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

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  1. Kanka

    Kanka Well-Known Member Loyal User

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    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
    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.

    ✓ 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

    ✓ 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

    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