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

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

Torrent:	Pluralsight \| Preparing Data for Feature Engineering and Machine Learning [FCO]
Description:	For More Udemy Free Courses >>> https://freetutorials.us/ For more Lynda and other Courses >>> https://www.freecoursesonline.me/ Forum for discussion >>> https://1hack.us/ 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.
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Category:	Other /Tutorials
Size:	468.3 MB
Show Files »	[FreeCoursesOnline.Me] [Pluralsight] Preparing Data for Feature Engineering and Machine Learning [FCO] 0. Websites you may like 0. (1Hack.Us) Premium Tutorials-Guides-Articles & Community based Forum.url (0.4 KB) 1. (FreeTutorials.Us) Download Udemy Paid Courses For Free.url (0.3 KB) 2. (FreeCoursesOnline.Me) Download Udacity, Masterclass, Lynda, PHLearn, Pluralsight Free.url (0.3 KB) 3. (NulledPremium.com) Download E-Learning, E-Books, Audio-Books, Comics, Articles & more etc.url (0.2 KB) 4. (FTUApps.com) Download Cracked Developers Applications For Free.url (0.2 KB) How you can help Team-FTU.txt (0.2 KB) 01 - Course Overview 01 - Course Overview.en.srt (3.3 KB) 01 - Course Overview.mp4 (9.7 MB) 02 - Understanding the Role of Features in Machine Learning 02 - Module Overview.en.srt (2.2 KB) 02 - Module Overview.mp4 (5.3 MB) 03 - Prerequisites and Course Outline.en.srt (2.6 KB) 03 - Prerequisites and Course Outline.mp4 (4.7 MB) 04 - Features and Labels.en.srt (12.0 KB) 04 - Features and Labels.mp4 (9.2 MB) 05 - The Machine Learning Workflow.en.srt (7.7 KB) 05 - The Machine Learning Workflow.mp4 (6.3 MB) 06 - Components of Feature Engineering.en.srt (5.0 KB) 06 - Components of Feature Engineering.mp4 (3.6 MB) 07 - Feature Selection, Feature Learning, and Feature Extraction.en.srt (13.6 KB) 07 - Feature Selection, Feature Learning, and Feature Extraction.mp4 (10.4 MB) 08 - Feature Combination and Dimensionality Reduction.en.srt (7.6 KB) 08 - Feature Combination and Dimensionality Reduction.mp4 (5.8 MB) 09 - Training, Validation, and Test Data.en.srt (10.5 KB) 09 - Training, Validation, and Test Data.mp4 (7.9 MB) 10 - K-fold Cross Validation.en.srt (7.5 KB) 10 - K-fold Cross Validation.mp4 (6.9 MB) 11 - Module Summary.en.srt (2.4 KB) 11 - Module Summary.mp4 (5.4 MB) 03 - Preparing Data for Machine Learning 12 - Module Overview.en.srt (3.1 KB) 12 - Module Overview.mp4 (6.4 MB) 13 - Problems with Data.en.srt (7.6 KB) 13 - Problems with Data.mp4 (6.6 MB) 14 - Dealing with Missing Values.en.srt (9.2 KB) 14 - Dealing with Missing Values.mp4 (6.4 MB) 15 - Dealing with Outliers.en.srt (11.0 KB) 15 - Dealing with Outliers.mp4 (8.2 MB) 16 - Applying Different Techniques to Handle Missing Values.en.srt (14.0 KB) 16 - Applying Different Techniques to Handle Missing Values.mp4 (13.9 MB) 17 - Detecting and Handling Outliers.en.srt (13.2 KB) 17 - Detecting and Handling Outliers.mp4 (13.0 MB) 18 - Reading and Exploring the Dataset.en.srt (15.1 KB) 18 - Reading and Exploring the Dataset.mp4 (16.3 MB) 19 - Perform Simple and Multiple Linear Regression.en.srt (8.5 KB) 19 - Perform Simple and Multiple Linear Regression.mp4 (8.7 MB) 20 - Module Summary.en.srt (2.2 KB) 20 - Module Summary.mp4 (4.6 MB) 04 - Understanding and Implementing Feature Selection 21 - Module Overview.en.srt (3.2 KB) 21 - Module Overview.mp4 (2.0 MB) 22 - Types of Data.en.srt (8.2 KB) 22 - Types of Data.mp4 (6.7 MB) 23 - Measuring Correlations.en.srt (8.7 KB) 23 - Measuring Correlations.mp4 (6.3 MB) 24 - Understanding Feature Selection Using Filter, Embedded, and Wrapper Methods.en.srt (11.2 KB) 24 - Understanding Feature Selection Using Filter, Embedded, and Wrapper Methods.mp4 (8.1 MB) 25 - Feature Selection Using Missing Value Ratio.en.srt (9.8 KB) 25 - Feature Selection Using Missing Value Ratio.mp4 (10.7 MB) 26 - Calculating and Visualizing Correlations Using Pandas.en.srt (11.6 KB) 26 - Calculating and Visualizing Correlations Using Pandas.mp4 (14.4 MB) 27 - Calculating and Visualizing Correlations Using Yellowbrick.en.srt (5.3 KB) 27 - Calculating and Visualizing Correlations Using Yellowbrick.mp4 (6.8 MB) 28 - Feature Selection Using Filter Methods.en.srt (11.8 KB) 28 - Feature Selection Using Filter Methods.mp4 (13.3 MB) 29 - Feature Selection Using Wrapper Methods.en.srt (11.2 KB) 29 - Feature Selection Using Wrapper Methods.mp4 (12.8 MB) 30 - Feature Selection Using Embedded Methods.en.srt (9.6 KB) 30 - Feature Selection Using Embedded Methods.mp4 (10.2 MB) 31 - Module Summary.en.srt (3.1 KB) 31 - Module Summary.mp4 (2.0 MB) 05 - Exploring Feature Extraction Techniques 32 - Module Overview.en.srt (2.5 KB) 32 - Module Overview.mp4 (2.0 MB) 33 - Representing Images as Matrices and Image Preprocessing Techniques.en.srt (8.7 KB) 33 - Representing Images as Matrices and Image Preprocessing Techniques.mp4 (6.6 MB) 34 - Feature Detection and Extraction from Images.en.srt (9.9 KB) 34 - Feature Detection and Extraction from Images.mp4 (7.7 MB) 35 - Feature Extraction from Text.en.srt (11.3 KB) 35 - Feature Extraction from Text.mp4 (8.0 MB) 36 - Module Summary.en.srt (2.4 KB) 36 - Module Summary.mp4 (1.7 MB) 06 - Implementing Feature Extraction 37 - Module Overview.en.srt (2.1 KB) 37 - Module Overview.mp4 (5.3 MB) 38 - Tokenization and Visualizing Frequency Distributions.en.srt (7.4 KB) 38 - Tokenization and Visualizing Frequency Distributions.mp4 (8.8 MB) 39 - Performing Normalization Using Different Techniques.en.srt (9.5 KB) 39 - Performing Normalization Using Different Techniques.mp4 (11.5 MB) 40 - Creating Feature Vectors from Text Data.en.srt (11.9 KB) 40 - Creating Feature Vectors from Text Data.mp4 (13.5 MB) 41 - Loading and Transforming Images.en.srt (9.5 KB) 41 - Loading and Transforming Images.mp4 (12.2 MB) 42 - Extracting Features from Images.en.srt (6.3 KB) 42 - Extracting Features from Images.mp4 (8.0 MB) 43 - Detecting Keypoints and Descriptors to Perform Image Matching.en.srt (9.3 KB) 43 - Detecting Keypoints and Descriptors to Perform Image Matching.mp4 (14.5 MB) 44 - Extracting Text from Images Using OCR.en.srt (7.0 KB) 44 - files
Added:	2019-12-01 11:05:18
Language:	English
Peers:	Seeders : 16 , Leechers : 4
Release name:	Pluralsight \| Preparing Data for Feature Engineering and Machine Learning [FCO]
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