PAC | Hands-On Feature Engineering With Python (2019 EN)

Company: Packt Publishing
Author: Sahiba Chopra
Full Title: Hands-On Feature Engineering With Python
Year: 2019
Language: English
Genre: Educational: Big Data
Skill Level: -
Price: €124.99
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Files: MP4 (+ Code Files)
Time: 01:11:24
Video: AVC, 1920 x 1080 (1.778) at 30.000 fps, 300 kbps
Audio: AAC at 160 Kbps, 2 channels, 48.0 KHz



A hands-on course to speed up the predicting power of machine learning algorithms.

Feature engineering is the most important aspect of machine learning. You know that every day you put off learning the process, you are hurting your model’s performance. Studies repeatedly prove that feature engineering can be much more powerful than the choice of algorithms. Yet the field of feature engineering can seem overwhelming and confusing. This course offers you the single best solution. In this course, all of the recommendations have been extensively tested and proven on real-world problems. You’ll find everything included: the recommendations, the code, the data sources, and the rationale. You’ll get an over-the-shoulder, step-by-step approach for every situation, and each segment can stand alone, allowing you to jump immediately to the topics most important to you.

By the end of the course, you’ll have a clear, concise path to feature engineering and will enable you to get improved results by applying feature engineering techniques on your own datasets.

Style and Approach
This course is a hands-on guide filled with practical tutorials and real-world datasets. It takes a step-by-step approach where viewers will get an idea of when to apply which type of feature engineering to get the most accurate results for machine learning applications.


Learn:
✓ Master the insider tips for world-class feature engineering
✓ Eliminate frustration and confusion in handling all aspects of features
✓ Dramatically reduce the time required to move to the modeling steps of the process
✓ Handle missing values with speed and ease
✓ Systematically test for feature interaction terms build new features
✓ Leverage advanced “target mean encoding” to maximize performance and understanding
✓ Handle outliers automatically with much less effort

Features:
✓ Get expert knowledge of feature engineering techniques for different datasets such as videos, text, images, and audio samples
✓ Uncover and execute feature extraction with detailed deep-learning techniques
✓ Discover how to perform feature engineering on unsupervised learning and semi-supervised learning


Lessons:
1. Introduction to Feature Engineering
01. The Course Overview
02. Feature Engineering, Extraction, and Selection
03. Setting Up the Environment
04. Exploratory Data Analysis
05. Creating a Baseline Machine Learning Model
06. Analyzing the Model Results
2. Implementing Feature Extraction
07. Feature Extraction
08. Working with Categorical Data
09. Merging and Expanding Numerical Variables
3. Implementing Feature Transformation
10. Dealing with Target Variables
11. Working with Correlated Variables
12. Working with Missing Data
13. Working with Outliers Using IQR
14. Removing Outliers Based on Median and Standard Deviation
4. Implementing Feature Selection
15. Filter Methods
16. Wrapper Methods
17. Feature Importance
5. Bringing it all together
18. Build a Baseline Model
19. Select the Best Features
20. Build an Ensemble Model

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