Udemy Data Mining for Business Analytics Data Analysis in Python

[0dayddl]

​

Download Free Download : Udemy Data Mining for Business Analytics Data Analysis in Python
mp4 | Video: h264,1280X720 | Audio: AAC, 44.1 KHz
Genre:eLearning | Language: English | Size:2.18 GB
​

Files Included :

Spoiler

001 Introduction to Data Mining course for Business Analytics & Data Analysis.mp4 (55.67 MB)
MP4
003 Course Resources, Material, and Colab setup - Important!.mp4 (41.47 MB)
MP4
004 How to get more from the course.mp4 (22.75 MB)
MP4
005 Reviews and the future of the course.mp4 (32.37 MB)
MP4
001 Game Plan for Survival Analysis section.mp4 (49.48 MB)
MP4
002 Survival Analyisis Introduction.mp4 (15.53 MB)
MP4
003 Case Study Briefing and Step by Step Guide.mp4 (3.52 MB)
MP4
004 Python - Changing Directory.mp4 (10.59 MB)
MP4
005 Python - Importing Libraries.mp4 (8.39 MB)
MP4
006 Python - Loading Data.mp4 (13.79 MB)
MP4
007 Python - Transforming Dependent Variable.mp4 (12.09 MB)
MP4
008 Kaplan-Meyer Estimator.mp4 (5.9 MB)
MP4
009 Censoring.mp4 (5.99 MB)
MP4
010 Python - Kaplan-Meyer Estimator.mp4 (8.38 MB)
MP4
011 Python - Calculating Specific Events.mp4 (30.81 MB)
MP4
012 Python - Plotting Survival Curves.mp4 (5.66 MB)
MP4
013 Python - Plotting Cumulative Curves.mp4 (11.87 MB)
MP4
014 Log Rank Test.mp4 (2.86 MB)
MP4
015 Python - Subsetting Dataframe.mp4 (3.69 MB)
MP4
016 Python - Kaplan-Meyer Estimator per Gender.mp4 (9.9 MB)
MP4
017 Python - Plotting both Survival Curves.mp4 (11.1 MB)
MP4
018 Python - Log Rank Test.mp4 (8.79 MB)
MP4
019 Extra Resources and Survival Analysis Challenge.mp4 (21.81 MB)
MP4
020 Python - Survival Analysis Challenge Solutions.mp4 (37.42 MB)
MP4
001 Game Plan.mp4 (4.84 MB)
MP4
002 Cox Proportional Hazard Regression.mp4 (6.37 MB)
MP4
003 Case Study Briefing and Step by Step Guide.mp4 (3.01 MB)
MP4
004 Python - Preparing Script and Data.mp4 (46.09 MB)
MP4
005 Python - Cox Proportional Hazard.mp4 (31.5 MB)
MP4
006 Python - Regression Summary Visualization.mp4 (5.89 MB)
MP4
007 Extra Resources and Challenge.mp4 (25.62 MB)
MP4
008 Python - Solution Challenges.mp4 (37.33 MB)
MP4
001 Game Plan.mp4 (7.44 MB)
MP4
002 Case Study Briefing and Step by Step Guide.mp4 (4.52 MB)
MP4
003 Problem Statement.mp4 (6 MB)
MP4
004 Python - Installing libraries.mp4 (13.67 MB)
MP4
005 Python - Importing Libraries and Data.mp4 (16.04 MB)
MP4
006 Introducing CHAID.mp4 (7.78 MB)
MP4
007 CHAID Statistics and Quirks.mp4 (9.7 MB)
MP4
008 Python - Removing column and unique values check.mp4 (11.42 MB)
MP4
009 Python - Visualizing Jobs Variable.mp4 (5.07 MB)
MP4
010 Python - Transforming Jobs Variable.mp4 (15.18 MB)
MP4
011 Python - Transforming Experience Variable.mp4 (11.62 MB)
MP4
012 Python - Transform Minimum Variable.mp4 (14.48 MB)
MP4
013 Python - Modify other variables to dummy variables.mp4 (3.01 MB)
MP4
014 Python - CHAID Preparation.mp4 (5.64 MB)
MP4
015 Python - CHAID Model.mp4 (25.54 MB)
MP4
016 Python - Data Visualization with CHAID Model.mp4 (79.66 MB)
MP4
017 Extra Resources and Challenge.mp4 (46.57 MB)
MP4
018 Python - Challenge solutions.mp4 (51.68 MB)
MP4
001 Game Plan.mp4 (4.46 MB)
MP4
002 Case Study Briefing and Clustering.mp4 (14.32 MB)
MP4
003 Gaussian Mixture Model vs Kmeans.mp4 (11.66 MB)
MP4
004 Python - Changing Directory and Importing Libraries.mp4 (8.4 MB)
MP4
005 Python - Loading Data.mp4 (11.13 MB)
MP4
006 AIC, BIC, and Step-by-Step Guide.mp4 (5.11 MB)
MP4
007 Python - Optimal Clusters.mp4 (30.38 MB)
MP4
008 Python - Gaussian Mixture Model.mp4 (3.89 MB)
MP4
009 Python - Cluster Prediction.mp4 (18.56 MB)
MP4
010 Python - Probability of belonging to each cluster.mp4 (20.33 MB)
MP4
011 Python - Cluster Interpretation.mp4 (28.79 MB)
MP4
012 Extra Resources and Challenge.mp4 (21.83 MB)
