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[ DevCourseWeb com ] Udemy - Machine Learning and Artificial Intelligence in Power BI
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1. Introduction to this course
1. Course Material.html
Material
Artificial Intelligence Charts
Exercises
Exercises_Supply Chain.pbix
Solution
Exercises_Solution_Supply Chain.pbix
data
Backorden Percentage.csv
Explanations.csv
Month.csv
Risk.csv
Sales.csv
Supply Analytics.csv
Exercises_Artificial Intelligence Charts.pdf
Sales Analytics.pbix
Data Analytics with Python in Power BI
Data Analytics with Seaborn.ipynb
Data analytics with Python.pbix
Data
Pokemon.csv
insurance.csv
Use Case_Boston Houses.pbix
Introduction to Power BI
Dashboard_Retail.pbix
Data
Channel.csv
District.csv
Item.csv
Sales.csv
Store.csv
Dax Reference Cheat Sheet.pbix
Exercises
Data
Channel.csv
Geography.csv
Product.csv
ProductCategory.csv
ProductSubcategory.csv
Promotion.csv
Sales.csv
Stores.csv
Solution
Exercise_Soluciton_Contoso PowerBi.pbix
Machine Learning and AI in Power BI
1. Regresion_sklearn.pbix
2. Regresion.pbix
3. Clasification_basic.pbix
3.1. Clasification_advance.pbix
4. Clustering.pbix
Exercises
Exercises_Solution
1. Regresion.pbix
2. Clasification.pbix
3. Clustering.pbix
Cooks Distance.png
Exercise_Solution_Regresion.ipynb
Learning Curve.png
Residuals.png
regresion_best_model_ejercicio.pkl
xgboost_cancer.pkl
cancer_pred.csv
dataset
blood.csv
cancer_pred.csv
clustering.csv
clustering2.csv
salary.xlsx
notebooks
Clustering.ipynb
Feature Importance.png
Final RF Model 11Nov2020.pkl
Prediction Error.png
Pycaret Fundamentals.ipynb
Regresion_ML model creation.ipynb
Residuals.png
clustering.csv
10. Regression models with Python in Power BI
1. Fundamentals of regression models with Pycaret in Power BI.mp4
2. Applied project_Development of a regression model of XGBoost.mp4
3. Applied project_Integration of the XGBoost model in Power BI.mp4
4. Applied project_Adding model evaluation charts to PowerBI.mp4
5. Exercise 1. Regression models in Power BI.mp4
6. Solution Exercise 1.mp4
11. Classification models with Python in Power BI
1. Fundamentals of classification models with Pycaret in Power BI.mp4
2. Evaluation metrics of classification models.mp4
3. Applied project_Development of a classification model in Power BI.mp4
4. Applied project_Model load and prediction in Power BI.mp4
5. Applied project_Applying advanced pre-processing to the data.mp4
6. Applied project_Evaluation of the classification model in Power BI.mp4
7. Exercise 2. Classification models in Power BI.mp4
8. Solution_Exercise 2.mp4
12. Clustering models with Python in Power BI
1. Fundamentals of Clustering Models with Pycaret in Power BI.mp4
2. Clustering model evaluation metrics.mp4
3. Applied project_Development of a clustering model in Power BI.mp4
4. Applied project_Development of the model in Jupyter and integration in Power BI.mp4
5. Exercise 3. Clustering models in Power BI.mp4
6. Solution Exercise 3.mp4
2. Introducción a Power BI
1. Introducción a Power Bi.mp4
2. Descarga y presentación de Power BI Desktop.mp4
3. Importación de datos.mp4
4. Herramientas para analizar la calidad del dato.mp4
5. Funciones de pre-procesamiento de datos.mp4
3. Artificial intelligence graphics
1. Visual Q&A.mp4
10. Predictions with time series.mp4
11. Solution Exercise 4.mp4
12. Detection of anomalies in time series.mp4
13. Solution Exercise 5.mp4
2. Configuring the Q&A visual.mp4
3. Solution Exercise 1.mp4
4. Key Influencers.mp4
5. Major Chart Segments Key Influencers.mp4
6. Correlation vs Causation.mp4
7. Solution Exercise 2.mp4
8. Exercise 3 Hierarchical scheme.mp4
9. Solution Exercise 3.mp4
4. Install Python and sync with Power BI
1. Install Python and synchronization with Power BI.mp4
2. Installing Pycaret.mp4
3. Jupyter Notebook Fundamentals.mp4
5. Advanced analytics
1. How to run python scripts.mp4
2. Seaborn Basics.mp4
3. Selecting the correct chart type.mp4
4. Applied project_Data preprocessing with Python.mp4
5. Applied project_Analysis of numerical variables with Seaborn.mp4
6. Machine Learning Fundamentals
1. Introduction to AI.mp4
2. Types of Machine Learning Models.mp4
3. Phases of training Machine Learning models.mp4
4. Main Machine Learning algorithms.mp4
7. Deploy models in Power BI
1. Deploy models in Power BI.mp4
8. Machine Learning models with Scikit-learn
1. Entrenando un modelo de regresión con Sklearn en Power BI.mp4
2. Evaluation and obtaining of metrics of the Sklearn regression model.mp4
9. AutoML Fundamentals with Python and Pycaret
1. Introduction to autoML.mp4
2. Training and optimization of models with Pycaret.mp4
3. Model evaluation and deployment with Pycaret.mp4
Bonus Resources.txt
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