Objective 1

Evaluating generalized linear models

  • Diagnostic evaluation of multivariate least-squares regressions
  • Influential outlier detection
  • Interpreting coefficients, marginal effects, and odds-ratios

Objective 2

Machine Learning (Supervised)

  • Performance evaluation: overfitting, scoring best practices, impacts on business and strategic decisions
  • Cross-validation procedure, parameter tuning, regularization
  • Ensemble approaches: bagging, boosting, and stacking models
  • Model explainability and feature effects

Objective 3

Natural Language Processing

  • Regular expressions and tokenization
  • Categorization and tagging words: corpora, n-gram, and transformation-based tagging
  • Context-free, dependency, and feature based grammars
  • Word & sentence embeddings

Objective 4

Machine Learning (Unsupervised)

  • Factor Analysis & PCA: dimension selection, rotation effects, variance contribution
  • Cluster analysis: model advantages and limitations, use case feasibility, interpretation
  • Cluster analysis: evaluation metrics

Objective 5

Time Series & Forecasting

  • ARIMA models, diagnostics, and evaluation
  • Autocorrelation plots for lag selection, detection of stationarity
  • Unit-root tests
  • Forecast metrics and model selection

Objective 6

Deep Learning

  • Keras framework and operation
  • Deep Neural Networks – CNN, LSTM, and more
  • Transfer learning and pre-trained models
  • OpenCV and open-source computer vision models for object detection, facial recognition, and image processing

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