God, whatever you do please don't start by picking a few datasets at UCI ML site and using R packages to play with the data. An ideal approach will be to: - P

God, whatever you do please don't start by picking a few datasets at UCI ML site and using R packages to play with the data. An ideal approach will be to: - Pick any programming language and start off with plain regression. It may look simple but this will become fantastic base going forward - Generate a synthetic data set and apply your freshly written regression on it - Expand your toolkit to include test and training data set generation and calculation of ROC curves and confusion tables - Add logistic regression, regularizers and other advanced regression models to the toolkit - Use a real world dataset and develop multiple different models. And pick the best model (choosing the right model itself is a big task in itself) - Then try coding Neural Networks, SVM, etc.