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Course Outline

Introduction to Applied Machine Learning

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

Supervised Learning and Unsupervised Learning

  • Overview of Machine Learning languages, types, and examples
  • Comparing 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
  • Exploring Generalizations and Nonlinearity
  • Exercises

Classification

  • Bayesian statistics refresher
  • Naive Bayes
  • Logistic regression
  • K-Nearest neighbors
  • Exercises

Cross-validation and Resampling

  • Approaches to Cross-validation
  • The Bootstrap method
  • 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
  • Working with scikit-learn
  • Working with PyBrain
  • Introduction to Deep Learning

Requirements

A working knowledge of the Python programming language is required. Basic familiarity with statistics and linear algebra is recommended.

 28 Hours

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