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Supervised Learning: Classification
AWS Training Pass
Take advantage of flexible training options with the AWS Training Pass and get Authorized AWS Training for a full year.
This course introduces you to one of the main types of modeling families of supervised Machine Learning: Classification. You will learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models. The hands-on section of this course focuses on using best practices for classification, including train and test splits, and handling data sets with unbalanced classes.
By the end of this course you should be able to:
- 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.
This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Classification techniques in a business setting.
To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.
1. Logistic Regression
2. K Nearest Neighbors
3. Support Vector Machines
4. Decision Trees
5. Ensemble Models
6. Modeling Unbalanced Classes
Self-Paced Training Info
Learn at your own pace with anytime, anywhere training
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Self-Paced Training Terms & Conditions
IBM Web Based Training courses are sold on a per-user basis. WBT courses provide a training advantage for you and your teams, helping you get up to speed quickly. Take the courses you need, at your convenience and at your own pace.
You can start the course at any time within 12 months of enrolling for the course. After you register/start the course, you have 30 days to complete your course. Within this 30 days, the self-paced format gives you the opportunity to complete the course at your convenience, at any location, and at your own pace. The course is available 24 hours a day.
System RequirementsTo participate in this course, the student workstation must meet the following hardware requirements:
- Minimum of 256 MB of memory
- Windows Vista or Windows 7-10 (32-bit or 64-bit edition)
- Internet Explorer 6 or higher or Firefox ESR
- 128-bit encryption
- Citrix Receiver for connection to the IBM Remote Lab Platform
- Access to Internet with at least 128 kbps down and 128 kbps up
- Other platforms/combinations may work but are not officially supported by IBM.
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