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Course Outline

Introduction to Applied Machine Learning

  • Statistical learning versus Machine Learning
  • Iteration and evaluation processes
  • The Bias-Variance trade-off

Supervised Learning and Unsupervised Learning

  • Machine Learning languages, types, and use cases
  • Differences between Supervised and Unsupervised Learning

Supervised Learning

  • Decision Trees
  • Random Forests
  • Model Evaluation techniques

Machine Learning with Python

  • Selecting appropriate libraries
  • Essential add-on tools

Regression

  • Linear regression
  • Generalizations and handling Nonlinearity
  • Practical Exercises

Classification

  • Refresher on Bayesian concepts
  • Naive Bayes
  • Logistic regression
  • K-Nearest neighbors
  • Practical Exercises

Cross-validation and Resampling

  • Different Cross-validation approaches
  • Bootstrap methods
  • Practical Exercises

Unsupervised Learning

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

Neural Networks

  • Layers and nodes
  • Python neural network libraries
  • Utilising scikit-learn
  • Utilising PyBrain
  • Deep Learning concepts

Requirements

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

 28 Hours

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