Giving Computers the Ability to Learn from Data

supervised learning,unsupervised learning and reinforcement learning

1. Supervised learning: classification task and regression
2. Unsupervised learning: finding subgroups with clustering and dimension reduction（commonly used approach in feature preprocessing to remove noise from data,and compress the data onto a smaller dimensional subspace while retaining most of the relevant information）
3. Reinforcement learning: solving interactive problems

preprocessing

1. transforming the features in the range [0, 1] or a standard normal distribution with zero mean and unit variance
2. Dimensionality reduction
3. divide the dataset into a separate training and test set. We use the training set to train and optimize our machine learning model, while we keep the test set until the very end to evaluate the final model.

Training and selecting a predictive model

1. we first have to decide upon a metric to measure performance.
2. compare at least a handful of different algorithms in order to train and select the best performing model.
3. different cross-validation techniques
4. use of hyperparameter optimization techniques（Intuitively, we can think of those hyperparameters as parameters that are not learned from the data but represent the knobs of a model that we can turn to improve its performance.

the whole process