](http://git-scm.com/documentation) 
-on the web.)
+(If any of the above seems like magic to you, then look up the 
+[Git documentation](http://git-scm.com/documentation) on the web.)
 
 It is recommended to check that your contribution complies with the
 following rules before submitting a pull request:
@@ -64,7 +64,7 @@ following rules before submitting a pull request:
    to other methods available in scikit-learn.
 
 -  At least one paragraph of narrative documentation with links to
-   references in the literature (with PDF links when possible) and
+````   references in the literature (with PDF links when possible) and
    the example.
 
 The documentation should also include expected time and space
@@ -76,7 +76,7 @@ scale in dimensionality: n_features is expected to be lower than
 You can also check for common programming errors with the following
 tools:
 
--  Code with a good unittest coverage (at least 80%), check with:
+-  Code with good unittest coverage (at least 80%), check with:
 
           $ pip install nose coverage
           $ nosetests --with-coverage path/to/tests_for_package
@@ -119,7 +119,7 @@ reStructuredText documents (like this one), tutorials, etc.
 reStructuredText documents live in the source code repository under the
 doc/ directory.
 
-You can edit the documentation using any text editor, and then generate
+You can edit the documentation using any text editor and then generate
 the HTML output by typing ``make html`` from the doc/ directory.
 Alternatively, ``make`` can be used to quickly generate the
 documentation without the example gallery. The resulting HTML files will
@@ -133,7 +133,7 @@ For building the documentation, you will need
 When you are writing documentation, it is important to keep a good
 compromise between mathematical and algorithmic details, and give
 intuition to the reader on what the algorithm does. It is best to always
-start with a small paragraph with a hand-waiving explanation of what the
+start with a small paragraph with a hand-waving explanation of what the
 method does to the data and a figure (coming from an example)
 illustrating it.
 
@@ -143,4 +143,3 @@ Further Information
 Visit the [Contributing Code](http://scikit-learn.org/stable/developers/index.html#coding-guidelines)
 section of the website for more information including conforming to the 
 API spec and profiling contributed code.
-

doc/tutorial/basic/tutorial.rst

@@ -16,11 +16,10 @@ Machine learning: the problem setting
 
 In general, a learning problem considers a set of n 
 `samples <http://en.wikipedia.org/wiki/Sample_(statistics)>`_ of
-data and try to predict properties of unknown data. If each sample is
-more than a single number, and for instance a multi-dimensional entry
+data and then tries to predict properties of unknown data. If each sample is
+more than a single number and, for instance, a multi-dimensional entry
 (aka `multivariate <http://en.wikipedia.org/wiki/Multivariate_random_variable>`_ 
-data), is it said to have several attributes,
-or **features**.
+data), is it said to have several attributes or **features**.
 
 We can separate learning problems in a few large categories:
 
@@ -35,9 +34,12 @@ We can separate learning problems in a few large categories:
       samples belong to two or more classes and we
       want to learn from already labeled data how to predict the class
       of unlabeled data. An example of classification problem would
-      be the digit recognition example, in which the aim is to assign
-      each input vector to one of a finite number of discrete
-      categories.
+      be the handwritten digit recognition example, in which the aim is 
+      to assign each input vector to one of a finite number of discrete
+      categories.  Another way to think of classification is as a discrete 
+      (as opposed to continuous) form of supervised learning where one has a 
+      limited number of categories and for each of the n samples provided,
+      one is to try to label them with the correct categroy or class.
 
