Data Cleansing Master Class in Python
Instructor: Mike West
The Complete Guide to Data Cleansing for Machine Learning Engineers
What you’ll learn
You’ll learn data imputation and advanced data cleansing techniques.
You’ll learn how to apply real-world data cleansing techniques to your data.
You’ll learn advanced data cleansing techniques.
You’ll learn how to prepare data in a way that avoids data leakage, and in turn, incorrect model evaluation.
Requirements
You’ll need a really solid foundation in Python.
You’ll need to understand the basics of machine learning.
Description
Welcome to Data Cleansing Master Class in Python.
In the course you’ll learn:
The importance of data preparation for predictive modeling machine learning projects.
How to prepare data in a way that avoids data leakage, and in turn, incorrect model evaluation.
How to identify and handle problems with messy data, such as outliers and missing values.
This course is a hands on-guide. It is a playbook and a workbook intended for you to learn by doing and then apply your new understanding to the feature engineering in Python. To get the most out of the course, I would recommend working through all the examples in each tutorial. If you watch this course like a movie you’ll get little out of it.
In the applied space machine learning is programming and programming is a hands on-sport.
Thank you for your interest in Data Cleansing Master Class in Python.
Who this course is for:
You are serious about become a machine learning engineer in the real-world.
