[UDEMY] Feature Selection for Machine Learning - [FTU] GloDLS

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名前:	[UDEMY] Feature Selection for Machine Learning - [FTU] GloDLS
_DESCRIPTION_: From beginner to advanced Created by: Soledad Galli Last updated: 11/2018 Language: English Subtitle: Included Torrent Contains: 92 Files, 12 Folders Course Source: https://www.udemy.com/feature-selection-for-machine-learning/ What you'll learn • Understand different methods of feature selection • Implement different methods of feature selection • Reduce feature space in a dataset • Build simpler, faster and more reliable machine learning models • Analyse and understand the selected features Requirements • A Python installation • Jupyter notebook installation • Python coding skills • Some experience with Numpy and Pandas • Familiarity with Machine Learning algorithms • Familiarity with scikit-learn Description Learn how to select features and build simpler, faster and more reliable machine learning models. This is the most comprehensive, yet easy to follow, course for feature selection available online. Throughout this course you will learn a variety of techniques used worldwide for variable selection, gathered from data competition websites and white papers, blogs and forums, and from the instructor’s experience as a Data Scientist. You will have at your fingertips, altogether in one place, multiple methods that you can apply to select features from your data set. The course starts describing simple and fast methods to quickly screen the data set and remove redundant and irrelevant features. Then it describes more complex techniques that select variables taking into account variable interaction, the feature importance and its interaction with the machine learning algorithm. Finally, it describes specific techniques used in data competitions and the industry. The lectures include an explanation of the feature selection technique, the rationale to use it, and the advantages and limitations of the procedure. It also includes full code that you can take home and apply to your own data sets. This course is therefore suitable for complete beginners in data science looking to learn how to go about to select features from a data set, as well as for intermediate and even advanced data scientists seeking to level up their skills. With more than 50 lectures and 8 hours of video this comprehensive course covers every aspect of variable selection. Throughout the course you will use python as your main language. So what are you waiting for? Enrol today, learn how to select variables for machine learning, and build simpler, faster and more reliable learning models. Who is the target audience? • Beginner Data Scientists who want to understand how to select variables for machine learning • Intermediate Data Scientists who want to level up their experience in feature selection for machine learning • Advanced Data Scientists who want to discover alternative methods for feature selection • Software engineers and academics switching careers into data science • Software engineers and academics stepping into data science • Data analysts who want to level up their skills in data science.
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カテゴリ:	Tutorials
言語:	English
合計サイズ:	397.11 MB
情報のハッシュ:	722B3338485097FF62F7925C2F6484415B2837C9
を追加することによって:	Prom3th3uS
追加日:	2018-11-25 12:19:27
トレントステータス:	Torrent Verified

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ファイルリスト:

