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Course Outline

Introduction to Applied Machine Learning

  • Distinguishing statistical learning from Machine learning
  • Iteration and evaluation processes
  • The Bias-Variance trade-off

Supervised Learning and Unsupervised Learning

  • Overview of Machine Learning languages, types, and examples
  • Contrasting 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

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

Cross-validation and Resampling

  • Various Cross-validation strategies
  • Bootstrap methods
  • Practical Exercises

Unsupervised Learning

  • K-means clustering
  • Illustrative Examples
  • Challenges in unsupervised learning and alternatives to K-means

Neural networks

  • Understanding layers and nodes
  • Python libraries for neural networks
  • Implementation with scikit-learn
  • Implementation with PyBrain
  • Deep Learning fundamentals

Requirements

Familiarity with the Python programming language is required. A foundational understanding of statistics and linear algebra is also advised.

 28 Hours

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