Udemy – Machine Learning with Imbalanced Data 2022-3 – Download

explanation

Machine Learning with Imbalanced Data is a course on techniques for handling imbalanced data and improving the efficiency of machine learning models. If you are currently working with imbalanced data sets and want to improve model performance or learn techniques to help you deal with data imbalance, this course is for you. Our fun, educational videos will teach you step-by-step everything you need to know about working with imbalanced data sets. This comprehensive course teaches all the methodologies available for working with imbalanced datasets and discusses the logic of how they work, how to use them in Python, and their pros and cons.

Here’s what you’ll learn in the Machine Learning with Imbalanced Data course:

Random undersampling method
It is a “low sampling” method that focuses on observations that are difficult to classify.
Low sampling method to increase the number of minority observations.
How to build dummy data to increase minority class examples
SMOTE and its variables
We use sampling techniques and group methods to improve model efficiency.
Best evaluation criteria for use with imbalanced data sets

Course specifications

Publisher: Udemy
teacher: Soledad Galli
Language:English
Level: average
Lesson: 129
Duration: 11 hours 24 minutes

June 2022 Machine Learning with Imbalanced Data

Machine learning using imbalanced data

Course Prerequisites:

Knowledge of machine learning basic algorithms (e.g. regression, decision trees, and nearest neighbors).

Python programming, including knowledge of NumPy, Pandas, and Scikit-learn.

movie

Machine learning using imbalanced data

sample film

installation manual

After extracting, watch with your favorite players.

English subtitles

Quality: 720

Changes:

The 2022/3 version has increased to 25 classes and 2 hours and 57 minutes of class time compared to 2021/1.