Machine learning models

Feature is input, label is output. Combining features to create another feature is called feature engineering.

[Unsupservised] Clustering: Grouping similar data points together.

Example: Give a group of people and output group them into different groups (Foolist, lovely, smart).

[Supervised] Classification: Predicting the category of a data point (Foolist, lovely, smart).

Example: Give a people and determine which group they should belong to. Evaluation metrics: True positive rate.

[Supervised]Regression: Predicting a numeric value.

Example: Regression is a form of machine learning used to understand the relationships between variables to predict a desired outcome. Regression predicts a numeric label or outcome based on variables, or features. For example, an automobile sales company might use the characteristics of a car (such as engine size, number of seats, mileage, and so on) to predict its likely selling price. In this case, the characteristics of the car are the features, and the selling price is the label. Evaluation metrics: MAE, RMSE and R2

Azure Machine Learning

Basic workspace vs Enterprise workspace

• Enterprise workspace is now fused with Basic

Azure Machine Learning Designer

• Provide drag-and-drop interface to create, test, and deploy machine learning models. You can drag and drop: Module, dataset
• Save progress as a draft pipeline.
• Support R and Python script.

Module:

• split data: to create training dataset and validation dataset.

Computer Vision

Confidence is the calculated probability of a corect image Classification.