Supervised Machine Learning: Classification
Another excellent platform for studying Online Logistic Regression Courses is Classification. This course exposes you to one of the most common supervised Machine Learning modeling families: classification. You'll learn how to utilize error metrics to compare models and how to train predictive models to identify categorical outcomes. This course's hands-on component focuses on using recommended practices for classification, such as train and test splits, and dealing with data sets with unbalanced classes
You should be able to do the following by the end of this course:
- Different categorization and classification ensembles uses and applications
- Explain logistic regression models and how to utilize them.
- Explain decision trees and tree-ensemble models and how to use them.
- Explain and apply alternative classification ensemble approaches.
- Compare and pick the categorization model that best fits your data using a number of error metrics.
- To address unbalanced classes in a data set, use oversampling and undersampling approaches.
Who should enroll in this class?
This course is for aspiring data scientists who want to obtain hands-on experience in a corporate setting with Supervised Machine Learning Classification algorithms.
What abilities should you possess?
You should be familiar with Python programming and have a basic understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, probability, and Statistics in order to get the most out of this course.
THE SKILLS YOU WILL DEVELOP:
- Decision Tree
- Learning set
- Classification Algorithms
- Supervised Learning
- Machine Learning (ML) Algorithms
Rating: 4.8/5
Enroll here: coursera.org/learn/supervised-machine-learning-classification
