You're right in that you need direction. Each subclass of machine learning has some elementary algorithms worth learning: For unsupervised learning or probabil

You're right in that you need direction. Each subclass of machine learning has some elementary algorithms worth learning: For unsupervised learning or probabilistic latent models, the EM algorithm, Metropolis-Hastings sampling, variational methods (variation EM, variational Bayes, expectation propagation). For supervised learning, linear regression, perceptrons, support vector machines. For reinforcement learning, Q-learning, E^3, Rmax.