TGx:Pluralsight | Preparing Data for Feature Engineering and Machine Learning [FCO]

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Pluralsight | Preparing Data for Feature Engineering and Machine Learning [FCO]

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Category:

Other > Training

Language:

English

Total Size:

468.34 MB

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d0c354116c5cda38b815ba39102cb93f51a451d8

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Prom3th3uS :_blocked:

Added:

30-11-2019 15:34

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1,994

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File	Size
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01 - Course Overview/01 - Course Overview.en.srt	3.28 kB
01 - Course Overview/01 - Course Overview.mp4	9.67 MB
02 - Understanding the Role of Features in Machine Learning/02 - Module Overview.en.srt	2.24 kB
02 - Understanding the Role of Features in Machine Learning/02 - Module Overview.mp4	5.29 MB
02 - Understanding the Role of Features in Machine Learning/03 - Prerequisites and Course Outline.en.srt	2.60 kB
02 - Understanding the Role of Features in Machine Learning/03 - Prerequisites and Course Outline.mp4	4.68 MB
02 - Understanding the Role of Features in Machine Learning/04 - Features and Labels.en.srt	12.01 kB
02 - Understanding the Role of Features in Machine Learning/04 - Features and Labels.mp4	9.15 MB
02 - Understanding the Role of Features in Machine Learning/05 - The Machine Learning Workflow.en.srt	7.71 kB
02 - Understanding the Role of Features in Machine Learning/05 - The Machine Learning Workflow.mp4	6.33 MB
02 - Understanding the Role of Features in Machine Learning/06 - Components of Feature Engineering.en.srt	4.99 kB
02 - Understanding the Role of Features in Machine Learning/06 - Components of Feature Engineering.mp4	3.57 MB
02 - Understanding the Role of Features in Machine Learning/07 - Feature Selection, Feature Learning, and Feature Extraction.en.srt	13.58 kB
02 - Understanding the Role of Features in Machine Learning/07 - Feature Selection, Feature Learning, and Feature Extraction.mp4	10.36 MB
02 - Understanding the Role of Features in Machine Learning/08 - Feature Combination and Dimensionality Reduction.en.srt	7.57 kB
02 - Understanding the Role of Features in Machine Learning/08 - Feature Combination and Dimensionality Reduction.mp4	5.78 MB
02 - Understanding the Role of Features in Machine Learning/09 - Training, Validation, and Test Data.en.srt	10.52 kB
02 - Understanding the Role of Features in Machine Learning/09 - Training, Validation, and Test Data.mp4	7.94 MB
02 - Understanding the Role of Features in Machine Learning/10 - K-fold Cross Validation.en.srt	7.53 kB
02 - Understanding the Role of Features in Machine Learning/10 - K-fold Cross Validation.mp4	6.90 MB
02 - Understanding the Role of Features in Machine Learning/11 - Module Summary.en.srt	2.44 kB
02 - Understanding the Role of Features in Machine Learning/11 - Module Summary.mp4	5.42 MB
03 - Preparing Data for Machine Learning/12 - Module Overview.en.srt	3.06 kB
03 - Preparing Data for Machine Learning/12 - Module Overview.mp4	6.40 MB
03 - Preparing Data for Machine Learning/13 - Problems with Data.en.srt	7.62 kB
03 - Preparing Data for Machine Learning/13 - Problems with Data.mp4	6.60 MB
03 - Preparing Data for Machine Learning/14 - Dealing with Missing Values.en.srt	9.17 kB
03 - Preparing Data for Machine Learning/14 - Dealing with Missing Values.mp4	6.39 MB
03 - Preparing Data for Machine Learning/15 - Dealing with Outliers.en.srt	10.99 kB
03 - Preparing Data for Machine Learning/15 - Dealing with Outliers.mp4	8.16 MB
03 - Preparing Data for Machine Learning/16 - Applying Different Techniques to Handle Missing Values.en.srt	13.99 kB
03 - Preparing Data for Machine Learning/16 - Applying Different Techniques to Handle Missing Values.mp4	13.89 MB
03 - Preparing Data for Machine Learning/17 - Detecting and Handling Outliers.en.srt	13.16 kB
03 - Preparing Data for Machine Learning/17 - Detecting and Handling Outliers.mp4	12.97 MB
03 - Preparing Data for Machine Learning/18 - Reading and Exploring the Dataset.en.srt	15.07 kB
03 - Preparing Data for Machine Learning/18 - Reading and Exploring the Dataset.mp4	16.31 MB
03 - Preparing Data for Machine Learning/19 - Perform Simple and Multiple Linear Regression.en.srt	8.53 kB
03 - Preparing Data for Machine Learning/19 - Perform Simple and Multiple Linear Regression.mp4	8.71 MB
03 - Preparing Data for Machine Learning/20 - Module Summary.en.srt	2.21 kB
03 - Preparing Data for Machine Learning/20 - Module Summary.mp4	4.62 MB
04 - Understanding and Implementing Feature Selection/21 - Module Overview.en.srt	3.19 kB
04 - Understanding and Implementing Feature Selection/21 - Module Overview.mp4	2.02 MB
04 - Understanding and Implementing Feature Selection/22 - Types of Data.en.srt	8.18 kB
04 - Understanding and Implementing Feature Selection/22 - Types of Data.mp4	6.66 MB
04 - Understanding and Implementing Feature Selection/23 - Measuring Correlations.en.srt	8.67 kB
