Udemy - Machine Learning Basics: Classification models in Python ...

Files:

• 1. Welcome to the course!.mp4 (17.6 MB)
• 1. Welcome to the course!.vtt (3.3 KB)
• 1.1 00_Introduction_01_py.pdf.pdf (472.2 KB)

• 1. Introduction to Machine Learning.mp4 (123.8 MB)
• 1. Introduction to Machine Learning.vtt (16.3 KB)
• 1.1 Lecture_machineLearning.pdf.pdf (991.6 KB)
• 2. Building a Machine Learning model.mp4 (45.3 MB)
• 2. Building a Machine Learning model.vtt (8.6 KB)

• 1. Types of Data.mp4 (25.9 MB)
• 1. Types of Data.vtt (4.3 KB)
• 1.1 01_01_Lecture_TypesOfData.pdf.pdf (177.7 KB)
• 2. Types of Statistics.mp4 (13.2 MB)
• 2. Types of Statistics.vtt (2.7 KB)
• 2.1 01_02_Lecture_TypesOfStatistics.pdf.pdf (171.7 KB)
• 3. Describing data Graphically.mp4 (82.2 MB)
• 3. Describing data Graphically.vtt (11.3 KB)
• 3.1 01_03_Lecture_DataSummaryandGraph.pdf.pdf (317.9 KB)
• 4. Measures of Centers.mp4 (45.7 MB)
• 4. Measures of Centers.vtt (6.4 KB)
• 4.1 01_04_Lecture_Centers.pdf.pdf (313.0 KB)
• 5. Practice Exercise 1.html (0.3 KB)
• 5.1 Exercise-1.pdf.pdf (553.8 KB)
• 6. Measures of Dispersion.mp4 (28.4 MB)
• 6. Measures of Dispersion.vtt (4.7 KB)
• 6.1 01_05_Lecture_Dispersion.pdf.pdf (210.6 KB)
• 7. Practice Exercise 2.html (0.3 KB)
• 7.1 Exercise-2.pdf.pdf (469.9 KB)

• 1. Installing Python and Anaconda.mp4 (18.6 MB)
• 1. Installing Python and Anaconda.vtt (2.2 KB)
• 2. Opening Jupyter Notebook.mp4 (73.1 MB)
• 2. Opening Jupyter Notebook.vtt (8.0 KB)
• 3. Introduction to Jupyter.mp4 (51.3 MB)
• 3. Introduction to Jupyter.vtt (10.7 KB)
• 4. Arithmetic operators in Python Python Basics.mp4 (16.0 MB)
• 4. Arithmetic operators in Python Python Basics.vtt (3.5 KB)
• 5. Strings in Python Python Basics.mp4 (80.6 MB)
• 5. Strings in Python Python Basics.vtt (14.3 KB)
• 6. Lists, Tuples and Directories Python Basics.mp4 (73.7 MB)
• 6. Lists, Tuples and Directories Python Basics.vtt (14.6 KB)
• 7. Working with Numpy Library of Python.mp4 (54.1 MB)
• 7. Working with Numpy Library of Python.vtt (9.1 KB)
• 8. Working with Pandas Library of Python.mp4 (56.4 MB)
• 8. Working with Pandas Library of Python.vtt (7.2 KB)
• 9. Working with Seaborn Library of Python.mp4 (48.9 MB)
• 9. Working with Seaborn Library of Python.vtt (6.5 KB)

• 1. Gathering Business Knowledge.mp4 (25.1 MB)
• 1. Gathering Business Knowledge.vtt (3.4 KB)
• 1.1 03_01_PDE_Business_knowledge.pdf.pdf (153.9 KB)
• 10. Outlier treatment in Python.mp4 (58.4 MB)
• 10. Outlier treatment in Python.vtt (7.0 KB)
• 11. Project Exercise 3.html (0.2 KB)
• 12. Missing Value Imputation.mp4 (27.6 MB)
• 12. Missing Value Imputation.vtt (3.6 KB)
• 12.1 04_05_PDE_Missing_value.pdf.pdf (315.7 KB)
• 13. Missing Value Imputation in Python.mp4 (27.6 MB)
• 13. Missing Value Imputation in Python.vtt (3.6 KB)
• 14. Project Exercise 4.html (0.2 KB)
• 15. Seasonality in Data.mp4 (20.9 MB)
• 15. Seasonality in Data.vtt (3.3 KB)
• 15.1 04_07_PDE_Seasonality.pdf.pdf (364.1 KB)
• 16. Variable Transformation.mp4 (15.3 MB)
• 16. Variable Transformation.vtt (1.2 KB)
• 16.1 04_07_Variable_Transformation.pdf.pdf (456.1 KB)
• 17. Variable transformation and Deletion in Python.mp4 (35.6 MB)
• 17. Variable transformation and Deletion in Python.vtt (3.4 KB)
• 18. Project Exercise 5.html (0.2 KB)
• 19. Dummy variable creation Handling qualitative data.mp4 (40.6 MB)
• 19. Dummy variable creation Handling qualitative data.vtt (4.3 KB)
• 19.1 04_11_Dummy_Var.pdf.pdf (163.0 KB)
• 2. Data Exploration.mp4 (23.4 MB)
• 2. Data Exploration.vtt (3.2 KB)
• 2.1 03_02_PDE_Data_exploration.pdf.pdf (322.9 KB)
• 20. Dummy variable creation in Python.mp4 (33.9 MB)
• 20. Dummy variable creation in Python.vtt (4.7 KB)
• 21. Project Exercise 6.html (0.2 KB)
• 3. The Dataset and the Data Dictionary.mp4 (87.6 MB)
• 3. The Dataset and the Data Dictionary.vtt (7.5 KB)
• 4. Data Import in Python.mp4 (25.5 MB)
• 4. Data Import in Python.vtt (3.9 KB)
• 5. Project Exercise 1.html (0.5 KB)
• 5.1 Movie_collection.csv.csv (55.8 KB)
• 6. Univariate analysis and EDD.mp4 (27.3 MB)
• 6. Univariate analysis and EDD.vtt (3.1 KB)
• 6.1 03_04_PDE_Univariate_Analysis_Uni.pdf.pdf (333.4 KB)
• 7. EDD in Python.mp4 (97.1 MB)
• 7. EDD in Python.vtt (14.4 KB)
• 8. Project Exercise 2.html (0.2 KB)
• 9. Outlier Treatment.mp4 (27.8 MB)
• 9. Outlier Treatment.vtt (4.0 KB)
• 9.1 04_06_PDE_Outlier_Treatment.pdf.pdf (355.1 KB)

