Udemy - Master Python Data Analysis and Modelling Essentials

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[ DevCourseWeb.com ] Udemy - Master Python Data Analysis and Modelling Essentials
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 01 - Introduction
    • 001 Introduction to Course Contents.mp4 (99.4 MB)
    • 001 Introduction to Course Contents_en.srt (7.3 KB)
    • 001 contents-intr.pdf (1.1 MB)
    • 002 Introduction to Data Analysis and Modelling.mp4 (36.6 MB)
    • 002 Introduction to Data Analysis and Modelling_en.srt (4.9 KB)
    • 002 data-analysis-model-intr-notes.pdf (554.5 KB)
    02 - Setting up Python Environment
    • 001 Installing Anaconda Python.mp4 (51.8 MB)
    • 001 Installing Anaconda Python_en.srt (7.6 KB)
    • 001 install-anaconda-python-notes.pdf (144.4 KB)
    • 002 Required Python Packages.mp4 (23.0 MB)
    • 002 Required Python Packages_en.srt (3.3 KB)
    • 002 packages-required-notes.pdf (338.8 KB)
    • 003 Installing Required Packages.mp4 (55.9 MB)
    • 003 Installing Required Packages_en.srt (8.7 KB)
    • 003 install-packages-required-notes.pdf (396.8 KB)
    • 004 Creating and Accessing Working Directory.mp4 (37.2 MB)
    • 004 Creating and Accessing Working Directory_en.srt (7.3 KB)
    • 004 creat-access-work-directory-notes.pdf (134.5 KB)
    03 - Data Exploration
    • 001 Reading and Writing Data.mp4 (115.1 MB)
    • 001 Reading and Writing Data_en.srt (12.4 KB)
    • 001 read-write-data-notes.pdf (353.7 KB)
    • 002 Accessing Basic Information of DataFrame.mp4 (90.8 MB)
    • 002 Accessing Basic Information of DataFrame_en.srt (8.7 KB)
    • 002 acess-information-notes.pdf (483.6 KB)
    • 003 Renaming Columns of DataFrame.mp4 (121.5 MB)
    • 003 Renaming Columns of DataFrame_en.srt (11.3 KB)
    • 003 rename-columns-notes.pdf (277.6 KB)
    • 004 Slicing DataFrame.mp4 (137.9 MB)
    • 004 Slicing DataFrame_en.srt (15.2 KB)
    • 004 slicing-data-notes.pdf (713.0 KB)
    • 005 Sorting DataFrame.mp4 (70.0 MB)
    • 005 Sorting DataFrame_en.srt (6.5 KB)
    • 005 sort-dataframe-notes.pdf (425.9 KB)
    • 006 Filtering DataFrame.mp4 (140.7 MB)
    • 006 Filtering DataFrame_en.srt (15.6 KB)
    • 006 filter-dataframe-notes.pdf (496.7 KB)
    • 007 Grouping DataFrame.mp4 (69.7 MB)
    • 007 Grouping DataFrame_en.srt (7.0 KB)
    • 007 group-dataframe-notes.pdf (291.6 KB)
    • 008 Calculating Summary Statistics of DataFrame.mp4 (113.1 MB)
    • 008 Calculating Summary Statistics of DataFrame_en.srt (12.1 KB)
    • 008 calculate-statistics-notes.pdf (331.7 KB)
    04 - Data Preparation
    • 001 Detecting Missing Values.mp4 (109.8 MB)
    • 001 Detecting Missing Values_en.srt (12.1 KB)
    • 001 missing-value-detection-notes.pdf (303.5 KB)
    • 002 Imputing Missing Values.mp4 (145.4 MB)
    • 002 Imputing Missing Values_en.srt (16.7 KB)
    • 002 impute-missing-values-notes.pdf (281.8 KB)
    • 003 Detecting Outliers.mp4 (184.7 MB)
    • 003 Detecting Outliers_en.srt (21.2 KB)
    • 003 outlier-detection-notes.pdf (705.1 KB)
    • 004 Treating Outliers.mp4 (149.1 MB)
    • 004 Treating Outliers_en.srt (14.3 KB)
    • 004 outlier-treatement-notes.pdf (520.8 KB)
    • 005 Correlation Analysis and Feature Selection.mp4 (277.2 MB)
    • 005 Correlation Analysis and Feature Selection_en.srt (24.1 KB)
    • 005 correlation-feature-selection-notes.pdf (797.8 KB)
