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Foundations

Foundations of Regression0/10
  • Designing a Learning System & the Types of Machine Learning
  • Linear regression: lines, SSR, and gradient descent
  • Why do we need logistic regression?
  • The sigmoid: the secret sauce of logistic regression
  • Logistic regression and decision boundaries
  • Intuition behind logistic regression
  • Log likelihood instead of squared error
  • Performance Metrics: Grading Classification & Regression Models
  • Interview Readiness, Foundations of Regression
Tree-Based Algorithms0/16
  • Decision Trees: impurity, splitting, and the CART and ID3 algorithms
  • Overfitting, Bias-Variance & Cross-Validation
  • Random Forests & Bagging
  • Boosting: AdaBoost & Gradient Boosting
  • Bayes' Theorem and Concept Learning
  • The Naive Bayes Classifier
  • The Bayes-Optimal Classifier and the Gibbs Algorithm
  • Bayesian Belief Networks
  • The EM Algorithm
  • Support Vector Machines: Hyperplanes, Margins, and Kernels
  • Non-Parametric Regression: Locally Weighted Regression and K-Nearest Neighbours
  • Unsupervised Learning & Clustering: Distances, k-Means, and Hierarchies
  • DBSCAN & Cluster Quality: Density Clustering, Silhouette & the Rand Index
  • Dimensionality Reduction: PCA, SVD, and t-SNE
  • Population-Based Optimization & Reinforcement Learning
Deep Neural Networks0/12
  • From linear regression to the perceptron
  • Layers in a deep neural network
  • Activation functions
  • Loss functions
  • How neural networks make predictions (forward pass)
  • How neural networks learn (backward pass)
  • Trainable parameters and hyperparameters
  • Overfitting in neural networks
  • Neural network architecture (binary classification recap)
  • Neural networks from scratch (NumPy)
  • Interview Readiness, Deep Neural Networks
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Support Vector Machines: Hyperplanes, Margins, and Kernels

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