Machine Learning with Imbalanced Data
What you'll learn
- Under-sampling methods at random
- Under-sampling methods which focus on observations that are harder to classify
- Under-sampling methods that ignore potentially noisy observations
- Over-sampling methods to increase the number of minority observations
- Ways of creating syntethic data to increase the examples of the minority class
- SMOTE and its variants
- Use ensemble methods with sampling techniques to improve model performance
- The most suitable evaluation metrics to use with imbalanced datasets
Course content
- Preview03:16
- Preview03:13
- Preview02:01
- 00:15Code | Jupyter notebooks
- 00:04Presentations covered in the course
- 00:07Python package Imbalanced-learn
- 00:08Download Datasets
- 00:47Additional resources for Machine Learning and Python programming
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
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 about 8 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
Instructor
![Soledad Galli](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 4.6 Instructor Rating
- 5,805 Reviews
- 25,125 Students
- 6 Courses
Soledad Galli is a lead data scientist and founder of Train in Data. She has experience in finance and insurance, received a Data Science Leaders Award in 2018 and was selected “LinkedIn’s voice” in data science and analytics in 2019. Sole is passionate about sharing knowledge and helping others succeed in data science.
As a data scientist in Finance and Insurance companies, Sole researched, developed and put in production machine learning models to assess Credit Risk, Insurance Claims and to prevent Fraud, leading in the adoption of machine learning in the organizations.
Sole is passionate about empowering people to step into and excel in data science. She mentors data scientists, writes articles online, speaks at data science meetings, and teaches online courses on machine learning.
Sole has recently created Train In Data, with the mission to facilitate and empower people and organizations worldwide to step into and excel in data science and analytics.
Sole has an MSc in Biology, a PhD in Biochemistry and 8+ years of experience as a research scientist in well-known institutions like University College London and the Max Planck Institute. She has scientific publications in various fields such as Cancer Research and Neuroscience, and her research was covered by the media on multiple occasions.
Soledad has 4+ years of experience as an instructor in Biochemistry at the University of Buenos Aires, taught seminars and tutorials at University College London, and mentored MSc and PhD students at Universities.
Feel free to contact her on LinkedIn.
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Soledad Galli es científica de datos y fundadora de Train in Data. Tiene experiencia en finanzas y seguros, recibió el premio Data Science Leaders Award en 2018 y fue seleccionada como "la voz de LinkedIn" en ciencia y análisis de datos en 2019. A Soledad le apasiona compartir conocimientos y ayudar a otros a tener éxito en la ciencia de datos.
Como científica de datos en compañías de finanzas y seguros, Sole desarrolló y puso en producción modelos de aprendizaje automático para evaluar el riesgo crediticio, automatizar reclamos de seguros y para prevenir el fraude, facilitando la adopción del aprendizaje de máquina en estas organizaciones.
A Sole le apasiona ayudar a que las personas aprendan y se destaquen en ciencia de datos, es por eso habla regularmente en reuniones de ciencia de datos, escribe varios artículos disponibles en la web y crea cursos sobre aprendizaje de máquina.
Sole ha creado recientemente Train In Data, con la misión de ayudar a las personas y organizaciones de todo el mundo a que aprendan y se destaquen en la ciencia y análisis de datos.
Sole tiene una maestría en biología, un doctorado en bioquímica y más de 8 años de experiencia como investigadora científica en instituciones prestigiosas como University College London y el Instituto Max Planck. Tiene publicaciones científicas en diversos campos, como la investigación contra el Cáncer y la Neurociencia, y sus resultados fueron cubiertos por los medios en múltiples ocasiones.
Soledad tiene más de 4 años de experiencia como instructora de bioquímica en la Universidad de Buenos Aires, dio seminarios y tutoriales en University College London, en Londres, y fue mentora de estudiantes de maestría y doctorado en diferentes universidades.
No dudes en contactarla en LinkedIn.