Udemy - Machine Learning and Artificial Intelligence in Power BI

Files:

• Get Bonus Downloads Here.url (0.2 KB)
~Get Your Files Here ! 1. Introduction to this course
• 1. Course Material.html (0.1 KB)
Material Artificial Intelligence Charts Exercises
• Exercises_Supply Chain.pbix (1.0 MB)
Solution
• Exercises_Solution_Supply Chain.pbix (1.0 MB)
data
• Backorden Percentage.csv (27.0 KB)
• Explanations.csv (1.0 KB)
• Month.csv (0.1 KB)
• Risk.csv (972.0 KB)
• Sales.csv (60.0 KB)
• Supply Analytics.csv (1.3 MB)
• Exercises_Artificial Intelligence Charts.pdf (181.1 KB)
• Sales Analytics.pbix (2.0 MB)
Data Analytics with Python in Power BI
• Data Analytics with Seaborn.ipynb (1,020.6 KB)
• Data analytics with Python.pbix (1.6 MB)
Data
• Pokemon.csv (43.0 KB)
• insurance.csv (54.3 KB)
• Use Case_Boston Houses.pbix (1.1 MB)
Introduction to Power BI
• Dashboard_Retail.pbix (857.9 KB)
Data
• Channel.csv (0.1 KB)
• District.csv (0.3 KB)
• Item.csv (1.3 MB)
• Sales.csv (1.6 MB)
• Store.csv (16.7 KB)
• Dax Reference Cheat Sheet.pbix (162.1 KB)
Exercises Data
• Channel.csv (0.1 KB)
• Geography.csv (22.2 KB)
• Product.csv (290.5 KB)
• ProductCategory.csv (0.2 KB)
• ProductSubcategory.csv (1.0 KB)
• Promotion.csv (2.3 KB)
• Sales.csv (2.0 MB)
• Stores.csv (15.8 KB)
Solution
• Exercise_Soluciton_Contoso PowerBi.pbix (957.9 KB)
Machine Learning and AI in Power BI
• 1. Regresion_sklearn.pbix (66.8 KB)
• 2. Regresion.pbix (456.7 KB)
• 3. Clasification_basic.pbix (77.1 KB)
• 3.1. Clasification_advance.pbix (91.3 KB)
• 4. Clustering.pbix (471.8 KB)
Exercises Exercises_Solution
• 1. Regresion.pbix (456.1 KB)
• 2. Clasification.pbix (109.8 KB)
• 3. Clustering.pbix (137.5 KB)
• Cooks Distance.png (9.8 KB)
• Exercise_Solution_Regresion.ipynb (68.1 KB)
• Learning Curve.png (32.3 KB)
• Residuals.png (28.9 KB)
• regresion_best_model_ejercicio.pkl (194.4 KB)
• xgboost_cancer.pkl (133.9 KB)
• cancer_pred.csv (0.3 KB)
dataset
• blood.csv (11.0 KB)
• cancer_pred.csv (0.3 KB)
• clustering.csv (25.2 KB)
• clustering2.csv (128.3 KB)
• salary.xlsx (10.0 KB)
notebooks
• Clustering.ipynb (4.2 MB)
• Feature Importance.png (15.8 KB)
• Final RF Model 11Nov2020.pkl (700.7 KB)
• Prediction Error.png (24.8 KB)
• Pycaret Fundamentals.ipynb (436.8 KB)
• Regresion_ML model creation.ipynb (58.0 KB)
• Residuals.png (24.2 KB)
• clustering.csv (25.2 KB)
10. Regression models with Python in Power BI
• 1. Fundamentals of regression models with Pycaret in Power BI.mp4 (30.4 MB)
• 2. Applied project_Development of a regression model of XGBoost.mp4 (48.6 MB)
• 3. Applied project_Integration of the XGBoost model in Power BI.mp4 (26.8 MB)
• 4. Applied project_Adding model evaluation charts to PowerBI.mp4 (28.3 MB)
• 5. Exercise 1. Regression models in Power BI.mp4 (7.1 MB)
• 6. Solution Exercise 1.mp4 (56.0 MB)
11. Classification models with Python in Power BI
• 1. Fundamentals of classification models with Pycaret in Power BI.mp4 (15.8 MB)
• 2. Evaluation metrics of classification models.mp4 (16.5 MB)
