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Download Torrent "Pluralsight | Preparing Data for Feature Engineering and Machine Learning [..."

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Title:Pluralsight | Preparing Data for Feature Engineering and Machine Learning [...
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Created by: Janani Ravi
Language: English
Updated: Oct 29, 2019
Duration: 3h 18m
Subtitle: Included
Torrent Contains: 101 Files, 7 Folders
Course Source: https://www.pluralsight.com/courses/preparing-data-feature-engineering-machine-learning

About

This course covers categories of feature engineering techniques used to get the best results from a machine learning model, including feature selection, and several feature extraction techniques to re-express features in the most appropriate form.

Description

However well designed and well implemented a machine learning model is, if the data fed in is poorly engineered, the model’s predictions will be disappointing. In this course, Preparing Data for Feature Engineering and Machine Learning, you will gain the ability to appropriately pre-process your data -- in effect engineer it -- so that you can get the best out of your ML models. First, you will learn how feature selection techniques can be used to find predictors that contain the most information. Feature selection can be broadly grouped into three categories known as filter, wrapper, and embedded techniques and we will understand and implement all of these. Next, you will discover how feature extraction differs from feature selection, in that data is substantially re-expressed, sometimes in forms that are hard to interpret. You will then understand techniques for feature extraction from image and text data. Finally, you will round out your knowledge by understanding how to leverage powerful Python libraries for working with images, text, dates, and geo-spatial data. When you’re finished with this course, you will have the skills and knowledge to identify the correct feature engineering techniques, and the appropriate solutions for your use-case.

Level

• Beginner

About Author

A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.




Category:Tutorials > Tutorials
Lang:English  English
Total Size:468 MB
Info Hash:d0c354116c5cda38b815ba39102cb93f51a451d8
Added By:Prom3th3uS
Date Added:30-11-2019 15:34:05

  

