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Course Outline

Introduction to Applied Machine Learning

  • Statistical learning compared to Machine learning
  • The process of iteration and evaluation
  • Understanding the Bias-Variance trade-off

Supervised and Unsupervised Learning

  • Languages, types, and examples in Machine Learning
  • Distinguishing between Supervised and Unsupervised Learning

Supervised Learning

  • Decision Trees
  • Random Forests
  • Evaluating models

Machine Learning with Python

  • Selecting appropriate libraries
  • Utilising add-on tools

Regression

  • Linear regression
  • Generalisations and nonlinearity
  • Practical exercises

Classification

  • Refresher on Bayesian concepts
  • Naive Bayes
  • Logistic regression
  • K-Nearest neighbours
  • Practical exercises

Cross-validation and Resampling

  • Various Cross-validation approaches
  • Bootstrap techniques
  • Practical exercises

Unsupervised Learning

  • K-means clustering
  • Practical examples
  • Challenges in unsupervised learning and methods beyond K-means

Neural Networks

  • Understanding layers and nodes
  • Python libraries for neural networks
  • Implementing solutions with scikit-learn
  • Working with PyBrain
  • Deep Learning

Requirements

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

 28 Hours

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