MP4
013 Python - Challenge solutions.mp4 (27.82 MB)
MP4
001 Game Plan.mp4 (4.79 MB)
MP4
002 What is Dimension Reduction.mp4 (9.42 MB)
MP4
003 Principal Component Analysis.mp4 (6.55 MB)
MP4
004 Python - Importing Libraries.mp4 (7.78 MB)
MP4
005 Python - Loading Data.mp4 (7.24 MB)
MP4
006 Python - Transforming String Variables.mp4 (6.06 MB)
MP4
007 Python - Correlation Matrix.mp4 (11.37 MB)
MP4
008 Python - Standardizing Variables.mp4 (7.61 MB)
MP4
009 Python - Optimal Number of Components.mp4 (12.7 MB)
MP4
010 Python - Cumulative Explained Variance.mp4 (5.31 MB)
MP4
011 Python - PCA.mp4 (8.16 MB)
MP4
012 Python - PCA interpretation.mp4 (37.04 MB)
MP4
013 Manifold Learning and t-SNE.mp4 (15.91 MB)
MP4
014 Python - t-SNE.mp4 (6.34 MB)
MP4
015 Python -Visualizing Manifold Learning.mp4 (16.54 MB)
MP4
016 Extra Resources and Challenge.mp4 (20.08 MB)
MP4
017 Python - Challenge Solutions.mp4 (88.46 MB)
MP4
001 Game Plan.mp4 (3.08 MB)
MP4
002 Step by Step Guide and Case Study Briefing.mp4 (4.67 MB)
MP4
003 Python - Importing Libraries.mp4 (7.83 MB)
MP4
004 Python - Loading Data.mp4 (6.12 MB)
MP4
005 Association Rule Learning.mp4 (9.12 MB)
MP4
006 Python - Create Transaction List.mp4 (28.36 MB)
MP4
007 Python - Encoding Transactions.mp4 (22.72 MB)
MP4
008 Apriori algorithm.mp4 (5.04 MB)
MP4
009 Python - Association Rule Learning.mp4 (18.78 MB)
MP4
010 Python - Apriori Visualization.mp4 (27.94 MB)
MP4
011 Extra Resources and Challenge.mp4 (19.37 MB)
MP4
012 Python - Challenge Solutions.mp4 (59.15 MB)
MP4
001 Game Plan for Random Forest.mp4 (2.56 MB)
MP4
002 Case Study Briefing and Step by Step Guide.mp4 (6.51 MB)
MP4
003 Python - Importing Libraries.mp4 (7.75 MB)
MP4
004 Python - Loading Data.mp4 (7 MB)
MP4
005 Python - Transforming Categorical Variables.mp4 (4.62 MB)
MP4
006 Random Forest.mp4 (10.19 MB)
MP4
007 Python - Training and Test Set.mp4 (20.43 MB)
MP4
008 Python - Random Forest.mp4 (7.65 MB)
MP4
009 Confusion Matrix, AUC, and F1-Score.mp4 (12.6 MB)
MP4
010 Python - Random Forest Predictions.mp4 (7.89 MB)
MP4
011 Python - Classification Report.mp4 (18.06 MB)
MP4
012 Python - Feature Importance for Business Analytics.mp4 (13.45 MB)
MP4
013 Extra Resources and Challenge.mp4 (32.7 MB)
MP4
014 Python - Challenge Solutions.mp4 (33.42 MB)
MP4
001 Game Plan for Explainable Artificial Intelligence.mp4 (2.38 MB)
MP4
002 LIME.mp4 (9.32 MB)
MP4
003 Python - Preparing LIME.mp4 (54.7 MB)
MP4
004 Python - Explaining Predictions.mp4 (14.96 MB)
MP4
005 Extra Resources and Challenge.mp4 (23.05 MB)
MP4
006 Python - Challenge Solutions.mp4 (50.77 MB)
MP4
001 Game Plan for XGBoost and SHAP.mp4 (2.83 MB)
MP4
002 Case Study Briefing and Step by Step Guide.mp4 (3.22 MB)
MP4
003 Python - Importing Libraries.mp4 (5.68 MB)
MP4
004 Python - Loading Data.mp4 (15.34 MB)
MP4
005 Introducing XGBoost.mp4 (5.38 MB)
MP4
006 How XGBoost works part 1.mp4 (11.6 MB)
MP4
007 How XGBoost works part 2.mp4 (6.08 MB)
MP4
008 XGBoost quirks.mp4 (1.67 MB)
MP4
009 Python - Isolate X and Y.mp4 (2.64 MB)
MP4
010 Python - Training and Test Set.mp4 (4.7 MB)
MP4
011 Python - XGBoost Matrices.mp4 (4.54 MB)
MP4
012 XGBoost Parameters.mp4 (6.66 MB)
MP4
013 Python - XGBoost Parameters.mp4 (7.18 MB)
MP4
014 Python - XGBoost Model.mp4 (13.4 MB)
MP4
015 Evaluate Regression-based Problems.mp4 (5.48 MB)
MP4
016 Python - Predictions.mp4 (2.48 MB)
MP4
017 Python - MAE and RSME.mp4 (12.61 MB)
MP4
018 SHAP.mp4 (8.17 MB)
MP4
019 Python - Preparing SHAP.mp4 (2.4 MB)
MP4
020 Python - Local Interpretability.mp4 (27.06 MB)
MP4
021 Python - Dependency Plots.mp4 (11.23 MB)
MP4
022 Python - Global Interpretability.mp4 (4.89 MB)
MP4
023 Extra Resources and Challenge.mp4 (16.51 MB)
MP4
024 Python - Challenge Solutions.mp4 (64.81 MB)
MP4

