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 Duration 28 hours

Course Outline

Introduction to Applied Machine Learning

  • Distinguishing between Statistical learning and Machine learning
  • Processes of iteration and evaluation
  • The Bias-Variance trade-off

Supervised vs. Unsupervised Learning

  • Overview of Machine Learning Languages, Types, and Examples
  • Comparing Supervised and Unsupervised Learning

Supervised Learning

  • Decision Trees
  • Random Forests
  • Evaluating Model Performance

Implementing Machine Learning in Python

  • Selecting appropriate libraries
  • Utilizing auxiliary tools

Regression

  • Linear regression
  • Generalizations and handling Nonlinearity
  • Practical Exercises

Classification

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

Cross-validation and Resampling

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

Unsupervised Learning

  • K-means clustering
  • Case studies
  • Challenges in unsupervised learning and alternatives to K-means

Neural Networks

  • Understanding Layers and nodes
  • Neural network libraries in Python
  • Implementing models 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 also advised.

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