Supervised Learning: Classification

IBM Training for Supervised Learning: Classification

Skill Level: Intermediate

Modality: WBT - Web Based Training - Self Paced

Duration: 1.4 Day/s

Starting Price: $ - Contact for price

Overview:

This course introduces you to one of the main types of modeling families of supervised Machine Learning: Classification.

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.

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Target Audience:

This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Classification techniques in a business setting..

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Prerequisites: 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..

Topic: 1. Logistic Regression2. K Nearest Neighbors3. Support Vector Machines4. Decision Trees5. Ensemble Models6. Modeling Unbalanced Classes.

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Objective: 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.

Category: Data, Analytics, and AI

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Brand: Watson AI

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