Applied Machine Learning: Foundations

Offered By: LinkedIn Learning

Tags

Machine Learning Courses Python Courses Linear Regression Courses scikit-learn Courses Decision Trees Courses Exploratory Data Analysis Courses Random Forests Courses Model Evaluation Courses Hyperparameter Tuning Courses

Course Description

Overview

Develop foundational skills and technical know-how for dealing with real-world problems using the Python ecosystem.

Syllabus

Introduction

Mastering machine learning essentials
What you should know

1. Introduction to Machine Learning

Overview of types of machine learning
Applications of ML
Tools for ML
Using GitHub Codespaces with this course

2. EDA

Exploring the dataset
Data preprocessing
Scikit-learn pipelines
Challenge: EDA plot
Solution: EDA plot

3. Model Creation

Dummy model
Linear regression
Decision trees
CatBoost
Challenge: Random forest pipeline
Solution: Random forest pipeline

4. Model Evaluation

R2
Root mean squared
Residual plot
Challenge: Evaluate random forest
Solution: Evaluate random forest

5. Model Tuning

Hyperparameters and linear regression
Tuning decision trees
Tuning CatBoost
Grid search
Challenge: Tuning random forest
Solution: Tuning random forest

6. Model Deployment

End-to-end notebook
Using MLFlow
Challenge: MLFlow with random forest
Solution: MLFlow with random forest

Conclusion

Next steps

Taught by

Derek Jedamski

Related Courses

Practical Machine Learning
Johns Hopkins University via Coursera Detección de objetos
Universitat Autònoma de Barcelona (Autonomous University of Barcelona) via Coursera Practical Machine Learning on H2O
H2O.ai via Coursera Modélisez vos données avec les méthodes ensemblistes
CentraleSupélec via OpenClassrooms Introduction to Machine Learning for Coders!
fast.ai via Independent