YoVDO

Applied Machine Learning: Algorithms

Offered By: LinkedIn Learning

Tags

Machine Learning Courses Linear Regression Courses scikit-learn Courses Logistic Regression Courses Decision Trees Courses Clustering Courses Random Forests Courses K-means Courses Principal Component Analysis Courses XGBoost Courses

Course Description

Overview

Learn about common machine learning algorithms, their pros and cons, and develop hands-on skills to leverage them.

Syllabus

Introduction
  • Applied machine learning: Algorithms
  • What you should know
1. Clustering
  • K-means
  • K evaluation
  • Understanding clusters
  • Other algorithms
  • Challenge: Apply KNN
  • Solution: Apply KNN
2. PCA
  • PCA
  • Structure of components
  • Components
  • Scatter plot
  • Other algorithms
  • Challenge: Utilize PCA
  • Solution: Utilize PCA
3. Linear Regression
  • Linear regression algorithm
  • scikit-learn
  • Real-world example
  • Assumptions
  • Challenge: Develop a linear regression model
  • Solution: Develop a linear regression model
4. Logistic Regression
  • Logistic regression algorithm
  • Basic example
  • Assumptions
  • Challenge: Construct a logistic regression model
  • Solution: Construct a logistic regression model
5. Decision Trees
  • Decision tree algorithm
  • Real-world example
  • Random Forest and XGBoost
  • Challenge: Design a decision tree model
  • Solution: Design a decision tree model
Conclusion
  • Next steps

Taught by

Derek Jedamski

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