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ML: Concepts & Applications

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Learn to use Python and libraries for data tasks, understand key machine learning techniques, and apply them to real-world datasets for a strong research foundation.

Key AI Functions:Unsupervised Learning,Artificial Neural Network,Machine Learning,regression,Statistical Classification

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Description for ML: Concepts & Applications

Acquire the skills necessary to utilize Python and libraries such as Scikit-learn, TensorFlow, and Pandas for data ingestion, investigation, preparation, and modeling.
Support vector machines, decision trees and ensembles, clustering, PCA, hidden Markov models, deep learning, and linear regression are among the techniques that are employed to train and evaluate models.
Acquire a conceptual understanding of these techniques in order to understand the significance and reasoning behind the results.
Based on an introductory machine learning course from the University of Chicago, work with real-world datasets, primarily from public policy, to establish a foundation for advanced research.

Level: Intermediate

Certification Degree: Yes

Languages the Course is Available: 21

Offered by: On Coursera provided by The University of Chicago

Duration: 3 weeks at 12 hours a week

Schedule: Flexible

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