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[FreeCoursesOnline.Me] [Pluralsight] Preparing Data For Feature Engineering And Machine Learning [FCO]
TORRENT SUMMARY
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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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FILE LIST
Filename
Size
0. Websites you may like/How you can help Team-FTU.txt
229 B
01 - Course Overview/01 - Course Overview.en.srt
3.3 KB
01 - Course Overview/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 - Understanding the Role of Features in Machine Learning/02 - Module Overview.mp4
5.3 MB
02 - Understanding the Role of Features in Machine Learning/03 - Prerequisites and Course Outline.en.srt
2.6 KB
02 - Understanding the Role of Features in Machine Learning/03 - Prerequisites and Course Outline.mp4
4.7 MB
02 - Understanding the Role of Features in Machine Learning/04 - Features and Labels.en.srt
12 KB
02 - Understanding the Role of Features in Machine Learning/04 - Features and Labels.mp4
9.2 MB
02 - Understanding the Role of Features in Machine Learning/05 - The Machine Learning Workflow.en.srt
7.7 KB
02 - Understanding the Role of Features in Machine Learning/05 - The Machine Learning Workflow.mp4
6.3 MB
02 - Understanding the Role of Features in Machine Learning/06 - Components of Feature Engineering.en.srt
5 KB
02 - Understanding the Role of Features in Machine Learning/06 - Components of Feature Engineering.mp4
3.6 MB
02 - Understanding the Role of Features in Machine Learning/07 - Feature Selection, Feature Learning, and Feature Extraction.en.srt
13.6 KB
02 - Understanding the Role of Features in Machine Learning/07 - Feature Selection, Feature Learning, and Feature Extraction.mp4
10.4 MB
02 - Understanding the Role of Features in Machine Learning/08 - Feature Combination and Dimensionality Reduction.en.srt
7.6 KB
02 - Understanding the Role of Features in Machine Learning/08 - Feature Combination and Dimensionality Reduction.mp4
5.8 MB
02 - Understanding the Role of Features in Machine Learning/09 - Training, Validation, and Test Data.en.srt
10.5 KB
02 - Understanding the Role of Features in Machine Learning/09 - Training, Validation, and Test Data.mp4
7.9 MB
02 - Understanding the Role of Features in Machine Learning/10 - K-fold Cross Validation.en.srt
7.5 KB
02 - Understanding the Role of Features in Machine Learning/10 - K-fold Cross Validation.mp4
6.9 MB
02 - Understanding the Role of Features in Machine Learning/11 - Module Summary.en.srt
2.4 KB
02 - Understanding the Role of Features in Machine Learning/11 - Module Summary.mp4
5.4 MB
03 - Preparing Data for Machine Learning/12 - Module Overview.en.srt
3.1 KB
03 - Preparing Data for Machine Learning/12 - Module Overview.mp4
6.4 MB
03 - Preparing Data for Machine Learning/13 - Problems with Data.en.srt
7.6 KB
03 - Preparing Data for Machine Learning/13 - Problems with Data.mp4
6.6 MB
03 - Preparing Data for Machine Learning/14 - Dealing with Missing Values.en.srt
9.2 KB
03 - Preparing Data for Machine Learning/14 - Dealing with Missing Values.mp4
6.4 MB
03 - Preparing Data for Machine Learning/15 - Dealing with Outliers.en.srt
11 KB
03 - Preparing Data for Machine Learning/15 - Dealing with Outliers.mp4
8.2 MB
03 - Preparing Data for Machine Learning/16 - Applying Different Techniques to Handle Missing Values.en.srt
14 KB
03 - Preparing Data for Machine Learning/16 - Applying Different Techniques to Handle Missing Values.mp4
13.9 MB
03 - Preparing Data for Machine Learning/17 - Detecting and Handling Outliers.en.srt
13.2 KB
03 - Preparing Data for Machine Learning/17 - Detecting and Handling Outliers.mp4
13 MB
03 - Preparing Data for Machine Learning/18 - Reading and Exploring the Dataset.en.srt
15.1 KB
03 - Preparing Data for Machine Learning/18 - Reading and Exploring the Dataset.mp4
16.3 MB
03 - Preparing Data for Machine Learning/19 - Perform Simple and Multiple Linear Regression.en.srt
8.5 KB
03 - Preparing Data for Machine Learning/19 - Perform Simple and Multiple Linear Regression.mp4
8.7 MB
03 - Preparing Data for Machine Learning/20 - Module Summary.en.srt
2.2 KB
03 - Preparing Data for Machine Learning/20 - Module Summary.mp4
4.6 MB
04 - Understanding and Implementing Feature Selection/21 - Module Overview.en.srt
3.2 KB
04 - Understanding and Implementing Feature Selection/21 - Module Overview.mp4
2 MB
04 - Understanding and Implementing Feature Selection/22 - Types of Data.en.srt
8.2 KB
04 - Understanding and Implementing Feature Selection/22 - Types of Data.mp4
6.7 MB
04 - Understanding and Implementing Feature Selection/23 - Measuring Correlations.en.srt
8.7 KB
04 - Understanding and Implementing Feature Selection/23 - Measuring Correlations.mp4
6.3 MB
04 - Understanding and Implementing Feature Selection/24 - Understanding Feature Selection Using Filter, Embedded, and Wrapper Methods.en.srt
11.2 KB
04 - Understanding and Implementing Feature Selection/24 - Understanding Feature Selection Using Filter, Embedded, and Wrapper Methods.mp4
8.1 MB
04 - Understanding and Implementing Feature Selection/25 - Feature Selection Using Missing Value Ratio.en.srt
9.8 KB
04 - Understanding and Implementing Feature Selection/25 - Feature Selection Using Missing Value Ratio.mp4
10.7 MB
04 - Understanding and Implementing Feature Selection/26 - Calculating and Visualizing Correlations Using Pandas.en.srt
11.6 KB
04 - Understanding and Implementing Feature Selection/26 - Calculating and Visualizing Correlations Using Pandas.mp4
14.4 MB
04 - Understanding and Implementing Feature Selection/27 - Calculating and Visualizing Correlations Using Yellowbrick.en.srt
5.3 KB
04 - Understanding and Implementing Feature Selection/27 - Calculating and Visualizing Correlations Using Yellowbrick.mp4
6.8 MB
04 - Understanding and Implementing Feature Selection/28 - Feature Selection Using Filter Methods.en.srt
11.8 KB
04 - Understanding and Implementing Feature Selection/28 - Feature Selection Using Filter Methods.mp4
13.3 MB
04 - Understanding and Implementing Feature Selection/29 - Feature Selection Using Wrapper Methods.en.srt
11.2 KB
04 - Understanding and Implementing Feature Selection/29 - Feature Selection Using Wrapper Methods.mp4
12.8 MB
04 - Understanding and Implementing Feature Selection/30 - Feature Selection Using Embedded Methods.en.srt
9.6 KB
04 - Understanding and Implementing Feature Selection/30 - Feature Selection Using Embedded Methods.mp4
10.2 MB
04 - Understanding and Implementing Feature Selection/31 - Module Summary.en.srt
3.1 KB
04 - Understanding and Implementing Feature Selection/31 - Module Summary.mp4
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