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. 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 Our members see more. Join us! ------------- Our members see more. Join us!