Finished last details from 2017.fall

Author: thmosqueiro

LICENSE

@@ -619,56 +619,3 @@ Program, unless a warranty or assumption of liability accompanies a
 copy of the Program in return for a fee.
 
                      END OF TERMS AND CONDITIONS
-
-            How to Apply These Terms to Your New Programs
-
-  If you develop a new program, and you want it to be of the greatest
-possible use to the public, the best way to achieve this is to make it
-free software which everyone can redistribute and change under these terms.
-
-  To do so, attach the following notices to the program.  It is safest
-to attach them to the start of each source file to most effectively
-state the exclusion of warranty; and each file should have at least
-the "copyright" line and a pointer to where the full notice is found.
-
-    <one line to give the program's name and a brief idea of what it does.>
-    Copyright (C) <year>  <name of author>
-
-    This program is free software: you can redistribute it and/or modify
-    it under the terms of the GNU General Public License as published by
-    the Free Software Foundation, either version 3 of the License, or
-    (at your option) any later version.
-
-    This program is distributed in the hope that it will be useful,
-    but WITHOUT ANY WARRANTY; without even the implied warranty of
-    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-    GNU General Public License for more details.
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-Also add information on how to contact you by electronic and paper mail.
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-  If the program does terminal interaction, make it output a short
-notice like this when it starts in an interactive mode:
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-    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
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-if any, to sign a "copyright disclaimer" for the program, if necessary.
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-<http://www.gnu.org/licenses/>.
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-  The GNU General Public License does not permit incorporating your program
-into proprietary programs.  If your program is a subroutine library, you
-may consider it more useful to permit linking proprietary applications with
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-Public License instead of this License.  But first, please read
-<http://www.gnu.org/philosophy/why-not-lgpl.html>.

README.md

@@ -7,18 +7,19 @@ This is the homepage for the Collaboratory Workshop, Machine Learning with Pytho
 
 * [Workshop description](./README.md#workshop-description)
 
-* [Day 1](./README.md#day1) - Jupyter and Machine Learning
+* [Day 1](./README.md#day-1---introduction-python-review-and-jupyter-notebooks) - Jupyter and Machine Learning
 
-* [Day 2](./README.md#day2) - Classification and performance
+* [Day 2](./README.md#day-2---classification-performance-and-cross-validation) - Classification and performance
 
-* [Day 3](./README.md#day3) - Cross-validation and regression
+* [Day 3](./README.md#day-3---regression-and-clustering) - Cross-validation and regression
 
 * [Resources of after the workshop](./README.md#after-the-workshop)
 
 * [Contact us!](./README.md#contact)
 
 * [Material's license](./README.md#materials-license)
 
+
 <br />
 
 ## Workshop description
@@ -27,7 +28,6 @@ In this workshop, we explore applications of Machine Learning to analyze biologi
 
 Students are encouraged to attend to the Advanced Python and Modern Statistics workshops, although no advanced knowledge will be assumed.
 
-
 ### Technical requirements
 
 Attendees should have a working copy of Python 2 or 3 with the following packages:
@@ -36,16 +36,15 @@ Attendees should have a working copy of Python 2 or 3 with the following package
 * Scikit-Learn
 * Jupyter Notebooks
 
-**Pro Tip:** regardless of your operating system, you can install [Continuum Analytics's Anaconda](https://www.anaconda.com/download) and all of the above requirements will be met. The installation process usually takes a few minutes. This is **highly recommended** if you are not an experienced user.
+**ProTip:** regardless of your operating system, you can install [Continuum Analytics's Anaconda](https://www.anaconda.com/download) and all of the above requirements will be met. The installation process usually takes a few minutes. This is **highly recommended** if you are not an experienced user.
 
 
 
 <br />
 
-<a name="day1" ></a>
 ## Day 1 - Introduction, Python review and Jupyter notebooks
 
-The slides from Day 1 are available [here](href="https://www.dropbox.com/s/o8jiciq3ps2nh47/MachineLearning_Python_Collaboratory_day1.pdf?dl=0).
+The slides from Day 1 are available [here](https://www.dropbox.com/s/o8jiciq3ps2nh47/MachineLearning_Python_Collaboratory_day1.pdf?dl=0).
 