     * `regression <http://en.wikipedia.org/wiki/Regression_analysis>`_:
       if the desired output consists of one or more
@@ -52,7 +54,7 @@ We can separate learning problems in a few large categories:
    it is called `clustering <http://en.wikipedia.org/wiki/Cluster_analysis>`_, 
    or to determine the distribution of data within the input space, known as 
    `density estimation <http://en.wikipedia.org/wiki/Density_estimation>`_, or 
-   to project the data from a high-dimensional space down to two or thee 
+   to project the data from a high-dimensional space down to two or three 
    dimensions for the purpose of *visualization* 
    (:ref:`Click here <unsupervised-learning>` 
    to go to the Scikit-Learn unsupervised learning page).
@@ -62,8 +64,8 @@ We can separate learning problems in a few large categories:
     Machine learning is about learning some properties of a data set
     and applying them to new data. This is why a common practice in
     machine learning to evaluate an algorithm is to split the data
-    at hand in two sets, one that we call a **training set** on which
-    we learn data properties, and one that we call a **testing set**,
+    at hand into two sets, one that we call the **training set** on which
+    we learn data properties and one that we call the **testing set**
     on which we test these properties.
 
 .. _loading_example_dataset:
@@ -142,7 +144,7 @@ the classes to which unseen samples belong.
 In `scikit-learn`, an estimator for classification is a Python object that
 implements the methods `fit(X, y)` and `predict(T)`.
 
-An example of estimator is the class ``sklearn.svm.SVC`` that
+An example of an estimator is the class ``sklearn.svm.SVC`` that
 implements `support vector classification
 <http://en.wikipedia.org/wiki/Support_vector_machine>`_. The
 constructor of an estimator takes as arguments the parameters of the

doc/tutorial/common_includes/info.txt

@@ -1 +1,3 @@
-Meant to share common RST file snippets that we want to reuse by inclusion in the real tutorial to lower the maintenance burden of redundant sections.
+Meant to share common RST file snippets that we want to reuse by inclusion 
+in the real tutorial in order to lower the maintenance burden 
+of redundant sections.

doc/tutorial/statistical_inference/finding_help.rst

@@ -10,7 +10,7 @@ clarification in the docstring or the online documentation, please feel free to
 ask on the `Mailing List <http://scikit-learn.sourceforge.net/support.html>`_
 
 
-Q&A communities with Machine Learning practictioners
+Q&A communities with Machine Learning practitioners
 ----------------------------------------------------
 
   :Metaoptimize/QA:
@@ -36,3 +36,7 @@ Q&A communities with Machine Learning practictioners
 .. _`good freely available textbooks on machine learning`: http://metaoptimize.com/qa/questions/186/good-freely-available-textbooks-on-machine-learning
 
 .. _`What are some good resources for learning about machine learning`: http://www.quora.com/What-are-some-good-resources-for-learning-about-machine-learning
+
+-- _'An excellent free online course for Machine Learning taught by Professor Andrew Ng of Stanford': https://www.coursera.org/course/ml
+
+-- _'Another excellent free online course that takes a more general approach to Artificial Intelligence':http://www.udacity.com/overview/Course/cs271/CourseRev/1

doc/tutorial/statistical_inference/model_selection.rst

@@ -97,9 +97,9 @@ of the computer.
 
    *
 
-    - Split it K folds, train on K-1, test on left-out
+    - Split it K folds, train on K-1 and then test on left-out
 
-    - Make sure that all classes are even accross the folds
+    - Make sure that all classes are even across the folds
 
     - Leave one observation out
 
@@ -155,8 +155,8 @@ estimator during the construction and exposes an estimator API::
     0.94228356336260977
 
 
-By default the :class:`GridSearchCV` uses a 3-fold cross-validation. However, if
-it detects that a classifier is passed, rather than a regressor, it uses
+By default, the :class:`GridSearchCV` uses a 3-fold cross-validation. However, 
+if it detects that a classifier is passed, rather than a regressor, it uses
 a stratified 3-fold.
 
