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@shmVirus

Machine Learning

Supervised and unsupervised learning, model selection, regularisation, neural networks, and evaluation — building systems that learn from data.

Machine learning turns data into models. This course covers supervised learning — regression, classification, decision trees, SVMs — unsupervised learning, neural networks, regularisation, and model evaluation.

Outcomes

  • Train and evaluate supervised models on tabular data
  • Detect and address overfitting using regularisation and cross-validation
  • Implement gradient descent and explain its convergence behaviour
  • Choose appropriate evaluation metrics for classification and regression

Outline

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  1. 01
    Supervised LearningRegression and classification, the bias-variance trade-off, and the training/validation/test split.
  2. 02
    Unsupervised LearningK-means clustering, hierarchical clustering, and PCA for dimensionality reduction.
  3. 03
    Neural NetworksPerceptrons, multilayer networks, backpropagation, and activation functions.
  4. 04
    Model EvaluationAccuracy, precision, recall, F1, ROC curves, and cross-validation for reliable evaluation.
  5. 05
    Regularisation and OptimisationL1 and L2 regularisation, gradient descent variants, and learning rate schedules.