ファイル	サイズ
01 Introduction/001 Introduction-en.srt	5.48 kB
01 Introduction/001 Introduction.mp4	4.62 MB
01 Introduction/002 Course Curriculum Overview-en.srt	4.91 kB
01 Introduction/002 Course Curriculum Overview.mp4	4.05 MB
01 Introduction/003 Course requirements-en.srt	4.43 kB
01 Introduction/003 Course requirements.mp4	6.42 MB
01 Introduction/004 Additional Requirements Nice to have.html	1.51 kB
01 Introduction/005 How to approach this course.html	2.38 kB
01 Introduction/006 Guide to setting up your computer.html	4.11 kB
01 Introduction/007 Installing XGBoost in windows.html	2.93 kB
01 Introduction/008 Feature-selection-presentations.zip	5.97 MB
01 Introduction/008 Presentations covered in this course.html	994.00 B
01 Introduction/009 Feature-selection-notebooks.zip	915.13 kB
01 Introduction/009 Jupyter notebooks covered in this course.html	994.00 B
01 Introduction/010 FAQ Data Science and Python programming.html	1.81 kB
02 Feature Selection/011 What is feature selection-en.srt	7.42 kB
02 Feature Selection/011 What is feature selection.mp4	7.82 MB
02 Feature Selection/012 Feature selection methods Overview-en.srt	7.30 kB
02 Feature Selection/012 Feature selection methods Overview.mp4	15.55 MB
02 Feature Selection/013 Filter Methods-en.srt	3.91 kB
02 Feature Selection/013 Filter Methods.mp4	4.87 MB
02 Feature Selection/014 Wrapper methods-en.srt	6.30 kB
02 Feature Selection/014 Wrapper methods.mp4	7.30 MB
02 Feature Selection/015 Embedded Methods-en.srt	4.93 kB
02 Feature Selection/015 Embedded Methods.mp4	9.53 MB
03 Filter Methods Basics/016 Constant quasi constant and duplicated features Intro-en.srt	4.95 kB
03 Filter Methods Basics/016 Constant quasi constant and duplicated features Intro.mp4	8.87 MB
03 Filter Methods Basics/017 Constant features-en.srt	12.76 kB
03 Filter Methods Basics/017 Constant features.mp4	14.50 MB
03 Filter Methods Basics/018 Quasi-constant features-en.srt	12.49 kB
03 Filter Methods Basics/018 Quasi-constant features.mp4	15.38 MB
03 Filter Methods Basics/019 Duplicated features-en.srt	8.64 kB
03 Filter Methods Basics/019 Duplicated features.mp4	20.70 MB
03 Filter Methods Basics/020 Basic methods review.html	4.61 kB
04 Filter methods Correlation/021 Correlation Intro-en.srt	6.63 kB
04 Filter methods Correlation/021 Correlation Intro.mp4	13.96 MB
04 Filter methods Correlation/022 Correlation-en.srt	18.68 kB
04 Filter methods Correlation/022 Correlation.mp4	24.38 MB
04 Filter methods Correlation/023 Basic methods plus Correlation pipeline.html	11.12 kB
05 Filter methods Statistical measures/024 Statistical methods Intro-en.srt	15.46 kB
05 Filter methods Statistical measures/024 Statistical methods Intro.mp4	16.57 MB
05 Filter methods Statistical measures/025 Mutual information-en.srt	9.97 kB
05 Filter methods Statistical measures/025 Mutual information.mp4	14.03 MB
05 Filter methods Statistical measures/026 Chi-square for categorical variables Fisher score-en.srt	5.57 kB
05 Filter methods Statistical measures/026 Chi-square for categorical variables Fisher score.mp4	7.27 MB
05 Filter methods Statistical measures/027 Univariate approaches-en.srt	12.21 kB
05 Filter methods Statistical measures/027 Univariate approaches.mp4	16.43 MB
05 Filter methods Statistical measures/028 Univariate ROC-AUC-en.srt	8.78 kB
05 Filter methods Statistical measures/028 Univariate ROC-AUC.mp4	10.87 MB
05 Filter methods Statistical measures/029 Basic methods Correlation univariate ROC-AUC pipeline.html	14.04 kB
05 Filter methods Statistical measures/030 BONUS select features by mean encoding KDD 2009.html	19.21 kB
06 Wrapper methods/031 Wrapper methods Intro-en.srt	8.38 kB
06 Wrapper methods/031 Wrapper methods Intro.mp4	15.55 MB
06 Wrapper methods/032 Step forward feature selection-en.srt	14.48 kB
06 Wrapper methods/032 Step forward feature selection.mp4	29.59 MB
06 Wrapper methods/033 Step backward feature selection-en.srt	14.46 kB
06 Wrapper methods/033 Step backward feature selection.mp4	32.07 MB
06 Wrapper methods/034 Exhaustive search-en.srt	10.26 kB
06 Wrapper methods/034 Exhaustive search.mp4	18.68 MB
07 Embedded methods Lasso regularisation/035 Least-angle-and-1-penalized-regression-A-review-.txt	68.00 B
07 Embedded methods Lasso regularisation/035 Machine-Learning-Explained-Regularization.txt	71.00 B
07 Embedded methods Lasso regularisation/035 Regularisation Intro-en.srt	6.78 kB
07 Embedded methods Lasso regularisation/035 Regularisation Intro.mp4	7.95 MB
07 Embedded methods Lasso regularisation/036 Lasso-en.srt	10.39 kB
07 Embedded methods Lasso regularisation/036 Lasso.mp4	13.93 MB
07 Embedded methods Lasso regularisation/037 Basic filter methods LASSO pipeline.html	16.14 kB
08 Embedded methods Linear models/038 Regression Coefficients Intro-en.srt	5.22 kB
08 Embedded methods Linear models/038 Regression Coefficients Intro.mp4	5.48 MB
08 Embedded methods Linear models/039 Selection by Logistic Regression Coefficients-en.srt	9.54 kB
08 Embedded methods Linear models/039 Selection by Logistic Regression Coefficients.mp4	20.16 MB
08 Embedded methods Linear models/040 Coefficients change with penalty-en.srt	6.74 kB
08 Embedded methods Linear models/040 Coefficients change with penalty.mp4	8.49 MB
08 Embedded methods Linear models/041 Selection by Linear Regression Coefficients-en.srt	3.94 kB
08 Embedded methods Linear models/041 Selection by Linear Regression Coefficients.mp4	5.08 MB
08 Embedded methods Linear models/042 Feature selection with linear models review.html	15.52 kB
09 Embedded methods Trees/043 Selecting Features by Tree importance Intro-en.srt	8.22 kB
09 Embedded methods Trees/043 Selecting Features by Tree importance Intro.mp4	9.28 MB
09 Embedded methods Trees/044 Select by model importance random forests embedded.html	15.11 kB
09 Embedded methods Trees/045 Select by model importance random forests recursively.html	11.08 kB
09 Embedded methods Trees/046 Select by model importance gradient boosted machines.html	9.64 kB
09 Embedded methods Trees/047 Feature selection with decision trees review.html	15.75 kB
10 Reading Resources/048 Additional reading resources.html	2.57 kB
11 Hybrid feature selection methods/049 BONUS Shuffling features.html	19.98 kB
11 Hybrid feature selection methods/050 BONUS Hybrid method Recursive feature elimination.html	48.79 kB
11 Hybrid feature selection methods/051 BONUS Hybrid method Recursive feature addition.html	51.08 kB
12 Final section Next steps/052 Bonus Lecture Discounts on my other courses.html	1.34 kB
Discuss.FreeTutorials.Us.html	165.68 kB
FreeCoursesOnline.Me.html	108.30 kB
FreeTutorials.Eu.html	102.23 kB
Presented By SaM.txt	33.00 B
Torrent Downloaded From GloDls.to.txt	84.00 B
[TGx]Downloaded from torrentgalaxy.org.txt	524.00 B

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[UDEMY] Feature Selection for Machine Learning - [FTU] GloDLS