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Course Outline

Introduction to Applied Machine Learning

  • Statistical learning versus Machine learning
  • Iteration and evaluation processes
  • Understanding the Bias-Variance trade-off

Supervised Learning and Unsupervised Learning

  • Machine Learning languages, types, and examples
  • Differences between Supervised and Unsupervised Learning

Supervised Learning

  • Decision Trees
  • Random Forests
  • Model Evaluation techniques

Machine Learning with Python

  • Selecting appropriate libraries
  • Utilizing add-on tools

Regression

  • Linear regression
  • Generalizations and Nonlinearity
  • Hands-on exercises

Classification

  • Bayesian concepts review
  • Naive Bayes
  • Logistic regression
  • K-Nearest neighbors (KNN)
  • Hands-on exercises

Cross-validation and Resampling

  • Cross-validation methodologies
  • Bootstrap techniques
  • Hands-on exercises

Unsupervised Learning

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

Neural networks

  • Architecture: Layers and nodes
  • Python-based neural network libraries
  • Integrating with scikit-learn
  • Integrating with PyBrain
  • Introduction to Deep Learning

Requirements

Participants should have knowledge of the Python programming language. Familiarity with basic statistics and linear algebra is recommended.

 28 Hours

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