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  • ML Concepts
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  • ML models
  • Linear regression (80 min)
    • Linear regression (10 min)
    • Loss (10 min)
    • Interactive exercise: Parameters (5 min)
    • Gradient descent (10 min)
    • Hyperparameters (10 min)
    • Interactive exercise: Gradient descent (5 min)
    • Programming exercise (20 min)
    • Test your knowledge (10 min)
    • What's next
  • Logistic regression (35 min)
    • Introduction (5 min)
    • Calculating a probability (10 min)
    • Loss and regularization (10 min)
    • Test your knowledge (10 min)
    • What's next
  • Classification (70 min)
    • Introduction (3 mins)
    • Thresholds and the confusion matrix (12 min)
    • Accuracy, recall, precision, and related metrics (15 min)
    • ROC and AUC (10 min)
    • Prediction bias (3 min)
    • Multi-class classification (2 min)
    • Programming exercise (15 min)
    • Test your knowledge (10 min)
    • What's next
  • Data
  • Working with numerical data (85 min)
    • Introduction (3 min)
    • How a model ingests data with feature vectors (5 min)
    • First steps (5 min)
    • Programming exercises (10 min)
    • Normalization (20 min)
    • Binning (15 min)
    • Scrubbing (5 min)
    • Qualities of good numerical features (5 min)
    • Polynomial transforms (5 min)
    • Test your knowledge (10 min)
    • Conclusion (2 min)
    • What's next
  • Working with categorical data (50 min)
    • Introduction (5 min)
    • Vocabulary and one-hot encoding (10 min)
    • Common issues with categorical data (5 min)
    • Feature crosses (5 min)
    • Feature cross exercises (15 min)
    • Test your knowledge (10 min)
    • What's next
  • Datasets, generalization, and overfitting (105 min)
    • Introduction (5 min)
    • Data characteristics (10 min)
    • Labels (10 min)
    • Imbalanced datasets (10 min)
    • Dividing the original dataset (10 min)
    • Transforming data (5 min)
    • Generalization (5 min)
    • Overfitting (10 min)
    • Model complexity (10 min)