Feature Engineering Case Study in Python

Feature Engineering Case Study in Python, available at $39.99, has an average rating of 4.17, with 30 lectures, based on 6 reviews, and has 109 subscribers.

You will learn about 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. This course is ideal for individuals who are If you want to become a machine learning engineer then this course is for you or If you want something beyond the typical lecture style course then this course is for you or If you want learn how to get the most out of your models, this course is for you. It is particularly useful for If you want to become a machine learning engineer then this course is for you or If you want something beyond the typical lecture style course then this course is for you or If you want learn how to get the most out of your models, this course is for you.

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Summary

Title: Feature Engineering Case Study in Python

Price: $39.99

Average Rating: 4.17

Number of Lectures: 30

Number of Published Lectures: 30

Number of Curriculum Items: 30

Number of Published Curriculum Objects: 30

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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.

Who Should Attend

If you want to become a machine learning engineer 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.

Target Audiences

If you want to become a machine learning engineer 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.

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!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: What you Should Know

Lecture 3: The Libraries

Lecture 4: Machine Learning Pipeline

Chapter 2: Feature Engineering

Lecture 1: Feature Engineering Defined

Lecture 2: The Unsung Hero

Lecture 3: Feature Engineering Toolkit

Chapter 3: Data Exploration

Lecture 1: The Dataset

Lecture 2: Exploring Continuous Features: Part 1

Lecture 3: Exploring Continuous Features: Part 2

Lecture 4: Plotting Continuous Features

Lecture 5: Exploring Categorical Features

Lecture 6: Plotting Categorical Features

Lecture 7: Feature Summary

Chapter 4: Crafting and Cleansing Features

Lecture 1: Mean Value Imputation

Lecture 2: Outlier Detection

Lecture 3: Transforming Skewed Features

Lecture 4: Craft New Features From Text

Lecture 5: Create New Indicator

Lecture 6: Combining Existing Features Into a New Feature

Lecture 7: Categorical Features to Numeric

Chapter 5: Data Preparation and Modeling

Lecture 1: Create Training and Testing Splits

Lecture 2: Scaling Features

Lecture 3: Create Datasets for Modeling

Chapter 6: Modeling

Lecture 1: Modeling Raw Features

Lecture 2: Modeling Cleaned Original Features

Lecture 3: Modeling All Features

Lecture 4: Modeling Reduced Set of Features

Lecture 5: Evaluate and Compare all Models

Lecture 6: Bonus Lecture: Tons of Free Machine Learning Content

Instructors

Mike West
Creator of LogikBot

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5 stars: 1 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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