AWS ML Speciality (Part 3.1)

This post gives a quick review on the framing of business problems as machine learning problems.

Frame business problems as machine learning problems

Determine when to use/when not to use ML

Use ML when:

• There is tons of data that need to be analyzed for patterns that can be used to make predictions
• You cannot code the rules. No simple rule-based solution is possible
• You cannot scale (say, to manually recognize a few million emails)

Don’t use ML when:

• You want to solve a simple problem for which a rule-based solution would suffice
• You don’t have labeled data and in-house expertise

Best practics when deciding on ML problem:

• define success criteria
• establish a performance metric
• define inputs, outputs and metrics
• decide if ML suitable?
• data sourcing and annotation objectives
• select a simple model

Know the difference between supervised and unsupervised learning

(From here)

Two types of classification: binary and multiclass

Selecting from among classification, regression, forecasting, clustering, recommendation, etc

Machine learning

• Supervised learning
  • Classification
  • Regression
• Unsupervised learning
  • Clustering
    • Topic modeling
      • LDA (Latent Dirichlet Allocation)\(^\star\)
      • NTM (Neural Topic Model)\(^\star\)
  • Embeddings
    • Object2Vec\(^\star\)
  • Anomaly Detection
    • Random Cut Forest\(^\star\)
    • IP Insights\(^\star\)
  • Dimensionality Reduction
    • Principle Component Analysis (PCA)\(^\star\)

(From here)

Sagemaker algorithms for each of them: