Another free book to learn Machine Learning. It also comes with a Youtube video series available here. 

Content

• Machine Learning Setup
• k-Nearest Neighbors / Curse of Dimensionality
• Perceptron
• Estimating Probabilities from data
• Bayes Classifier and Naive Bayes
• Logistic Regression / Maximum Likelihood Estimation / Maximum a Posteriori
• Gradient Descent
• Linear Regression
• Support Vector Machine
• Empirical Risk Minimization
• Model Selection
• Bias-Variance Tradeoff
• Kernels
• Kernels continued
• Gaussian Processes
• k-Dimensional Trees
• Decision Trees
• Bagging
• Boosting
• Neural Networks
• Deep Learning / Stochastic Gradient Descent

You can access this material here. For other free tutorials (including from Berkeley, Harvard, Columbia, Google, Microsoft and so on), follow this link.