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.
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.