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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