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Course Outline

Introduction to Applied Machine Learning

  • Comparing statistical learning and machine learning
  • Iterative processes and evaluation strategies
  • Understanding the Bias-Variance trade-off

Supervised and Unsupervised Learning

  • Overview of Machine Learning languages, types, and examples
  • Distinguishing between supervised and unsupervised learning

Supervised Learning

  • Decision Trees
  • Random Forests
  • Evaluating models

Machine Learning with Python

  • Selecting appropriate libraries
  • Utilizing supplementary 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 techniques
  • 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
  • Implementing with scikit-learn
  • Implementing with PyBrain
  • Introduction to Deep Learning

Requirements

Familiarity with the Python programming language is required. A basic understanding of statistics and linear algebra is highly recommended.

 28 Hours

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