Merge branch 'master' of github.com:lambda-science/Machine-Learning-T…

…D-ESBS

README.md

@@ -6,38 +6,40 @@ In this TD you will learn how to perform a full end-to-end data analysis using m
 However this needs a correct Python instalation to work and using Jupyter Notebook with the correct libraries installed.    
 Here I suggest you two way to be able to work in this TD.  
 
-# 1/ The Easy (and Lazy) Way: using Online Google Collab Notebooks.
-You will NEED a Google Account for this to work.  
+# 1/ The Fast and Easy (no installation) Way: using Online Google Collab Notebooks.
+Because it is provided by our lord and saviour Google, you will NEED a Google Account for this to work.  
 1. Go to: https://colab.research.google.com/
 2. On the pop-up to select Notebook, select the Github Tab and enter this link: https://github.com/lambda-science/Machine-Learning-TD-ESBS and select the notebook showing (TD_Machine_Learning.ipynb).
 3. Download the `healthcare-dataset-stroke-data.csv` dataset availiable here: [DOWNLOAD](https://www.lbgi.fr/~meyer/healthcare-dataset-stroke-data.csv)
 4. On the left select the Folder icon and then click the upload file button as indicated in the screenshot and upload the dataset.   
 ![Upload File](https://i.imgur.com/2WlyUku.png) 
 5. You should be able to run notebook and start writing code ! Create a cell and run `print("Hello World")` to check if everything is good !
 
-/!\ Warnings: I briefly test this and seem's to work fine. However if there are issues with python version, libraries ect... I won't be able to provide extensive support
-### **Congratulations** you should now be ready to code for the TD !  
+### **Congratulations** you should now be ready to code for the TD ! You should read the second part at least for the informations that it contains ;)  
 
 .  
 
-# 2/ The Hard but Worth-It Way ! (As you will be doing it this way in any internship or job)
+# 2/ The Hard but Worth-It Way ! (As you will be doing it this way in any internship or job)  
+It makes everything run on your computer instead of relying on Google's one. I will briefly explain each tool to you and how to use them.  
 
 1. **Git**  
+To Install Git: [Git](https://git-scm.com/downloads) (should already be installed on all Linux)
 Git is command-line software used in informatics to do code versioning (tracking modifications and updates). In any informatics project you WILL be using it. In this TD we will only use Git to download the TD Code from a GitHub Repository using the command in a terminal:   
 `git clone https://github.com/lambda-science/Machine-Learning-TD-ESBS.git`.  
-This code will also be availiable on Moodle in a zip folder for people struggling with git.  
+For people that do not want to use Git and could not install it the code is also availiable as a .zip file [HERE](https://www.lbgi.fr/~meyer/TD_ML.zip)  
+
 
 
 2. **Anaconda envrionnement**  
 Anaconda is a python distribution and package manager. It is the prefered way for data-scientist to install Python. We will use Anaconda to install our **python environnement**. An environnement is a python installation with a specific set of library installed. This way we can insure that we all have the same package installed with the same version.  
-In any Python project, you WILL be using Anaconda environnement. It's is crucial for reproductibility, sharing and tracking.  
+In any Python project, you WILL be using Anaconda environnement (or at least Virtual Environnement). It's is crucial for reproductibility, sharing and tracking.  
 **To install our environnement**  
 If not already done, install Anaconda from: [Anaconda Download Page](https://www.anaconda.com/products/individual#Downloads)  
 After cloning the Git repository, you will find a file named `environment.yml` containing all informations for the Anaconda envrionnement. You can now create the environnement using:  
 `conda env create -f environment.yml`  
-Note: If an error occurs such as `conda is not a valid command` you might need to use anaconda prompt for the command. Also environnement are heavy (1.5-2gb here) and can take some time to install.  
+Note: If an error occurs such as `conda is not a valid command` you might need to use the anaconda prompt software for the command. Also environnement are heavy (1.5-2gb here) and can take some time to install.  
 You can now activate your environnement in your current terminal using:  
-`conda activate TD_ML`  
+`conda activate TD_ML`. Your command-line should now look like `(TD-ML) you@computername:~`
 
 3. **Jupyter Notebooks**  
 Jupyter Notebook is the main tool of any data-scientist. It allows you to write and run python code dynamically without reloading all the code, data and variables everytime.   
@@ -47,6 +49,6 @@ You have several option to open Jupyter Notebooks.
 `jupyter-notebook`  
 You browser should automatically open a windows on Jupyter or you can simply click the link. Please check if you can open the ipynb file and the folder.  
 Note: Don't close the terminal prompt as it would shutdown the Jupyter Server.  
-* **The 2nd Easy Way:** Use [VSCode](https://code.visualstudio.com/)  
+* **The 2nd Way:** Use [VSCode](https://code.visualstudio.com/)  
 Install the python extension. VSCode is able to natively open Jupyter Notebook with a great interface and without a server. Just select the right python environnement and you're ready to go !  
 ### **Congratulations** you should now be ready to code for the TD. Simply open the .ipynb file using jupyter-notebook or VSCode !