• 3. Applied project_Development of a classification model in Power BI.mp4 (37.3 MB)
• 4. Applied project_Model load and prediction in Power BI.mp4 (14.9 MB)
• 5. Applied project_Applying advanced pre-processing to the data.mp4 (35.8 MB)
• 6. Applied project_Evaluation of the classification model in Power BI.mp4 (43.5 MB)
• 7. Exercise 2. Classification models in Power BI.mp4 (5.6 MB)
• 8. Solution_Exercise 2.mp4 (40.7 MB)
12. Clustering models with Python in Power BI
• 1. Fundamentals of Clustering Models with Pycaret in Power BI.mp4 (10.8 MB)
• 2. Clustering model evaluation metrics.mp4 (4.7 MB)
• 3. Applied project_Development of a clustering model in Power BI.mp4 (61.0 MB)
• 4. Applied project_Development of the model in Jupyter and integration in Power BI.mp4 (66.0 MB)
• 5. Exercise 3. Clustering models in Power BI.mp4 (5.0 MB)
• 6. Solution Exercise 3.mp4 (26.3 MB)
2. Introducción a Power BI
• 1. Introducción a Power Bi.mp4 (15.2 MB)
• 2. Descarga y presentación de Power BI Desktop.mp4 (30.2 MB)
• 3. Importación de datos.mp4 (34.9 MB)
• 4. Herramientas para analizar la calidad del dato.mp4 (31.5 MB)
• 5. Funciones de pre-procesamiento de datos.mp4 (49.0 MB)
3. Artificial intelligence graphics
• 1. Visual Q&A.mp4 (27.4 MB)
• 10. Predictions with time series.mp4 (34.6 MB)
• 11. Solution Exercise 4.mp4 (18.5 MB)
• 12. Detection of anomalies in time series.mp4 (36.0 MB)
• 13. Solution Exercise 5.mp4 (26.3 MB)
• 2. Configuring the Q&A visual.mp4 (27.4 MB)
• 3. Solution Exercise 1.mp4 (31.0 MB)
• 4. Key Influencers.mp4 (42.2 MB)
• 5. Major Chart Segments Key Influencers.mp4 (19.2 MB)
• 6. Correlation vs Causation.mp4 (11.8 MB)
• 7. Solution Exercise 2.mp4 (19.5 MB)
• 8. Exercise 3 Hierarchical scheme.mp4 (45.2 MB)
• 9. Solution Exercise 3.mp4 (22.6 MB)
4. Install Python and sync with Power BI
• 1. Install Python and synchronization with Power BI.mp4 (33.2 MB)
• 2. Installing Pycaret.mp4 (15.6 MB)
• 3. Jupyter Notebook Fundamentals.mp4 (23.0 MB)
5. Advanced analytics
• 1. How to run python scripts.mp4 (23.0 MB)
• 2. Seaborn Basics.mp4 (20.4 MB)
• 3. Selecting the correct chart type.mp4 (18.0 MB)
• 4. Applied project_Data preprocessing with Python.mp4 (73.6 MB)
• 5. Applied project_Analysis of numerical variables with Seaborn.mp4 (33.8 MB)
6. Machine Learning Fundamentals
• 1. Introduction to AI.mp4 (15.1 MB)
• 2. Types of Machine Learning Models.mp4 (18.2 MB)
• 3. Phases of training Machine Learning models.mp4 (8.1 MB)
• 4. Main Machine Learning algorithms.mp4 (7.7 MB)
7. Deploy models in Power BI
• 1. Deploy models in Power BI.mp4 (18.3 MB)
8. Machine Learning models with Scikit-learn
• 1. Entrenando un modelo de regresión con Sklearn en Power BI.mp4 (32.4 MB)
• 2. Evaluation and obtaining of metrics of the Sklearn regression model.mp4 (35.7 MB)
9. AutoML Fundamentals with Python and Pycaret
• 1. Introduction to autoML.mp4 (9.5 MB)
• 2. Training and optimization of models with Pycaret.mp4 (57.2 MB)
• 3. Model evaluation and deployment with Pycaret.mp4 (33.2 MB)
• Bonus Resources.txt (0.4 KB)