Intro to Machine Learning

5 Types of Data Science Questions How much or how many? (regression) Which category? (classification) Which group? (clustering) Is this weird? (anomaly detection) Which option should be taken? (recommendation)

Define SMART success metrics Specific Measurable Achievable Relevant Time-bound For example: Achieve customer churn prediction accuracy of X% by the end of this 3- month project, so that we can offer promotions to reduce churn.

Brandon Rohrer, Senior Data Scientist at Microsoft How to do Data Science Brandon Rohrer, Senior Data Scientist at Microsoft

Microsoft Team Data Science Process

Feature Engineering Adding calculated fields and / or additional labels to your data set Removing fields is called “Feature Selection”

Common tasks in pre-processing / feature engineering Data cleaning: Fill in or missing values, detect and remove noisy data and outliers. Data transformation: Normalize data to reduce dimensions and noise. Data reduction: Sample data records or attributes for easier data handling. Data discretization: Convert continuous attributes to categorical attributes for ease of use with certain machine learning methods. Text cleaning: remove embedded characters which may cause data misalignment, for e.g., embedded tabs in a tab-separated data file, embedded new lines which may break records, etc.

Model Fitting

Model Training Split the input data randomly for modeling into a training data set and a test data set. Build the models using the training data set. Evaluate (training and test dataset) a series of competing machine learning algorithms along with the various associated tuning parameters (known as parameter sweep) that are geared toward answering the question of interest with the current data. Determine the “best” solution to answer the question by comparing the success metric between alternative methods.

Model Evaluation Regression Classification Recommendation Clustering Coefficient of determination (R Squared) from 0 to 1 Relative Abs, Relative Squared, Root Mean Squared, and Mean Abs Error Classification ROC Curve, Confusion Matrix, Accuracy, Precision, Recall, F1 Recommendation NDCG Clustering Avg distance to cluster center , other center Maximal distance to cluster center

Cross Validation Leverages smaller data sets where 70 / 30 might not be feasible Helps avoid overfitting More accurate estimate of model performance

Deployment R / Python (SQL Server 2017) Machine Learning Services (In-database) Azure ML