CTECH463 - Training Common Machine Learning Models

Course Description

After data has been prepared, the next step in the machine learning lifecycle is model training and evaluation. In this course, you will focus on the model training and evaluation process for supervised learning models and explore a few supervised learning algorithms that are commonly used. You will be introduced to the model training for two popular supervised learning algorithms: k-nearest neighbors (KNN) and decision trees (DT), exploring their applicability to classification problems. You will practice creating your own machine learning models using a popular Python package for machine learning called scikit-learn. By the end of this course, you will have new, applicable skills in training common ML models.

You are required to have completed the following courses or have equivalent experience before taking this course:

Machine Learning Foundations
Managing Data in Machine Learning

Faculty Author

Brian D'Alessandro

Benefits to the Learner

Define the core foundational elements of model training and evaluation
Develop intuition for different classes of algorithms
Analyze the mechanics of two popular supervised learning algorithms: decision trees and k-nearest neighbors
Develop intuition on tradeoffs between different algorithmic choices

Target Audience

Data scientists and data analysts
Programmers, developers, and software engineers
Statisticians
Product managers
Entrepreneurs
Working professionals seeking to upskill or career change

Applies Towards the Following Certificates

Applied Machine Learning and AI : Core Courses

$999.00