Machine Learning Concepts Apply AWS ML to problems you have existing samples of actual answers For example, to predict if new email is spam or not, you need to collect examples of spam and non-spam. Binary classification (true / false) Is spam or not spam, churn, will customer accept campaign? Multiclass classification (one of more than two outcomes) Regression (numeric number) Building a Machine Learning Application Frame the core ML problems Collect, clean and prepare data Features from raw data Feed to learning algorithm to build models Use the model to generate predictions for new data Linear Models Leaning process computes one weight for each feature to form a model that can predict the target value For example, estimated target = 0.2 + 5 * age + 0.00003 * income Learning Algorithm Learn the weights of the model Loss function: penalty when estimate target provide by the model not equal exact result Optimization technique: minimize the loss (Stochastic G