Get in Touch

Course Outline

Introduction to Applied Machine Learning

  • Distinguishing between Statistical learning and Machine learning
  • Iteration and evaluation processes
  • The Bias-Variance trade-off

Supervised Learning and Unsupervised Learning

  • Machine Learning Languages, Types, and Examples
  • Contrasting Supervised and Unsupervised Learning

Supervised Learning

  • Decision Trees
  • Random Forests
  • Model Evaluation

Machine Learning with Python

  • Selecting appropriate libraries
  • Utilizing Add-on tools

Regression

  • Linear regression
  • Generalizations and Nonlinearity
  • Practical Exercises

Classification

  • Bayesian fundamentals review
  • Naive Bayes
  • Logistic regression
  • K-Nearest neighbors
  • Practical Exercises

Cross-validation and Resampling

  • Different Cross-validation strategies
  • Bootstrap methods
  • Practical Exercises

Unsupervised Learning

  • K-means clustering
  • Real-world Examples
  • Challenges in unsupervised learning and techniques beyond K-means

Neural networks

  • Understanding Layers and nodes
  • Python libraries for neural networks
  • Application with scikit-learn
  • Application with 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

Number of participants


Price per participant

Testimonials (7)

Upcoming Courses

Related Categories