 .. topic:: Nested cross-validation
@@ -167,7 +167,7 @@ a stratified 3-fold.
 	array([ 0.97996661,  0.98163606,  0.98330551])
 
     Two cross-validation loops are performed in parallel: one by the
-    :class:`GridSearchCV` estimator to set `gamma`, the other one by
+    :class:`GridSearchCV` estimator to set `gamma` and the other one by
     `cross_val_score` to measure the prediction performance of the
     estimator. The resulting scores are unbiased estimates of the
     prediction score on new data.
@@ -183,8 +183,8 @@ Cross-validated estimators
 ----------------------------
 
 Cross-validation to set a parameter can be done more efficiently on an
-algorithm-by-algorithm basis. This is why, for certain estimators, the
-sklearn exposes :ref:`cross_validation` estimators, that set their parameter
+algorithm-by-algorithm basis. This is why for certain estimators the
+sklearn exposes :ref:`cross_validation` estimators that set their parameter
 automatically by cross-validation::
 
     >>> from sklearn import linear_model, datasets

doc/tutorial/statistical_inference/putting_together.rst

@@ -8,8 +8,8 @@ Putting it all together
 Pipelining
 ============
 
-We have seen that some estimators can transform data, and some estimators
-can predict variables. We can create combined estimators:
+We have seen that some estimators can transform data and that some estimators
+can predict variables. We can also create combined estimators:
 
 .. image:: ../../auto_examples/images/plot_digits_pipe_1.png
    :target: ../../auto_examples/plot_digits_pipe.html
@@ -26,7 +26,7 @@ Face recognition with eigenfaces
 =================================
 
 The dataset used in this example is a preprocessed excerpt of the
-"Labeled Faces in the Wild", aka LFW_:
+"Labeled Faces in the Wild", also known as LFW_:
 
   http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz (233MB)
 
@@ -71,10 +71,6 @@ Expected results for the top 5 most represented people in the dataset::
 Open problem: Stock Market Structure
 =====================================
 
-Can we predict the variation in stock prices for Google?
+Can we predict the variation in stock prices for Google over a given time frame?
 
 :ref:`stock_market`
-
-
-
-

doc/tutorial/statistical_inference/settings.rst

@@ -26,10 +26,10 @@ these arrays is the **samples** axis, while the second is the
     features: their sepal and petal length and width, as detailed in
     `iris.DESCR`.
 
-When the data is not intially in the `(n_samples, n_features)` shape, it
-needs to be preprocessed to be used by the scikit.
+When the data is not initially in the `(n_samples, n_features)` shape, it
+needs to be preprocessed in order to by used by scikit.
 
-.. topic:: An example of reshaping data: the digits dataset 
+.. topic:: An example of reshaping data would be the digits dataset 
 
     .. image:: ../../auto_examples/datasets/images/plot_digits_last_image_1.png
         :target: ../../auto_examples/datasets/plot_digits_last_image.html
@@ -46,7 +46,7 @@ needs to be preprocessed to be used by the scikit.
         >>> pl.imshow(digits.images[-1], cmap=pl.cm.gray_r) #doctest: +SKIP
         <matplotlib.image.AxesImage object at ...>
 
-    To use this dataset with the scikit, we transform each 8x8 image in a
+    To use this dataset with the scikit, we transform each 8x8 image into a
     feature vector of length 64 ::
 
         >>> data = digits.images.reshape((digits.images.shape[0], -1))
@@ -68,16 +68,16 @@ Estimators objects
 
 **Fitting data**: the main API implemented by scikit-learn is that of the
 `estimator`. An estimator is any object that learns from data;
-it may a classification, regression or clustering algorithm or
+it may be a classification, regression or clustering algorithm or
 a `transformer` that extracts/filters useful features from raw data.
 
-All estimator objects expose a `fit` method, that takes a dataset
+All estimator objects expose a `fit` method that takes a dataset
 (usually a 2-d array):
 
     >>> estimator.fit(data)
 
 **Estimator parameters**: All the parameters of an estimator can be set
-when it is instantiated, or by modifying the corresponding attribute::
+when it is instantiated or by modifying the corresponding attribute::
 
     >>> estimator = Estimator(param1=1, param2=2)
     >>> estimator.param1
@@ -90,4 +90,3 @@ underscore::
 
     >>> estimator.estimated_param_ #doctest: +SKIP
 
-