This course teaches you how to build end-to-end machine learning applications. It covers topics such as feature engineering, intermediate machine learning models, and recommender systems.
This course explores various ensemble techniques, including bagging, boosting, and stacking, to improve the performance of your machine learning models.
An interactive course that teaches the fundamentals of EDA in Python, covering summary statistics, data visualization, and preparing data for machine learning models.
Learn how to use GitHub Copilot to streamline your workflow and development. This course provides an in-depth understanding of the AI-driven coding assistant.
An interactive, hands-on course where you learn to build and use decision trees and random forests, two powerful supervised machine learning models. Includes projects to solidify your understanding.
This career path equips you with the skills to manage data, get results, and drive decision-making. It includes modules on data wrangling and cleaning with Python.
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