diff --git a/README.md b/README.md
index 40daa0714..93db67889 100644
--- a/README.md
+++ b/README.md
@@ -10,11 +10,11 @@ surrounded by huge amounts of data. For example, there are about one trillion we
 hour of video is uploaded to YouTube every second, amounting to years of content every
 day; the genomes of 1000s of people, each of which has a length of more than a billion  base pairs, have
 been sequenced by various labs and so on. This deluge of data calls for automated methods of data analysis,
-which is exactly what machine learning provides. 
+which is exactly what machine learning aims at providing. 
 
 ## Learning outcomes
 
-This course aims thus at discussing many of the central algorithms used in Data Analysis and Machine Learning.  The course is project based and through  various numerical projects, normally three, you will be exposed to fundamental research problems in these fields, with the aim to reproduce state of the art scientific results. Both supervised and unsupervised methods will be covered. You will learn to develop and structure large codes for studying different  systems where Machine Learning is applied to, get acquainted with computing facilities and learn to handle large scientific projects. A good scientific and ethical conduct is emphasized throughout the course. More specifically, after this course you will
+This course aims at discussing many of the central algorithms used in Data Analysis and Machine Learning.  The course is project based and through  various numerical projects, normally three, you will be exposed to fundamental research problems in these fields, with the aim to reproduce state of the art scientific results. Both supervised and unsupervised methods will be covered. You will learn to develop and structure large codes for studying different  systems where Machine Learning is applied to, get acquainted with computing facilities and learn to handle large scientific projects. A good scientific and ethical conduct is emphasized throughout the course. More specifically, after this course you will
 
 - Learn about basic data analysis, statistical analysis, Bayesian statistics, Monte Carlo sampling, data optimization and machine learning;
 - Be capable of extending the acquired knowledge to other systems and cases;
@@ -23,7 +23,7 @@ This course aims thus at discussing many of the central algorithms used in Data
 - Learn about various neural networks and deep  learning methods for supervised and unsupervised learning;
 - Learn about about decision trees and random forests
 - Learn about support vector machines and kernel transformations
-- Reduction of data sets, from PCA to clustering, supervised and unsupervided methods
+- Reduction of data sets, from PCA to clustering, supervised and unsupervised methods
 - Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++.  
 
 ## Prerequisites