[ FreeCourseWeb ] Udemy - Data Cleansing Master Class in Python

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[ FreeCourseWeb.com ] Udemy - Data Cleansing Master Class in Python
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 01 Introduction
    • 001 Course Introduction.en.srt (2.8 KB)
    • 001 Course Introduction.mp4 (22.6 MB)
    • 002 Course Structure.en.srt (3.6 KB)
    • 002 Course Structure.mp4 (23.9 MB)
    • 003 Is this Course Right for You_.en.srt (1.7 KB)
    • 003 Is this Course Right for You_.mp4 (1.6 MB)
    02 Foundations
    • 001 Introducing Data Preparation.en.srt (2.8 KB)
    • 001 Introducing Data Preparation.mp4 (36.4 MB)
    • 002 The Machine Learning Process.en.srt (5.4 KB)
    • 002 The Machine Learning Process.mp4 (14.3 MB)
    • 003 Data Preparation Defined.en.srt (3.8 KB)
    • 003 Data Preparation Defined.mp4 (30.2 MB)
    • 004 Choosing a Data Preparation Technique.en.srt (2.7 KB)
    • 004 Choosing a Data Preparation Technique.mp4 (25.9 MB)
    • 005 What is Data in Machine Learning_.en.srt (4.7 KB)
    • 005 What is Data in Machine Learning_.mp4 (17.9 MB)
    • 006 Raw Data.en.srt (8.2 KB)
    • 006 Raw Data.mp4 (20.5 MB)
    • 007 Machine Learning is Mostly Data Preparation.en.srt (4.1 KB)
    • 007 Machine Learning is Mostly Data Preparation.mp4 (40.9 MB)
    • 008 Common Data Preparation Tasks - Data Cleansing.en.srt (3.7 KB)
    • 008 Common Data Preparation Tasks - Data Cleansing.mp4 (21.7 MB)
    • 009 Common Data Preparation Tasks - Feature Selection.en.srt (3.5 KB)
    • 009 Common Data Preparation Tasks - Feature Selection.mp4 (7.9 MB)
    • 010 Common Data Preparation Tasks - Data Transforms.en.srt (3.9 KB)
    • 010 Common Data Preparation Tasks - Data Transforms.mp4 (4.7 MB)
    • 011 Common Data Preparation Tasks - Feature Engineering.en.srt (2.2 KB)
    • 011 Common Data Preparation Tasks - Feature Engineering.mp4 (21.6 MB)
    • 012 Common Data Preparation Tasks - Dimensionality Reduction.en.srt (2.9 KB)
    • 012 Common Data Preparation Tasks - Dimensionality Reduction.mp4 (4.1 MB)
    • 013 Data Leakage.en.srt (1.1 KB)
    • 013 Data Leakage.mp4 (8.8 MB)
    • 014 Problem With Naïve Data Preparation.en.srt (5.2 KB)
    • 014 Problem With Naïve Data Preparation.mp4 (24.9 MB)
    • 015 Case Study_ Data Leakage_ Train_Test_Split Naïve Approach.en.srt (3.9 KB)
    • 015 Case Study_ Data Leakage_ Train_Test_Split Naïve Approach.mp4 (16.5 MB)
    • 016 Case Study_ Data Leakage_ Train_Test_Split Correct Approach.en.srt (2.4 KB)
    • 016 Case Study_ Data Leakage_ Train_Test_Split Correct Approach.mp4 (9.5 MB)
    • 017 Case Study_ Data Leakage_ K-Fold Naïve Approach.en.srt (4.2 KB)
    • 017 Case Study_ Data Leakage_ K-Fold Naïve Approach.mp4 (14.3 MB)
    • 018 Case Study_ Data Leakage_ K-Fold Correct Approach.en.srt (3.1 KB)
    • 018 Case Study_ Data Leakage_ K-Fold Correct Approach.mp4 (12.8 MB)
    • Data Cleansing Master Class - Data Preparation With Training and Testing Sets.ipynb (6.6 KB)
    03 Data Cleansing
    • 001 Data Cleansing Overview.en.srt (2.2 KB)
    • 001 Data Cleansing Overview.mp4 (20.0 MB)
    • 002 Identify Columns That Contain a Single Value.en.srt (3.2 KB)
    • 002 Identify Columns That Contain a Single Value.mp4 (7.5 MB)
    • 003 Identify Columns with Few Values.en.srt (4.2 KB)
    • 003 Identify Columns with Few Values.mp4 (12.0 MB)
    • 004 Remove Columns with Low Variance.en.srt (3.8 KB)
    • 004 Remove Columns with Low Variance.mp4 (11.2 MB)
    • 005 Identify and Remove Rows That Contain Duplicate Data.en.srt (3.9 KB)
    • 005 Identify and Remove Rows That Contain Duplicate Data.mp4 (15.6 MB)
    • 006 Defining Outliers.en.srt (2.7 KB)
    • 006 Defining Outliers.mp4 (14.4 MB)
    • 007 Remove Outliers - The Standard Deviation Approach.en.srt (5.4 KB)
