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.
