Applied Machine Learning: Algorithms

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Free Online Course: Applied Machine Learning: Algorithms provided by LinkedIn Learning is a comprehensive online course, which lasts for 2-3 hours worth of material. The course is taught in English and is free of charge. Upon completion of the course, you can receive an e-certificate from LinkedIn Learning. Applied Machine Learning: Algorithms is taught by Derek Jedamski.

Overview
  • Learn how machine learning algorithms work. Explore a variety of algorithms and learn how to set a structure that guides you through picking the best one for the problem at hand.

Syllabus
  • Introduction

    • The power of algorithms in machine learning
    • What you should know
    • What tools you need
    • Using the exercise files
    1. Review of Foundations
    • Defining model vs. algorithm
    • Process overview
    • Clean continuous variables
    • Clean categorical variables
    • Split into train, validation, and test set
    2. Logistic Regression
    • What is logistic regression?
    • When should you consider using logistic regression?
    • What are the key hyperparameters to consider?
    • Fit a basic logistic regression model
    3. Support Vector Machines
    • What is Support Vector Machine?
    • When should you consider using SVM?
    • What are the key hyperparameters to consider?
    • Fit a basic SVM model
    4. Multi-layer Perceptron
    • What is a multi-layer perceptron?
    • When should you consider using a multi-layer perceptron?
    • What are the key hyperparameters to consider?
    • Fit a basic multi-layer perceptron model
    5. Random Forest
    • What is Random Forest?
    • When should you consider using Random Forest?
    • What are the key hyperparameters to consider?
    • Fit a basic Random Forest model
    6. Boosting
    • What is boosting?
    • When should you consider using boosting?
    • What are the key hyperparameters to consider boosting?
    • Fit a basic boosting model
    7. Summary
    • Why do you need to consider so many different models?
    • Conceptual comparison of algorithms
    • Final model selection and evaluation
    Conclusion
    • Next steps