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

 

[0dayddl]

Data Mining For Business Analytics & Data Analysis In Python
Last updated 9/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English (US) | Size: 1.96 GB | Duration: 9h 0m​

Python for Data Analytics & Explainable Artificial Intelligence. Data Mining for Business Data Analytics & Intelligence.

What you'll learn
Identify the value of data mining for quickly analyzing and interpreting data.
Apply data mining algorithms using Python programming language for Business Analytics.
Explain the principles behind various data mining algorithms, including supervised and unsupervised machine learning, and explainable AI
Explain the results of data mining models using explainable artificial intelligence models: LIME and SHAP.
Practice applying data mining techniques through hands-on exercises and case studies.
Implement cluster analysis, dimension reduction, and association rule learning using Python.
Perform survival analysis, Cox proportional hazard regression, and CHAID using Python.
Use random forest and feature selection to improve the accuracy of data mining models.
Develop a portfolio of data mining projects for Business Data Analytics and Intelligence.
Use data mining techniques to inform business decisions and strategies.

Requirements
Statistics - Linear and Logistic Regression
Basic Python

Description
Are you looking to learn how to do Data Mining like a pro? Do you want to find actionable business insights using data science and analytics and explainable artificial intelligence? You have come to the right place.I will show you the most impactful Data Mining algorithms using Python that I have witnessed in my professional career to derive meaningful insights and interpret data.In the age of endless spreadsheets, it is easy to feel overwhelmed with so much data. This is where Data Mining techniques come in. To swiftly analyze, find patterns, and deliver an outcome to you. For me, the Data Mining value added is that you stop the number crunching and pivot table creation, leaving time to come with actionable plans based on the insights.Now, why should you enroll in the course? Let me give you four reasons.The first is that you will learn the models' intuition without focusing too much on the math. It is crucial that you know why a model makes sense and the underlying assumptions behind it. I will explain to you each model using words, graphs, and metaphors, leaving math and the Greek alphabet to the bare minimum.The second reason is the thorough course structure of the most impactful Data Mining techniques for Data Science and Business Analytics. Based on my experience, the course curriculum has the algorithms I believe to be most impactful, up-to-date, and sought after. Here is the list of the algorithms we will learn:Supervised Machine LearningSurvival AnalysisCox Proportional Hazard RegressionCHAIDUnsupervised Machine LearningCluster Analysis - Gaussian Mixture ModelDimension Reduction - PCA and Manifold LearningAssociation Rule Learning· Explainable Artificial IntelligenceRandom Forest and Feature Seletion and ImportanceLIMEXGBoost and SHAPThe third reason is that we code Python together, line by line. Programming is challenging, especially for beginners. I will guide you through every Python code snippet. I will also explain all parameters and functions that you need to use, step by step. In the end, you will have code templates ready to use in your problems.The final reason is that you practice, practice, practice. At the end of each section, there is a challenge. The goal is that you apply immediately what you have learned. I give you a dataset and a list of actions you need to take to solve it. I think it is the best way to really cement all the techniques in you. Hence, there will be 2 case studies per technique.I hope to have spiked your interest, and I am looking forward to seeing you inside!

Who this course is for:
Professionals looking to learn Data Mining algorithms,Data Analysts starting to learn Data Mining techniques,Business Analysts looking to learn algorithms on how to uncover business insights,Any Python programmer who would like to learn Data Mining tools

For More Courses Visit & Bookmark Your Preferred Language Blog
From Here: - - - - - - - -

AusFile

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

RapidGator

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

TurboBit

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!