Practical Machine Learning

Johns Hopkins University’s practical machine learning course teaches students how to create and assess machine learning prediction models using actual data. Training and test datasets, cross-validation, overfitting, feature engineering, preprocessing, and machine learning techniques including regression, classification trees, Naive Bayes, and random forests are all covered in the course. Additionally, students complete projects aimed at building predictive models and obtain hands-on experience with the R programming language.

Provider/Creator: Johns Hopkins University
Platform: Coursera
Category: Applied Machine Learning
Level: Intermediate
Duration: ~34 hours
Certificate: Yes (Paid certificate, free audit available)
Rating: 4.6/5
Direct Course Link: Practical Machine Learning
Recommended For: Data analysts, aspiring data scientists, R programmers, and professionals who want to learn practical machine learning techniques and predictive modeling