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Course Outline

Introduction to Applied Machine Learning

  • Statistical learning compared to Machine learning
  • Iterative processes and evaluation methods
  • Understanding the Bias-Variance trade-off

Supervised and Unsupervised Learning

  • Programming languages, types, and examples in Machine Learning
  • Distinctions 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
  • Concepts of generalization and nonlinearity
  • Practical exercises

Classification

  • Review of Bayesian concepts
  • Naive Bayes algorithm
  • Logistic regression
  • K-Nearest neighbors
  • Practical exercises

Cross-validation and Resampling

  • Different cross-validation approaches
  • The Bootstrap method
  • Practical exercises

Unsupervised Learning

  • K-means clustering
  • Illustrative examples
  • Challenges in unsupervised learning and methods beyond K-means

Neural networks

  • Structure of layers and nodes
  • Python libraries for neural networks
  • Working with scikit-learn
  • Working with PyBrain
  • Deep Learning concepts

Requirements

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

 28 Hours

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