Machine Learning with Imbalanced Data

Machine Learning with Imbalanced Data. Machine learning is a field of artificial intelligence that uses algorithms to learn from data. When training the machine learning algorithm, it is important to have data that is representative of the target population. However, sometimes there is not enough data to train the algorithm properly. This is where machine learning with imbalanced data comes in.

With imbalanced data, there are too few examples of one category or another. This can be caused by a number of factors, such as a small sample size or an imbalance in the distribution of values within the population. When working with imbalanced data, it is important to take into account these limitations when designing and training the machine learning algorithm. By doing so, you can ensure that your model will be accurate and effective.

One common limitation with imbalanced data is that it can often be difficult to train a machine learning model that is accurate and effective. This is because the algorithm will likely be unable to generalize well to new data sets that contain an imbalance in the distribution of values. In order to overcome this limitation, it is often necessary to use techniques such as feature engineering or pre-processing of the data set.

  • 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 synthetic 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
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Machine Learning with Imbalanced Data Course 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

Machine Learning with Imbalanced Data Course 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.

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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.

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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.

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Who this course is for:

  • Data Scientists and Machine Learning engineers working with imbalanced datasets


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