Machine Learning with Windows Azure

This is a brief overview of Machine Learning usage on Azure cloud.

Login to your Azure account, select Machine Learning and follow these

Steps:

1. Create new Experiment
2. Drag-n-drop datasets and modules and modify their properties
3. Create a workflow. There are available pre-configured ML modules with R and Python scripts that:
1. Following best practices may help to solve a problem for specific domain.
2. Have pre-built data schemas.
3. Contain domain specific data processing and feature engineering.
4. Include training algorithms and calculation metrics.

Workflow Concerns:

• A Single Experiment:
• could be too complex
• difficult to navigate
• easy to mess up
• iterations have to go through entire graph
• Multi-Steps Workflow:
• each step in a separate Experiment
• uses saved datasets / readers / writers
• iterations come through individual steps separately

Machine Learning Templates from Azure Machine Learning Gallery

• Text classification (text tagging, text categorisation, etc). For example, assign piece of text to one or more pre-defined set of classes or categories:
• categorise articles
• organise web pages into hierarchical categories
• filter email spam
• search query user intend prediction
• support ticket team routing
• sentiment analysis
• feedback analysis
• Fraud detection
• Retail forecasting
• Predictive maintenance

Text Processing:

1. Language detection
2. Normalisation:
1. Convert words into normalised ones:
1. Down case: The -> the
2. Lemmatisation: plays -> play
3. Stemming: mice -> mouse
3. Stop-words removal: the, a, to, with, etc
4. Special character removal: for example, ignore all Non-Alpha-Numeric
5. Ignore numbers, emails, URLs, etc

Feature Extraction:

• 'Bags-of-words' - the document is treated as a set of words regardless order and grammar
• Split text into Bi-grams, tri-grams, n-grams
• Score how it correlates with your topic (e.g. sport, business, etc)
• Move from symbols to numbers:
• term occurrence (number of times words or n-grams appear in document)
• term frequency
• Inverse Document Frequency (IDF)
• the inverted rate of documents that contain words or n-grams against the whole training data set of documents
• downgrades the importance of useless words like 'which', 'the', etc
• TF-IDF
• frequent words that appear only in a small number of documents achieve high value
• Dimensionality Reduction (vocabulary used in documents could be too large) - 'curse of dimensionality'. To do:
• filter-based filter reduction
• wrapper-based filter reduction
• feature hashing
• topic modelling (LDA)

Model Training:

• Binary-class Learners:
• 2-class Logistic Regression
• 2-class Support Vector Machine
• 2-class Boosted Decision Tree
• Multi-class Learners:
• One-vs-All Muliclass
• Multi-class Logistic Regression
• Multi-class Decision Forest

Model Evaluation:

• Split data into Train, Development and Test subsets
• Compare and visualise the results of comparison between two trained models, e.g. N-grams and Uni-grams

Finally

Deploy your model on Azure as a web service. 

Easy!

:)