Feature Engineering for Machine Learning in Python
Every day you read about the amazing breakthroughs in how the newest applications of machine learning are changing the world. Often this reporting glosses over the fact that a huge amount of data munging and feature engineering must be done before any of these fancy models can be used. In Feature Engineering for Machine Learning in Python course, you will learn how to do just that. You will work with Stack Overflow Developers survey, and historic US presidential inauguration addresses, to understand how best to preprocess and engineer features from categorical, continuous, and unstructured data. This course will give you hands-on experience on how to prepare any data for your own machine learning models.
In Feature Engineering for Machine Learning in Python course, you will explore what feature engineering is and how to get started with applying it to real-world data. You will be introduced to the reality of messy and incomplete data. You will learn how to find where your data has missing values and explore multiple approaches on how to deal with them. Besides, you will focus on analyzing the underlying distribution of your data and whether it will impact your machine learning pipeline. You will learn how to deal with skewed data and situations where outliers may be negatively impacting your analysis.
This course offers:
- Flexible deadlines: Reset deadlines based on your availability.
- Get a Certificate when you complete
- 100% online
- Intermediate level
- This course is part of these tracks: Machine Learning Scientist with Python
- Approximately 4 hours to complete
- Subtitles: English
Enroll here: https://app.datacamp.com/learn/courses/feature-engineering-for-machine-learning-in-python
