Machine Learning with Imbalanced Data

Name: Machine Learning with Imbalanced Data
Rating: 4.7 (550 reviews)

Learn to over-sample and under-sample your data, apply SMOTE, ensemble methods, and cost-sensitive learning.

(550 ratings)

6,241 students

Created by Soledad Galli

Last updated 1/2023

English

English [Auto], Vietnamese [Auto]

What you'll learn

Apply random under-sampling to remove observations from majority classes
Perform under-sampling by removing observations that are hard to classify
Carry out under-sampling by retaining observations at the boundary of class separation
Apply random over-sampling to augment the minority class
Create syntethic data to increase the examples of the minority class
Implement SMOTE and its variants to synthetically generate data
Use ensemble methods with sampling techniques to improve model performance
Change the miss-classification cost optimized by the models to accomodate minority classes
Determine model performance with the most suitable metrics for imbalanced datasets

Requirements

Knowledge of machine learning basic algorithms, i.e., regression, decision trees and nearest neighbours
Python programming, including familiarity with NumPy, Pandas and Scikit-learn
A Python and Jupyter notebook installation

Description

Welcome to Machine Learning with Imbalanced Datasets. In this course, you will learn multiple techniques which you can use with imbalanced datasets to improve the performance of your machine learning models.

If you are working with imbalanced datasets right now and want to improve the performance of your models, or you simply want to learn more about how to tackle data imbalance, this course will show you how.

We'll take you step-by-step through engaging video tutorials and teach you everything you need to know about working with imbalanced datasets. Throughout this comprehensive course, we cover almost every available methodology to work with imbalanced datasets, discussing their logic, their implementation in Python, their advantages and shortcomings, and the considerations to have when using the technique. Specifically, you will learn:

Under-sampling methods at random or focused on highlighting certain sample populations
Over-sampling methods at random and those which create new examples based of existing observations
Ensemble methods that leverage the power of multiple weak learners in conjunction with sampling techniques to boost model performance
Cost sensitive methods which penalize wrong decisions more severely for minority classes
The appropriate metrics to evaluate model performance on imbalanced datasets

By the end of the course, you will be able to decide which technique is suitable for your dataset, and / or apply and compare the improvement in performance returned by the different methods on multiple datasets.

This comprehensive machine learning course includes over 50 lectures spanning more than 10 hours of video, and ALL topics include hands-on Python code examples which you can use for reference and for practice, and re-use in your own projects.

In addition, the code is updated regularly to keep up with new trends and new Python library releases.

So what are you waiting for? Enroll today, learn how to work with imbalanced datasets and build better machine learning models.

Who this course is for:

Data scientists and machine learning engineers working with imbalanced datasets
Data scientists who want to improve the performance of models trained on imbalanced datasets
Students who want to learn intermediate content on machine learning
Students working with imbalanced multi-class targets

Featured review

Eyas A.

75 courses

33 reviews

a year ago

Excellent course. This course prepares you for the real data you will work on in the real world. The instructor did a great job with explaining and simplifying all the topics. Highly recommended if you want to be professional and efficient when working with datasets. The notebooks and the slides will be an important reference for me in the future. Thank you!

Soledad Galli

Data scientist | Instructor | Software developer

4.6 Instructor Rating
10,211 Reviews
48,585 Students
7 Courses

Hey, I am Sole. I am a data scientist and open-source Python developer with a passion for teaching and programming.

I started as a research scientist in biology; I did a PhD and many years of postdoctoral work, and then I moved on to data science. I worked in finance and insurance companies, creating and putting into production machine learning models to assess credit risk, insurance claims, and prevent fraud.

I received a Data Science Leaders Award in 2018 and was selected as one of "LinkedIn’s voices" in data science and analytics in 2019. I love sharing knowledge about data science and machine learning, probably a consequence of my years in academia. This is why I teach online and create and contribute to open-source software.

I am the creator and maintainer of Feature-engine, an open-source Python library for feature engineering and selection. I also write articles online, speak at data science meetings, and podcasts.

I've got an MSc in Biology, a PhD in Biochemistry, and 8+ years of experience as a research scientist at well-known institutions like University College London and the Max Planck Institute.

I've also taught at the university. For more than four years, I taught biochemistry at the University of Buenos Aires and mentored MSc and PhD students.

Feel free to contact me on LinkedIn.