Dec

22

2020

Feature Engineering Case Study in Python

Laser 22 Dec 2020 08:02 LEARNING » e-learning - Tutorial

Feature Engineering Case Study in Python
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 29 lectures (1h 41m) | Size: 380.3 MB

You'll define what feature eeering is and it's importance in machine learning.

A Complete Introduction to Feature Eeering

You'll walk through an end to end case study on featuring eeering.

You'll learn data imputation and advanced data cleansing techniques.

You'll learn how to compare and contrast various cleansed datasets.

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 eeers 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 eeering 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, transfog, and creating features through the lens of model performance.

What You'll Learn

What is feature eeering?

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 eeering 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 Eeering Case Study in Python.

Let's get started!

If you want to become a machine learning eeer then this course is for you

If you want something beyond the typical lecture style course then this course is for you

If you want learn how to get the most out of your models, this course is for you.



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