Here is the detailed syllabus for the Machine Learning tutorial. If you are looking for more information on machine learning, don't forget to check out our Machine Learning Basics guide.

Course Outline

  • Introduction to Machine Learning

    • What is Machine Learning?
    • Types of Machine Learning
    • Applications of Machine Learning

  • Data Preprocessing

    • Data Collection
    • Data Cleaning
    • Data Transformation

  • Supervised Learning

    • Linear Regression
    • Logistic Regression
    • Decision Trees
    • Random Forest
    • Support Vector Machines

  • Unsupervised Learning

    • Clustering
    • Association Rules
    • Dimensionality Reduction

  • Reinforcement Learning

    • Markov Decision Processes
    • Q-Learning
    • Policy Gradient Methods

Learning Resources

  • Books

    • "Python Machine Learning" by Sebastian Raschka
    • "The Hundred-Page Machine Learning Book" by Andriy Burkov

  • Online Courses

    • Coursera: Machine Learning by Andrew Ng
    • edX: Introduction to Machine Learning by MIT

Practice Projects

  • Build a Simple Linear Regression Model
  • Predict House Prices Using Decision Trees
  • Analyze Customer Churn with Clustering

Machine Learning Workflow

For more detailed information on machine learning, you can also visit our Machine Learning Advanced Topics section.