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Course Outline

Introduction to Applied Machine Learning

  • Differences between statistical learning and Machine Learning
  • Iteration and evaluation processes
  • The Bias-Variance trade-off

Supervised and Unsupervised Learning

  • Overview of Machine Learning languages, types, and examples
  • Comparison of Supervised and Unsupervised Learning

Supervised Learning

  • Decision Trees
  • Random Forests
  • Model Evaluation

Implementing Machine Learning with Python

  • Selecting appropriate libraries
  • Essential add-on tools

Regression

  • Linear regression
  • Generalizations and Nonlinearity
  • Practical exercises

Classification

  • Bayesian concepts refresher
  • 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

  • Understanding layers and nodes
  • Neural network libraries in Python
  • Utilizing scikit-learn
  • Using PyBrain
  • Deep Learning

Requirements

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

 28 Hours

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