Summary

Using SciKit-learn for Artificial Intelligence and Machine Learning

The first module of this course introduces some fundamentals of AI and ML including their relationship, different types of data, training and testing, common types of learning techniques (supervised and unsupervised learning) and applications (regression, classification, and clustering). The second module introduces some commonly used machine learning algorithms.

Registration Overview

Prerequisites: Basic python programming skills

Students who complete the course will earn 3 PDH.
Please direct questions about the course to Help@hprc.tamu.edu. For questions related to the registration process, please contact TEES EDGE.

Learning Description

After completing this online, asynchronous course, students will

understand the fundamentals of AI and ML
implement linear regression, logistic regression
implement Support Vector Machine (SVM) and K-Means clustering algorithms with Scikit-learn machine learning library

Target Audience

Students with interest in machine learning with Scikit-learn

Sponsors

Since 1876, Texas A&M Engineering has awarded more than 116,000 degrees to talented problem solvers who have helped to change the world. TEES EDGE is Texas A&M’s access point for lifelong engineering education and innovation for professionals.

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