Files

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01 - Course Overview/01 - Course Overview.en.srt3.28 kB
01 - Course Overview/01 - Course Overview.mp410 MB
02 - Understanding the Role of Features in Machine Learning/02 - Module Overview.en.srt2.24 kB
02 - Understanding the Role of Features in Machine Learning/02 - Module Overview.mp45 MB
02 - Understanding the Role of Features in Machine Learning/03 - Prerequisites and Course Outline.en.srt2.60 kB
02 - Understanding the Role of Features in Machine Learning/03 - Prerequisites and Course Outline.mp45 MB
02 - Understanding the Role of Features in Machine Learning/04 - Features and Labels.en.srt12.01 kB
02 - Understanding the Role of Features in Machine Learning/04 - Features and Labels.mp49 MB
02 - Understanding the Role of Features in Machine Learning/05 - The Machine Learning Workflow.en.srt7.71 kB
02 - Understanding the Role of Features in Machine Learning/05 - The Machine Learning Workflow.mp46 MB
02 - Understanding the Role of Features in Machine Learning/06 - Components of Feature Engineering.en.srt4.99 kB
02 - Understanding the Role of Features in Machine Learning/06 - Components of Feature Engineering.mp44 MB
02 - Understanding the Role of Features in Machine Learning/07 - Feature Selection, Feature Learning, and Feature Extraction.en.srt13.58 kB
02 - Understanding the Role of Features in Machine Learning/07 - Feature Selection, Feature Learning, and Feature Extraction.mp410 MB
02 - Understanding the Role of Features in Machine Learning/08 - Feature Combination and Dimensionality Reduction.en.srt7.57 kB
02 - Understanding the Role of Features in Machine Learning/08 - Feature Combination and Dimensionality Reduction.mp46 MB
02 - Understanding the Role of Features in Machine Learning/09 - Training, Validation, and Test Data.en.srt10.52 kB
02 - Understanding the Role of Features in Machine Learning/09 - Training, Validation, and Test Data.mp48 MB
02 - Understanding the Role of Features in Machine Learning/10 - K-fold Cross Validation.en.srt7.53 kB
02 - Understanding the Role of Features in Machine Learning/10 - K-fold Cross Validation.mp47 MB
02 - Understanding the Role of Features in Machine Learning/11 - Module Summary.en.srt2.44 kB
02 - Understanding the Role of Features in Machine Learning/11 - Module Summary.mp45 MB
03 - Preparing Data for Machine Learning/12 - Module Overview.en.srt3.06 kB
03 - Preparing Data for Machine Learning/12 - Module Overview.mp46 MB
03 - Preparing Data for Machine Learning/13 - Problems with Data.en.srt7.62 kB
03 - Preparing Data for Machine Learning/13 - Problems with Data.mp47 MB
03 - Preparing Data for Machine Learning/14 - Dealing with Missing Values.en.srt9.17 kB
03 - Preparing Data for Machine Learning/14 - Dealing with Missing Values.mp46 MB
03 - Preparing Data for Machine Learning/15 - Dealing with Outliers.en.srt10.99 kB
03 - Preparing Data for Machine Learning/15 - Dealing with Outliers.mp48 MB
03 - Preparing Data for Machine Learning/16 - Applying Different Techniques to Handle Missing Values.en.srt13.99 kB
03 - Preparing Data for Machine Learning/16 - Applying Different Techniques to Handle Missing Values.mp414 MB
03 - Preparing Data for Machine Learning/17 - Detecting and Handling Outliers.en.srt13.16 kB
03 - Preparing Data for Machine Learning/17 - Detecting and Handling Outliers.mp413 MB
03 - Preparing Data for Machine Learning/18 - Reading and Exploring the Dataset.en.srt15.07 kB
03 - Preparing Data for Machine Learning/18 - Reading and Exploring the Dataset.mp416 MB
03 - Preparing Data for Machine Learning/19 - Perform Simple and Multiple Linear Regression.en.srt8.53 kB
03 - Preparing Data for Machine Learning/19 - Perform Simple and Multiple Linear Regression.mp49 MB
03 - Preparing Data for Machine Learning/20 - Module Summary.en.srt2.21 kB
03 - Preparing Data for Machine Learning/20 - Module Summary.mp45 MB
04 - Understanding and Implementing Feature Selection/21 - Module Overview.en.srt3.19 kB
04 - Understanding and Implementing Feature Selection/21 - Module Overview.mp42 MB
04 - Understanding and Implementing Feature Selection/22 - Types of Data.en.srt8.18 kB
04 - Understanding and Implementing Feature Selection/22 - Types of Data.mp47 MB
04 - Understanding and Implementing Feature Selection/23 - Measuring Correlations.en.srt8.67 kB
04 - Understanding and Implementing Feature Selection/23 - Measuring Correlations.mp46 MB
04 - Understanding and Implementing Feature Selection/24 - Understanding Feature Selection Using Filter, Embedded, and Wrapper Methods.en.srt11.17 kB
04 - Understanding and Implementing Feature Selection/24 - Understanding Feature Selection Using Filter, Embedded, and Wrapper Methods.mp48 MB
