Course curriculum
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1
ML Breath
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[Amazon] Variance-Bias Trade-Off
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[Amazon] Cross-Validation
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[Google] Multicollinearity
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[PayPal] Imbalanced Labels
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[Amazon] The Curse of Dimensionality
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[Uber] Explain AUC
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[Google] Model Evaluation
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[Amazon] Underfitting and Overfitting
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[Capital One] Feature Selection
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[Amazon] Euclidean and Manhattan Distances
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[Capital One] Handling Missing Values
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[Google] Regression Model Metrics
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[Amazon] Hyperparameter Tuning
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[Uber] L1 & L2 Regularization
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[Salesforce] Outliers in Modeling
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[C3.ai] Model Building End-To-End
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[Meta] Feature Engineering
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[Amazon] Handling Categorical Variables
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[Amazon] Model Ensembling
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[Microsoft] Text Features
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2
ML Algorithm
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[Intuit] Logistic Regression Pseudocode
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[Meta] Decision Tree Pseudocode
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[PayPal] Random Forest vs Logistic Regression
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[Amazon] Bagging vs Boosting
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[Meta] How Is the random forest model trained?
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[Intuit] Variance & Bias in Trees
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[Salesforce] K-Means on Scaled and Unscaled Data
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[Microsoft] Principal Component Analysis
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[Microsoft] K-Means
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[Microsoft] Finding Optimal K in K-Means
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[Google] How do you evaluate regression modeling?
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[Apple] KNN Pseudocode
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[Amazon] GBTs Pseudocode
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[Amazon] GBTs Hyperparameters
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3
Deep Learning
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[PayPal] Activation Functions
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[Google] Normalization
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[Google] Optimizers
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[Google] Tuning Dense Neural Networks
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[Microsoft] Gradient Descent
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[Microsoft] Backpropagation
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[Amazon] Neural Network Overfitting
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[Intuit] Encoder-Decoder
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[Google] RNN vs CNN
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4
ML Production
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[PayPal] Prediction Point
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[Google] Online vs Offline Model Performance
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[Intuit] Model Deployment on AWS
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[Capital One] Model Deployment Challenges
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[Capital One] Model Failure in Production
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[Intuit] Kubernetes in Model Deployment
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[Intuit] Model Orchestration
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