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If you are interested in Machine Learning, these FREE courses from IBM make learning easy and accessible for everyone
1. Supervised Machine Learning: Regression. In this course you are going to learn about:
* Differentiate uses and applications of classification and regression in the context of supervised machine learning
* Describe and use linear regression models
* Use a variety of error metrics to compare and select a linear regression model that best suits your data
* Articulate why regularization may help prevent overfitting
* Use regularization regressions: Ridge, LASSO, and Elastic net
2. Supervised Machine Learning: Classification. In this course you are going to learn about:
Differentiate uses and applications of classification and classification ensembles
* Describe and use logistic regression models
* Describe and use decision tree and tree-ensemble models
* Describe and use other ensemble methods for classification
* Use a variety of error metrics to compare and select the classification model that best suits your data
* Use oversampling and undersampling as techniques to handle unbalanced classes in a data set
3. Unsupervised Machine Learning. In this course you are going to learn about:
* Explain the kinds of problems suitable for Unsupervised Learning approaches
* Explain the curse of dimensionality, and how it makes clustering difficult with many features
* Describe and use common clustering and dimensionality-reduction algorithms
* Try clustering points where appropriate, compare the performance of per-cluster models
* Understand metrics relevant for characterizing clusters
What would you like to learn next?
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