Feature Engineering Case Study in Python

A Complete Introduction to Feature Engineering

Course Overview

What you’ll learn

  • You’ll define what feature engineering is and it’s importance in machine learning..
  • You’ll walk through an end to end case study on featuring engineering..
  • You’ll learn data imputation and advanced data cleansing techniques..
  • You’ll learn how to compare and contrast various cleansed datasets..

Course Content

  • Introduction –> 4 lectures • 5min.
  • Feature Engineering –> 3 lectures • 7min.
  • Data Exploration –> 7 lectures • 35min.
  • Crafting and Cleansing Features –> 7 lectures • 23min.
  • Data Preparation and Modeling –> 3 lectures • 11min.
  • Modeling –> 5 lectures • 21min.

Feature Engineering Case Study in Python

Requirements

  • You’ll need to have a solid foundation in Python to get the most out of this course..
  • You’ll need to have a solid foundation in machine learning to get the most out of this course..
  • You’ll need to have some applied statistics for machine learning engineers to get the most out of this class..

Course Overview

The quality of the predictions coming out of your machine learning model is a direct reflection of the data you feed it during training. Feature engineering helps you extract every last bit of value out of data. This course provides the tools to take a data set, tease out the signal, and throw out the noise in order to optimize your models.

The concepts generalize to nearly any kind of machine learning algorithm. In the course you’ll explore continuous and categorical features and shows how to clean, normalize, and alter them. Learn how to address missing values, remove outliers, transform data, create indicators, and convert features. In the final sections, you’ll to prepare features for modeling and provides four variations for comparison, so you can evaluate the impact of cleaning, transforming, and creating features through the lens of model performance.

What You’ll Learn

  • What is feature engineering?
  • Exploring the data
  • Plotting features
  • Cleaning existing features
  • Creating new features
  • Standardizing features
  • Comparing the impacts on model performance

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 Feature Engineering Case Study in Python.

Let’s get started!