These are some notes that I typed up during an Azure Machine Learning - Data Camp that I attended at the Microsoft learning center in the Aon building. It was entertaining, informative, and all around a great session; unfortunately, it was entirely useless for my job, since this really has nothing to do with the work that I perform. On the plus side - I was able to recommend to my co-workers on the Analytics team that they should attend it.

Ross Loforte, Data Scientist, Microsoft https://github.com/melzoghbi/datacamp

• Data science is about using data to make decisions that drive actions, involving Data Selection, Preprocessing, Transformation, Data Mining, Delivering value from data (interpretation and evaluation).

• Challenges have been extremely complex overhead, expensive startup, and steep learning curves. The goal of Azure Machine Learning Services is to provide a simpler and scalable method.

• Machine Learning is using known data to develop a model to predict unknown data.

  • Known Data: Big enough archive, previous observations, past data
  • Model: Known data + algorithms (ML algorithms)
  • Unknown Data: Missing, unseen, not existing, future data

• Microsoft's usage of ML: 1997 Hotmail spam filters, 2008 Bing maps routing, 2009 Bing search, 2010 Kinect

• Azure Machine Learning's goal - Make machine learning accessible to every enterprise, data scientist, developer, information worker, consumer, and device anywhere in the world.

• Expect that 75% of the time will be building the model and testing validity of predictions.

• Cycle of Steps to building a machine learning solution

  • Frame the problem
  • Get and Prepare Data
  • Develop the Model
    • Analysis / Metric Definition
    • Feature Engineering
    • Model Training
    • Parameter Tuning
    • Evaluation
  • Deploy Model
  • Evaluate / Track Performance

• ML Algorithm defines how your model will react

  • Written in R or Python

• Classification: answer yes/no

• Regression: predict a numeric value

• Anomaly Detection: value outside of bounds

• Clustering: unsupervised learning, group like data