[docs] fixed links and typos (#855)

* fixed links while viewing from wiki page

* fixed typos

* fixed repeated word and added markdown in FAQ.md

* fixed link to VS

README.md

@@ -37,11 +37,11 @@ News
 
 02/12/2017: LightGBM v1 stable release.
 
-01/08/2017 : Release [**R-package**](./R-package) beta version, welcome to have a try and provide feedback.
+01/08/2017 : Release [**R-package**](https://github.com/Microsoft/LightGBM/tree/master/R-package) beta version, welcome to have a try and provide feedback.
 
 12/05/2016 : **Categorical Features as input directly**(without one-hot coding). Experiment on [Expo data](http://stat-computing.org/dataexpo/2009/) shows about 8x speed-up with same accuracy compared with one-hot coding.
 
-12/02/2016 : Release [**python-package**](./python-package) beta version, welcome to have a try and provide feedback.
+12/02/2016 : Release [**python-package**](https://github.com/Microsoft/LightGBM/tree/master/python-package) beta version, welcome to have a try and provide feedback.
 
 
 External (unofficial) Repositories
@@ -93,7 +93,7 @@ LightGBM has been developed and used by many active community members. Your help
 - Contribute to the [tests](https://github.com/Microsoft/LightGBM/tree/master/tests) to make it more reliable. 
 - Contribute to the [documents](https://github.com/Microsoft/LightGBM/tree/master/docs) to make it clearer for everyone.
 - Contribute to the [examples](https://github.com/Microsoft/LightGBM/tree/master/examples) to share your experience with other users.
-- Check out [Development Guide](./docs/development.rst).
+- Check out [Development Guide](https://github.com/Microsoft/LightGBM/blob/master/docs/development.rst).
 - Open issue if you met problems during development.
 
 Microsoft Open Source Code of Conduct

docs/FAQ.md

@@ -82,7 +82,7 @@ Remember this is a free/open community support. We may not be available 24/7 to
 
 - **Question 8**: CPU are not kept busy (like 10% CPU usage only) in Windows when using LightGBM on very large datasets with many core systems.
 
-- **Solution 8**: Please use [use Visual Studio](https://github.com/Microsoft/LightGBM/issues/740) as it may be 10x faster than MinGW especially for very large trees.
+- **Solution 8**: Please use [Visual Studio](https://www.visualstudio.com/downloads/) as it may be [10x faster than MinGW](https://github.com/Microsoft/LightGBM/issues/749) especially for very large trees.
 
 ---
 
@@ -117,9 +117,9 @@ Remember this is a free/open community support. We may not be available 24/7 to
 
 - **Question 2**: I see error messages like `Cannot get/set label/weight/init_score/group/num_data/num_feature before construct dataset`, but I already construct dataset by some code like `train = lightgbm.Dataset(X_train, y_train)`, or error messages like `Cannot set predictor/reference/categorical feature after freed raw data, set free_raw_data=False when construct Dataset to avoid this.`.
 
-- **Solution 2**: Because LightGBM constructs bin mappers to build trees, and train and valid Datasets within one Booster share the same bin mappers, categorical features and feature names etc., the Dataset objects are constructed when construct a Booster. And if you set free_raw_data=True (default), the raw data (with python data struct) will be freed. So, if you want to:
+- **Solution 2**: Because LightGBM constructs bin mappers to build trees, and train and valid Datasets within one Booster share the same bin mappers, categorical features and feature names etc., the Dataset objects are constructed when construct a Booster. And if you set `free_raw_data=True` (default), the raw data (with python data struct) will be freed. So, if you want to:
 
   + get label(or weight/init_score/group) before construct dataset, it's same as get `self.label`
   + set label(or weight/init_score/group) before construct dataset, it's same as `self.label=some_label_array`
   + get num_data(or num_feature) before construct dataset, you can get data with `self.data`, then if your data is `numpy.ndarray`, use some code like `self.data.shape`
-  + set predictor(or reference/categorical feature) after construct dataset, you should set free_raw_data=False or init a Dataset object with the same raw data
+  + set predictor(or reference/categorical feature) after construct dataset, you should set `free_raw_data=False` or init a Dataset object with the same raw data

docs/development.rst

@@ -6,7 +6,7 @@ Algorithms
 
 Refer to `Features <https://github.com/Microsoft/LightGBM/wiki/Features>`__ to get important algorithms used in LightGBM.
 
-Classes And Code Structure
+Classes and Code Structure
 --------------------------
 
 Important Classes
@@ -31,7 +31,7 @@ Important Classes
 +-------------------------+--------------------------------------------------------------------------------------+
 | ``Metric``              | Evaluation metrics                                                                   |
 +-------------------------+--------------------------------------------------------------------------------------+
-| ``Network``             | Newwork interfaces and communication algorithms                                      |
+| ``Network``             | Network interfaces and communication algorithms                                      |
 +-------------------------+--------------------------------------------------------------------------------------+
 | ``ObjectiveFunction``   | Objective function used to train                                                     |
 +-------------------------+--------------------------------------------------------------------------------------+
@@ -46,9 +46,9 @@ Code Structure
 +---------------------+------------------------------------------------------------------------------------------------------------------------------------+
 | Path                | description                                                                                                                        |
 +=====================+====================================================================================================================================+
-| ./include           | header files                                                                                                                       |
+| ./include           | Header files                                                                                                                       |
 +---------------------+------------------------------------------------------------------------------------------------------------------------------------+
-| ./include/utils     | some common functions                                                                                                              |
+| ./include/utils     | Some common functions                                                                                                              |
 +---------------------+------------------------------------------------------------------------------------------------------------------------------------+
 | ./src/application   | Implementations of training and prediction logic                                                                                   |
 +---------------------+------------------------------------------------------------------------------------------------------------------------------------+
@@ -75,7 +75,7 @@ C API
 
 Refere to the comments in `c\_api.h <https://github.com/Microsoft/LightGBM/blob/master/include/LightGBM/c_api.h>`__.
 
-High level Language package
+High Level Language Package
 ---------------------------
 
 Follow the implementation of `python-package <https://github.com/Microsoft/LightGBM/tree/master/python-package/lightgbm>`__.