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Course Outline

Introduction to Applied Machine Learning

  • Distinguishing between statistical learning and Machine Learning
  • Processes of iteration and evaluation
  • The Bias-Variance trade-off

Supervised and Unsupervised Learning

  • Machine Learning languages, types, and illustrative examples
  • Comparing supervised and unsupervised learning

Supervised Learning

  • Decision Trees
  • Random Forests
  • Evaluating model performance

Machine Learning with Python

  • Selecting appropriate libraries
  • Utilising additional tools

Regression

  • Linear regression
  • Generalisations and handling nonlinearity
  • Practical exercises

Classification

  • Review of Bayesian concepts
  • Naive Bayes
  • Logistic regression
  • K-Nearest Neighbours
  • Practical exercises

Cross-validation and Resampling

  • Different approaches to cross-validation
  • Bootstrap methods
  • Practical exercises

Unsupervised Learning

  • K-means clustering
  • Case studies
  • Challenges in unsupervised learning and methods beyond K-means

Neural Networks

  • Understanding layers and nodes
  • Neural network libraries in Python
  • Implementing models with scikit-learn
  • Using PyBrain
  • Introduction to Deep Learning

Requirements

Proficiency in the Python programming language is required. A foundational understanding of statistics and linear algebra is also recommended.

 28 Hours

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