    • 007 Remove Outliers - The Standard Deviation Approach.mp4 (18.5 MB)
    • 008 Remove Outliers - The IQR Approach.en.srt (3.8 KB)
    • 008 Remove Outliers - The IQR Approach.mp4 (14.9 MB)
    • 009 Automatic Outlier Detection.en.srt (5.2 KB)
    • 009 Automatic Outlier Detection.mp4 (18.6 MB)
    • 010 Mark Missing Values.en.srt (6.8 KB)
    • 010 Mark Missing Values.mp4 (22.7 MB)
    • 011 Remove Rows with Missing Values.en.srt (2.4 KB)
    • 011 Remove Rows with Missing Values.mp4 (10.0 MB)
    • 012 Statistical Imputation.en.srt (2.0 KB)
    • 012 Statistical Imputation.mp4 (2.6 MB)
    • 013 Mean Value Imputation.en.srt (4.9 KB)
    • 013 Mean Value Imputation.mp4 (15.9 MB)
    • 014 Simple Imputer with Model Evaluation.en.srt (1.8 KB)
    • 014 Simple Imputer with Model Evaluation.mp4 (7.6 MB)
    • 015 Compare Different Statistical Imputation Strategies.en.srt (2.5 KB)
    • 015 Compare Different Statistical Imputation Strategies.mp4 (9.3 MB)
    • 016 K-Nearest Neighbors Imputation.en.srt (5.1 KB)
    • 016 K-Nearest Neighbors Imputation.mp4 (16.9 MB)
    • 017 KNNImputer and Model Evaluation.en.srt (3.4 KB)
    • 017 KNNImputer and Model Evaluation.mp4 (12.9 MB)
    • 018 Iterative Imputation.en.srt (4.1 KB)
    • 018 Iterative Imputation.mp4 (13.8 MB)
    • 019 IterativeImputer and Model Evaluation.en.srt (1.4 KB)
    • 019 IterativeImputer and Model Evaluation.mp4 (6.5 MB)
    • 020 IterativeImputer and Different Imputation Order.en.srt (2.2 KB)
    • 020 IterativeImputer and Different Imputation Order.mp4 (8.4 MB)
    • 030 housing.csv (47.9 KB)
    • Automatic Outlier Detection.ipynb (4.5 KB)
    • Comparing Different Imputed Statistics.ipynb (12.3 KB)
    • Identify and Remove Duplicate Rows.ipynb (1.8 KB)
    • IterativeImputer Data Transform.ipynb (2.4 KB)
    • IterativeImputer and Different Number of Iterations.ipynb (14.1 KB)
    • IterativeImputer and Model Evaluation.ipynb (2.4 KB)
    • KNNImputer and Model Evaluation Different K-Values.ipynb (13.6 KB)
    • Mark Missing Values.ipynb (11.3 KB)
    • Outlier Removal - IQR Approach.ipynb (1.8 KB)
    • Outlier Removal - Standard Deviation Approach.ipynb (2.3 KB)
    • Remove Missing Values.ipynb (9.4 KB)
    • SimpleImputer and Model Evaluation.ipynb (2.0 KB)
    • Sparse Column Identification and Removal.ipynb (20.6 KB)
    • Statistical Imputation With KNN.ipynb (7.2 KB)
    • Statistical Imputa

Description

Data Cleansing Master Class in Python



MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 104 lectures (3h 31m) | Size: 1.12 GB
The Complete Guide to Data Cleansing for Machine Learning Engineers
What you'll learn:
You'll learn data imputation and advanced data cleansing techniques.
You'll learn how to apply real-world data cleansing techniques to your data.
You'll learn advanced data cleansing techniques.
You'll learn how to prepare data in a way that avoids data leakage, and in turn, incorrect model evaluation.

Requirements
You'll need a really solid foundation in Python.
You'll need to understand the basics of machine learning.

Description
Welcome to Data Cleansing Master Class in Python.

Data preparation may be the most important part of a machine learning project. It is the most time consuming part, although it seems to be the least discussed topic. Data preparation, sometimes referred to as data preprocessing, is the act of transforming raw data into a form that is appropriate for modeling.

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[ FreeCourseWeb ] Udemy - Data Cleansing Master Class in Python

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[ FreeCourseWeb ] Udemy - Data Cleansing Master Class in Python

Torrent hash: E81491CF77A0E5DE54A96947BA2CACD2F7A2CC2B