    • 006 Encoding Categorical Values.mp4 (221.0 MB)
    • 006 Encoding Categorical Values_en.srt (21.8 KB)
    • 006 encode-categorical-notes.pdf (523.6 KB)
    • 007 Data Splitting.mp4 (80.9 MB)
    • 007 Data Splitting_en.srt (8.5 KB)
    • 007 split-data-notes.pdf (378.9 KB)
    • 008 Data Normalization.mp4 (275.2 MB)
    • 008 Data Normalization_en.srt (24.4 KB)
    • 008 data-normalization-notes.pdf (820.0 KB)
    05 - Classic Statistical Linear Regression Models
    • 001 Statistical Modelling Process.mp4 (26.3 MB)
    • 001 Statistical Modelling Process_en.srt (3.3 KB)
    • 001 classic-linear-statmodel-process-notes.pdf (135.2 KB)
    • 002 Data Normalization in Classic Statistical Regression.mp4 (51.7 MB)
    • 002 Data Normalization in Classic Statistical Regression_en.srt (4.9 KB)
    • 002 classic-linear-statmodel-nores-contains-all-the-rest-of-the-section.pdf (634.7 KB)
    • 003 Model Estimation and Result Interpretation.mp4 (93.2 MB)
    • 003 Model Estimation and Result Interpretation_en.srt (9.4 KB)
    • 004 Multicollinearity.mp4 (85.7 MB)
    • 004 Multicollinearity_en.srt (7.9 KB)
    • 005 Model Improvement.mp4 (106.6 MB)
    • 005 Model Improvement_en.srt (10.3 KB)
    • 006 Model Evaluation.mp4 (75.8 MB)
    • 006 Model Evaluation_en.srt (6.8 KB)
    • 007 Model Result Visualization.mp4 (58.0 MB)
    • 007 Model Result Visualization_en.srt (6.3 KB)
    06 - Machine Learning Linear Regression Models
    • 001 Machine Learning Modelling Process.mp4 (12.0 MB)
    • 001 Machine Learning Modelling Process_en.srt (1.5 KB)
    • 001 ml-linear-model-process-notes.pdf (185.4 KB)
    • 002 Model Trainning.mp4 (43.8 MB)
    • 002 Model Trainning_en.srt (4.8 KB)
    • 002 machine-learn-regression-notes.pdf (676.5 KB)
    • 003 Model Evaluation.mp4 (34.7 MB)
    • 003 Model Evaluation_en.srt (2.8 KB)
    • 004 Model Improvement.mp4 (119.6 MB)
    • 004 Model Improvement_en.srt (10.5 KB)
    • 005 Model Result Visualization.mp4 (114.5 MB)
    • 005 Model Result Visualization_en.srt (12.4 KB)
    • Bonus Resources.txt (0.4 KB)

Description

Master Python Data Analysis and Modelling Essentials



https://DevCourseWeb.com

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 34 lectures (7h 2m) | Size: 2.97 GB

A Real-World Project using Jupyter notebook, Numpy, SciPy, Pandas, Matplotlib, Statmodels, Scikit-learn, and many more

What you'll learn
Data analysis and modelling process
Setting up Python data analysis and modelling environment
Data exploration
Rename the data columns
Data slicing, sorting, filtering, and grouping data
Missing value detection and imputation
Outlier detection and treatment
Correlation Analysis and feature selection
Splitting data set for model fitting and testing
Data normalization with different methods
Developing a classic statistical linear regression model
Developing a machine linear regression model
interpreting the model results
Improving the models
Evaluating the models
Visualizing the model results

Requirements
Basic Python language knowledge needed to understand the codes



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Udemy - Master Python Data Analysis and Modelling Essentials

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Download torrent
3.4 GB
seeders:15
leechers:12
Udemy - Master Python Data Analysis and Modelling Essentials

Torrent hash: AC2824B41153E0FE4B731D71E3DBF4987C3E1A54