• 1. Three Classifiers and the problem statement.mp4 (22.9 MB)
• 1. Three Classifiers and the problem statement.vtt (3.3 KB)
• 1.1 01_INtro.pdf.pdf (190.4 KB)
• 10. Confusion Matrix.mp4 (26.7 MB)
• 10. Confusion Matrix.vtt (3.7 KB)
• 10.1 06_Confusion matrix.pdf.pdf (222.3 KB)
• 11. Making Confusion Matrix in Python.mp4 (64.7 MB)
• 11. Making Confusion Matrix in Python.vtt (8.8 KB)
• 12. Evaluating performance of model.mp4 (42.8 MB)
• 12. Evaluating performance of model.vtt (7.5 KB)
• 12.1 08_ROC.pdf.pdf (183.1 KB)
• 13. Evaluating model performance in Python.mp4 (11.8 MB)
• 13. Evaluating model performance in Python.vtt (2.1 KB)
• 14. Project Exercise 9.html (0.2 KB)
• 15. Linear Discriminant Analysis.mp4 (48.7 MB)
• 15. Linear Discriminant Analysis.vtt (9.7 KB)
• 15.1 07_LDA.pdf.pdf (183.1 KB)
• 16. LDA in Python.mp4 (14.4 MB)
• 16. LDA in Python.vtt (2.1 KB)
• 17. Project Exercise 10.html (0.2 KB)
• 18. Test-Train Split.mp4 (45.7 MB)
• 18. Test-Train Split.vtt (8.9 KB)
• 18.1 10_Test_Train.pdf.pdf (238.7 KB)
• 19. Test-Train Split in Python.mp4 (43.1 MB)
• 19. Test-Train Split in Python.vtt (6.1 KB)
• 2. Why can't we use Linear Regression.mp4 (20.4 MB)
• 2. Why can't we use Linear Regression.vtt (4.5 KB)
• 2.1 02_whynot_linear.pdf.pdf (155.3 KB)
• 20. Project Exercise 11.html (0.2 KB)
• 21. K-Nearest Neighbors classifier.mp4 (83.6 MB)
• 21. K-Nearest Neighbors classifier.vtt (8.3 KB)
• 21.1 09_KNN.pdf.pdf (236.7 KB)
• 22. K-Nearest Neighbors in Python Part 1.mp4 (45.9 MB)
• 22. K-Nearest Neighbors in Python Part 1.vtt (4.9 KB)
• 23. K-Nearest Neighbors in Python Part 2.mp4 (52.0 MB)
• 23. K-Nearest Neighbors in Python Part 2.vtt (5.8 KB)
• 24. Project Exercise 12.html (0.2 KB)
• 25. Understanding the results of classification models.mp4 (46.0 MB)
• 25. Understanding the results of classification models.vtt (6.3 KB)
• 25.1 11_results.pdf.pdf (170.9 KB)
• 26. Summary of the three models.mp4 (25.3 MB)
• 26. Summary of the three models.vtt (4.8 KB)
• 26.1 12_steps.pdf.pdf (148.1 KB)
• 27. The Final Exercise!.html (1.8 KB)
• 28. Course Conclusion.html (1.0 KB)
• 3. Logistic Regression.mp4 (39.1 MB)
• 3. Logistic Regression.vtt (7.2 KB)
• 3.1 03_logistic.pdf.pdf (352.7 KB)
• 4. Training a Simple Logistic Model in Python.mp4 (61.2 MB)
• 4. Training a Simple Logistic Model in Python.vtt (8.6 KB)
• 5. Project Exercise 7.html (0.3 KB)
• 6. Result of Simple Logistic Regression.mp4 (31.1 MB)
• 6. Result of Simple Logistic Regression.vtt (4.8 KB)
• 6.1 04_P_value.pdf.pdf (228.0 KB)
• 7. Logistic with multiple predictors.mp4 (10.0 MB)
• 7. Logistic with multiple predictors.vtt (2.5 KB)
• 7.1 05_Multiple_predictors.pdf.pdf (151.3 KB)
• 8. Training multiple predictor Logistic model in Python.mp4 (34.0 MB)
• 8. Training multiple predictor Logistic model in Python.vtt (4.9 KB)
• 9. Project Exercise 8.html (0.3 KB)

• [CourseClub.Me].url (0.0 KB)
• [DesireCourse.Net].url (0.0 KB)