Engineering and Technology
Create new features to improve the performance of your Machine Learning models.
In today's rapidly evolving world, machine learning applications are constantly making groundbreaking advancements. However, it is often overlooked that a significant amount of data manipulation and feature engineering is required before these sophisticated models can be effectively utilized. This course aims to equip you with the necessary skills to perform these crucial tasks. Throughout the course, you will delve into the Stack Overflow Developers survey and historic US presidential inauguration addresses. By doing so, you will gain a comprehensive understanding of the most effective methods for preprocessing and engineering features from various types of data, including categorical, continuous, and unstructured data. By participating in this course, you will gain valuable hands-on experience in preparing data for your own machine learning models. This practical knowledge will empower you to confidently tackle any dataset and optimize it for successful machine learning applications.
by DataCamp
Create new features to improve the performance of your Machine Learning models.
by DataCamp
Learn techniques to extract useful information from text and process them into a format suitable for...
by DataCamp
This course focuses on feature engineering and machine learning for time series data.
by DataCamp
Learn how to approach and win competitions on Kaggle.
by DataCamp
Learn how to manipulate data and create machine learning feature sets in Spark using SQL in Python.
by DataCamp
Julia is a new programming language designed to be the ideal language for scientific computing, mach...
by DataCamp
In this course you'll learn how to get your cleaned data ready for modeling.
by DataCamp
Learn to build pipelines that stand the test of time.
by DataCamp
Learn the principles of feature engineering for machine learning models and how to implement them us...
by DataCamp
Learn the gritty details that data scientists are spending 70-80% of their time on; data wrangling a...