Machine Learning, Deep Learning, and NLP

  • Dimensionality Reduction as Data Pre-Processing
  • Principal Component Analysis (PCA)
  • Linear and Polynomial Regression
  • K-Nearest Neighbors
  • Decision Tree
  • Balancing Bias vs Variance of ML Model
  • Ensemble Learning: Random Forest and Adaptive Boost
  • Identifying Important Features of Data
  • Introduction to Deep Learning: Logistic Regression, Perceptron, MLP
  • Convolutional Neural Network (CNN)

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