Machine Learning Mastery with R Jason Brownlee

• I have just finished this excellent eBook from Jason Brownlee. This was for me a constant excitation at every chapter. The learning curve is progressive and you finish with end to end projects.
• A lot of fundamentals about the metrics of Machine Learning, the algorithms of Machine Learning, how to spot the best Machine Learning algorithms, and the fundamentals of analyzing the data before using any algorithms are explained into this book. What I liked, is that it is a book on which you are not passive, but you can implement and see the results directly on your machine.
• Prior to this book, I followed the MOOC "Introduction à la statistique avec R" which gives you the basics of statistics and the basics of R language.
• You will go through the algorithms KNN(Méthode des plus proches voisins) part of the non-linear algorithms family (peu d' hypothèses sur la fonction à modéliser) together with Naive Bayes, Support Vector Machine (SVM), Classification and Regression tress (CART). You will also go through the linear algorithms (forte hypothèse sur la forme de la fonction modélisée) like linear regression, logistic regression, Linear Discriminators Analysis (LDA), regularized regression.
• You will compare the performance of Machine Learning algorithms, and you will tune the Machine Learning algorithms.
• The required metrics need to analyze you data are also explained in the book, but in plain English and not with complex mathematical equations. RMSE (Root Mean Squared Error), the average deviation of the prediction from the observation and R2, le coefficient de détermination (1 is perfect, 0 is worst), accuracy for the classification (percentage of correctly classified instances out of all instances), Log Loss used to evaluate binary classification and more common for multi class classification algorithms.
• I have been using the R language and Studio tool to copy and paste the snippets of code from the book in my environment. See below my RStudio environment with an example extracted from the book: