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Course Outline

Introduction to Applied Machine Learning

  • Distinguishing between statistical learning and Machine Learning
  • Processes of iteration and evaluation
  • Understanding the Bias-Variance trade-off

Supervised and Unsupervised Learning

  • Exploring Machine Learning languages, categories, and use cases
  • Comparing supervised and unsupervised learning paradigms

Supervised Learning

  • Decision Trees
  • Random Forests
  • Evaluating model performance

Machine Learning with Python

  • Selecting appropriate libraries
  • Utilizing supplementary tools

Regression

  • Linear regression
  • Generalizations and handling nonlinearity
  • Practical exercises

Classification

  • Reviewing Bayesian concepts
  • Naive Bayes
  • Logistic regression
  • K-Nearest neighbors
  • Practical exercises

Cross-validation and Resampling

  • Various cross-validation strategies
  • Bootstrap methods
  • Practical exercises

Unsupervised Learning

  • K-means clustering
  • Case studies
  • Addressing challenges in unsupervised learning and moving beyond K-means

Neural Networks

  • Understanding layers and nodes
  • Python libraries for neural networks
  • Using scikit-learn
  • Using PyBrain
  • Deep Learning

Requirements

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

 28 Hours

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