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Supervised ML: Classification

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This course teaches aspiring data scientists to train and compare classification models using supervised machine learning techniques, focusing on practical applications and best practices.

Key AI Functions:Ensemble Learning, Machine Learning (ML) Algorithms, Supervised Learning, Classification Algorithms, Decision Tree

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Description for Supervised ML: Classification

Learn to train predictive models for the classification of categorical outcomes and compare models using error metrics, with a particular emphasis on classification in supervised machine learning.

Acquire practical experience in the application of classification best practices, including the management of unbalanced datasets and the implementation of train-test divisions.

Become proficient in a variety of techniques, such as alternative ensemble algorithms, tree-ensemble models, decision trees, and logistic regression, as well as error metrics for model comparison.

This course is designed for individuals who are interested in becoming data scientists and possess a fundamental understanding of Python programming, calculus, linear algebra, probability, statistics, and exploratory data analysis.

Level: Intermediate

Certification Degree: Yes

Languages the Course is Available: 22

Offered by: On Coursera provided by IBM

Duration: 24 hours (approximately)

Schedule: Flexible

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