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 Duration 28 hours

Course Outline

Introduction to Applied Machine Learning

  • Distinction between statistical learning and Machine learning
  • Process of iteration and evaluation
  • The Bias-Variance trade-off

Supervised and Unsupervised Learning

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

Supervised Learning

  • Decision Trees
  • Random Forests
  • Evaluating model performance

Implementing Machine Learning in Python

  • Selecting appropriate libraries
  • Utilizing supplementary 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 technique
  • Practical exercises

Unsupervised Learning

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

Neural networks

  • Understanding layers and nodes
  • Python libraries for neural networks
  • Implementation using scikit-learn
  • Implementation using PyBrain
  • Introduction to Deep Learning

Requirements

A working knowledge of the Python programming language is required. While not mandatory, a basic understanding of statistics and linear algebra is highly recommended.

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