04 - Understanding and Implementing Feature Selection/25 - Feature Selection Using Missing Value Ratio.en.srt9.82 kB
04 - Understanding and Implementing Feature Selection/25 - Feature Selection Using Missing Value Ratio.mp411 MB
04 - Understanding and Implementing Feature Selection/26 - Calculating and Visualizing Correlations Using Pandas.en.srt11.58 kB
04 - Understanding and Implementing Feature Selection/26 - Calculating and Visualizing Correlations Using Pandas.mp414 MB
04 - Understanding and Implementing Feature Selection/27 - Calculating and Visualizing Correlations Using Yellowbrick.en.srt5.29 kB
04 - Understanding and Implementing Feature Selection/27 - Calculating and Visualizing Correlations Using Yellowbrick.mp47 MB
04 - Understanding and Implementing Feature Selection/28 - Feature Selection Using Filter Methods.en.srt11.78 kB
04 - Understanding and Implementing Feature Selection/28 - Feature Selection Using Filter Methods.mp413 MB
04 - Understanding and Implementing Feature Selection/29 - Feature Selection Using Wrapper Methods.en.srt11.24 kB
04 - Understanding and Implementing Feature Selection/29 - Feature Selection Using Wrapper Methods.mp413 MB
04 - Understanding and Implementing Feature Selection/30 - Feature Selection Using Embedded Methods.en.srt9.64 kB
04 - Understanding and Implementing Feature Selection/30 - Feature Selection Using Embedded Methods.mp410 MB
04 - Understanding and Implementing Feature Selection/31 - Module Summary.en.srt3.11 kB
04 - Understanding and Implementing Feature Selection/31 - Module Summary.mp42 MB
05 - Exploring Feature Extraction Techniques/32 - Module Overview.en.srt2.51 kB
05 - Exploring Feature Extraction Techniques/32 - Module Overview.mp42 MB
05 - Exploring Feature Extraction Techniques/33 - Representing Images as Matrices and Image Preprocessing Techniques.en.srt8.73 kB
05 - Exploring Feature Extraction Techniques/33 - Representing Images as Matrices and Image Preprocessing Techniques.mp47 MB
05 - Exploring Feature Extraction Techniques/34 - Feature Detection and Extraction from Images.en.srt9.93 kB
05 - Exploring Feature Extraction Techniques/34 - Feature Detection and Extraction from Images.mp48 MB
05 - Exploring Feature Extraction Techniques/35 - Feature Extraction from Text.en.srt11.29 kB
05 - Exploring Feature Extraction Techniques/35 - Feature Extraction from Text.mp48 MB
05 - Exploring Feature Extraction Techniques/36 - Module Summary.en.srt2.37 kB
05 - Exploring Feature Extraction Techniques/36 - Module Summary.mp42 MB
06 - Implementing Feature Extraction/37 - Module Overview.en.srt2.08 kB
06 - Implementing Feature Extraction/37 - Module Overview.mp45 MB
06 - Implementing Feature Extraction/38 - Tokenization and Visualizing Frequency Distributions.en.srt7.43 kB
06 - Implementing Feature Extraction/38 - Tokenization and Visualizing Frequency Distributions.mp49 MB
06 - Implementing Feature Extraction/39 - Performing Normalization Using Different Techniques.en.srt9.48 kB
06 - Implementing Feature Extraction/39 - Performing Normalization Using Different Techniques.mp412 MB
06 - Implementing Feature Extraction/40 - Creating Feature Vectors from Text Data.en.srt11.85 kB
06 - Implementing Feature Extraction/40 - Creating Feature Vectors from Text Data.mp413 MB
06 - Implementing Feature Extraction/41 - Loading and Transforming Images.en.srt9.50 kB
06 - Implementing Feature Extraction/41 - Loading and Transforming Images.mp412 MB
06 - Implementing Feature Extraction/42 - Extracting Features from Images.en.srt6.34 kB
06 - Implementing Feature Extraction/42 - Extracting Features from Images.mp48 MB
06 - Implementing Feature Extraction/43 - Detecting Keypoints and Descriptors to Perform Image Matching.en.srt9.27 kB
06 - Implementing Feature Extraction/43 - Detecting Keypoints and Descriptors to Perform Image Matching.mp415 MB
06 - Implementing Feature Extraction/44 - Extracting Text from Images Using OCR.en.srt6.97 kB
06 - Implementing Feature Extraction/44 - Extracting Text from Images Using OCR.mp415 MB
06 - Implementing Feature Extraction/45 - Extracting Features from Dates.en.srt8.33 kB
06 - Implementing Feature Extraction/45 - Extracting Features from Dates.mp410 MB
06 - Implementing Feature Extraction/46 - Working with Geospatial Features.en.srt12.38 kB
06 - Implementing Feature Extraction/46 - Working with Geospatial Features.mp453 MB
06 - Implementing Feature Extraction/47 - Summary and Further Study.en.srt3.07 kB
06 - Implementing Feature Extraction/47 - Summary and Further Study.mp47 MB
Exercise Files.zip30 MB



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