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Applied Machine Learning: Algorithms

via LinkedIn Learning

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.

In the first installment of the Applied Machine Learning series, instructor Derek Jedamski covered foundational concepts, providing you with a general recipe to follow to attack any machine learning problem in a pragmatic, thorough manner. In this course—the second and final installment in the series—Derek builds on top of that architecture by exploring a variety of algorithms, from logistic regression to gradient boosting, and showing how to set a structure that guides you through picking the best one for the problem at hand. Each algorithm has its pros and cons, making each one the preferred choice for certain types of problems. Understanding what actually drives each algorithm, as well as their benefits and drawbacks, can give you a significant competitive advantage as a data scientist.

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

Taught by

Derek Jedamski

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