 <a href="https://www.dropbox.com/s/o8jiciq3ps2nh47/MachineLearning_Python_Collaboratory_day1.pdf?dl=0"><img src="./materials/day_1/day1_thumbnailr.png" width=300px /></a>
 
@@ -72,21 +71,18 @@ Genomics and NGA
 * [Clustering methods applied to TCGA Ovarian Cancer Coexpression Matrix](https://github.com/ucsd-ccbb/jupyter-genomics/blob/master/notebooks/networkAnalysis/TCGA_clustering_OV/TCGA_clustering_OV.ipynb), by Brin Rosenthal
 * [5 Analyzing Core Diversity](https://github.com/ucsd-ccbb/jupyter-genomics/blob/master/notebooks/microbiome/5%20Analyzing%20Core%20Diversity/5%20Analyzing%20Core%20Diversity.ipynb), by Amanda Birmingham
 
-
 Cell and molecular biology
 
 * [Autoencoders for calcium fluorescence](https://github.com/codekansas/calcium-gan/blob/master/autoencoder.ipynb), by Benjamin Bolte
 * [Calcium Imaging Segmentation with Neural Networks](https://github.com/alexklibisz/deep-calcium/blob/master/notebooks/dlmia_workshop_figures.ipynb), by Alex Klibisz
 * [Analysis of time lapse images of plates with growing colonies](https://github.com/JorgeRV/biosync/blob/master/Colony_size_and_fluo.ipynb), by Jorge Riveros Vergara
 * [Interlab Study](http://nbviewer.jupyter.org/github/thmosqueiro/modeligem/blob/master/notebooks/DataAnalysis/Interlab/Interlab.ipynb) for iGEM 2015, Brazil-USP team.
 
-
 Ecology and evolutionary biology
 
 * [Reverse Ecology of Uncultivated Freshwater Actinobacteria](https://github.com/celawson87/reverseEcology/blob/master/iPythonNotebooks/ReverseEcologyOLD.ipynb), by Joshua Hamilton
 * [Example of feature extraction from images](https://github.com/bw4sz/ComputerVisionEcology/blob/master/Measurement.ipynb), by Ben Weinstein
 
-
 Data visualization
 
 * [Amazing notebook with 21 examples](http://nbviewer.jupyter.org/gist/msund/7ac1203ded66fe8134cc) of plots using various Python libraries
@@ -103,8 +99,6 @@ Generic data analysis and introductory notebooks
 * [Example of Machine Learning](http://nbviewer.jupyter.org/github/rhiever/Data-Analysis-and-Machine-Learning-Projects/blob/master/example-data-science-notebook/Example%20Machine%20Learning%20Notebook.ipynb) with the Iris dataset.
 
 
-<br />
-
 ### More about object orientation
 
 If you want to learn more about object orientation in Python, you can find below some resources to help you getting started.
@@ -116,7 +110,6 @@ If you want to learn more about object orientation in Python, you can find below
 
 <br />
 
-<a name="day2" ></a>
 ## Day 2 - Classification, performance and cross-validation
 
 Slides from this class are available [here](https://www.dropbox.com/s/4q1pk2c4xmgd257/MachineLearning_Python_Collaboratory_day2.pdf?dl=0).
@@ -141,7 +134,6 @@ Slides from this class are available [here](https://www.dropbox.com/s/4q1pk2c4xm
 
 <br />
 
-<a name="day3" ></a>
 ## Day 3 - Regression and clustering
 
 The slides from Day 3 are available [here](https://www.dropbox.com/s/szmxeqclhgcjwq5/MachineLearning_Python_Collaboratory_day3.pdf?dl=0).
@@ -156,7 +148,6 @@ The slides from Day 3 are available [here](https://www.dropbox.com/s/szmxeqclhgc
 
 <br />
 
-<a name="after-the-workshop" ></a>
 ## After the workshop
 
 Here are some resources to keep you advancing your studies and find everything you need to apply Machine Learning by yourself in your research. If you find anything interesting and would like to add to the list below, [please leave us a message](./README.md#contact)!