04 - Understanding and Implementing Feature Selection/23 - Measuring Correlations.mp4	6.26 MB
04 - Understanding and Implementing Feature Selection/24 - Understanding Feature Selection Using Filter, Embedded, and Wrapper Methods.en.srt	11.17 kB
04 - Understanding and Implementing Feature Selection/24 - Understanding Feature Selection Using Filter, Embedded, and Wrapper Methods.mp4	8.08 MB
04 - Understanding and Implementing Feature Selection/25 - Feature Selection Using Missing Value Ratio.en.srt	9.82 kB
04 - Understanding and Implementing Feature Selection/25 - Feature Selection Using Missing Value Ratio.mp4	10.75 MB
04 - Understanding and Implementing Feature Selection/26 - Calculating and Visualizing Correlations Using Pandas.en.srt	11.58 kB
04 - Understanding and Implementing Feature Selection/26 - Calculating and Visualizing Correlations Using Pandas.mp4	14.42 MB
04 - Understanding and Implementing Feature Selection/27 - Calculating and Visualizing Correlations Using Yellowbrick.en.srt	5.29 kB
04 - Understanding and Implementing Feature Selection/27 - Calculating and Visualizing Correlations Using Yellowbrick.mp4	6.82 MB
04 - Understanding and Implementing Feature Selection/28 - Feature Selection Using Filter Methods.en.srt	11.78 kB
04 - Understanding and Implementing Feature Selection/28 - Feature Selection Using Filter Methods.mp4	13.32 MB
04 - Understanding and Implementing Feature Selection/29 - Feature Selection Using Wrapper Methods.en.srt	11.24 kB
04 - Understanding and Implementing Feature Selection/29 - Feature Selection Using Wrapper Methods.mp4	12.80 MB
04 - Understanding and Implementing Feature Selection/30 - Feature Selection Using Embedded Methods.en.srt	9.64 kB
04 - Understanding and Implementing Feature Selection/30 - Feature Selection Using Embedded Methods.mp4	10.19 MB
04 - Understanding and Implementing Feature Selection/31 - Module Summary.en.srt	3.11 kB
04 - Understanding and Implementing Feature Selection/31 - Module Summary.mp4	1.96 MB
05 - Exploring Feature Extraction Techniques/32 - Module Overview.en.srt	2.51 kB
05 - Exploring Feature Extraction Techniques/32 - Module Overview.mp4	2.02 MB
05 - Exploring Feature Extraction Techniques/33 - Representing Images as Matrices and Image Preprocessing Techniques.en.srt	8.73 kB
05 - Exploring Feature Extraction Techniques/33 - Representing Images as Matrices and Image Preprocessing Techniques.mp4	6.65 MB
05 - Exploring Feature Extraction Techniques/34 - Feature Detection and Extraction from Images.en.srt	9.93 kB
05 - Exploring Feature Extraction Techniques/34 - Feature Detection and Extraction from Images.mp4	7.71 MB
05 - Exploring Feature Extraction Techniques/35 - Feature Extraction from Text.en.srt	11.29 kB
05 - Exploring Feature Extraction Techniques/35 - Feature Extraction from Text.mp4	8.01 MB
05 - Exploring Feature Extraction Techniques/36 - Module Summary.en.srt	2.37 kB
05 - Exploring Feature Extraction Techniques/36 - Module Summary.mp4	1.69 MB
06 - Implementing Feature Extraction/37 - Module Overview.en.srt	2.08 kB
06 - Implementing Feature Extraction/37 - Module Overview.mp4	5.35 MB
06 - Implementing Feature Extraction/38 - Tokenization and Visualizing Frequency Distributions.en.srt	7.43 kB
06 - Implementing Feature Extraction/38 - Tokenization and Visualizing Frequency Distributions.mp4	8.82 MB
06 - Implementing Feature Extraction/39 - Performing Normalization Using Different Techniques.en.srt	9.48 kB
06 - Implementing Feature Extraction/39 - Performing Normalization Using Different Techniques.mp4	11.52 MB
06 - Implementing Feature Extraction/40 - Creating Feature Vectors from Text Data.en.srt	11.85 kB
06 - Implementing Feature Extraction/40 - Creating Feature Vectors from Text Data.mp4	13.47 MB
06 - Implementing Feature Extraction/41 - Loading and Transforming Images.en.srt	9.50 kB
06 - Implementing Feature Extraction/41 - Loading and Transforming Images.mp4	12.23 MB
06 - Implementing Feature Extraction/42 - Extracting Features from Images.en.srt	6.34 kB
06 - Implementing Feature Extraction/42 - Extracting Features from Images.mp4	8.03 MB
06 - Implementing Feature Extraction/43 - Detecting Keypoints and Descriptors to Perform Image Matching.en.srt	9.27 kB
06 - Implementing Feature Extraction/43 - Detecting Keypoints and Descriptors to Perform Image Matching.mp4	14.53 MB
06 - Implementing Feature Extraction/44 - Extracting Text from Images Using OCR.en.srt	6.97 kB
06 - Implementing Feature Extraction/44 - Extracting Text from Images Using OCR.mp4	14.84 MB
06 - Implementing Feature Extraction/45 - Extracting Features from Dates.en.srt	8.33 kB
06 - Implementing Feature Extraction/45 - Extracting Features from Dates.mp4	10.44 MB
06 - Implementing Feature Extraction/46 - Working with Geospatial Features.en.srt	12.38 kB
06 - Implementing Feature Extraction/46 - Working with Geospatial Features.mp4	52.96 MB
06 - Implementing Feature Extraction/47 - Summary and Further Study.en.srt	3.07 kB
06 - Implementing Feature Extraction/47 - Summary and Further Study.mp4	7.17 MB
Exercise Files.zip	30.12 MB

Description

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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.