DesireCourse Net Udemy Machine Learning Basics Classification models in Python

/1. Introduction/

1. Welcome to the course!.mp4

1. Welcome to the course!.vtt

1.1 00_Introduction_01_py.pdf.pdf

/2. Introduction to Machine Learning/

1. Introduction to Machine Learning.mp4

1. Introduction to Machine Learning.vtt

1.1 Lecture_machineLearning.pdf.pdf

2. Building a Machine Learning model.mp4

2. Building a Machine Learning model.vtt

/3. Basics of Statistics/

1. Types of Data.mp4

1. Types of Data.vtt

1.1 01_01_Lecture_TypesOfData.pdf.pdf

2. Types of Statistics.mp4

2. Types of Statistics.vtt

2.1 01_02_Lecture_TypesOfStatistics.pdf.pdf

3. Describing data Graphically.mp4

3. Describing data Graphically.vtt

3.1 01_03_Lecture_DataSummaryandGraph.pdf.pdf

4. Measures of Centers.mp4

4. Measures of Centers.vtt

4.1 01_04_Lecture_Centers.pdf.pdf

5. Practice Exercise 1.html

5.1 Exercise-1.pdf.pdf

6. Measures of Dispersion.mp4

6. Measures of Dispersion.vtt

6.1 01_05_Lecture_Dispersion.pdf.pdf

7. Practice Exercise 2.html

7.1 Exercise-2.pdf.pdf

/4. Setting up Python and Jupyter Notebook/

1. Installing Python and Anaconda.mp4

1. Installing Python and Anaconda.vtt

2. Opening Jupyter Notebook.mp4

2. Opening Jupyter Notebook.vtt

3. Introduction to Jupyter.mp4

3. Introduction to Jupyter.vtt

4. Arithmetic operators in Python Python Basics.mp4

4. Arithmetic operators in Python Python Basics.vtt

5. Strings in Python Python Basics.mp4

5. Strings in Python Python Basics.vtt

6. Lists, Tuples and Directories Python Basics.mp4

6. Lists, Tuples and Directories Python Basics.vtt

7. Working with Numpy Library of Python.mp4

7. Working with Numpy Library of Python.vtt

8. Working with Pandas Library of Python.mp4

8. Working with Pandas Library of Python.vtt

9. Working with Seaborn Library of Python.mp4

9. Working with Seaborn Library of Python.vtt

/5. Data Preprocessing/

1. Gathering Business Knowledge.mp4

1. Gathering Business Knowledge.vtt

1.1 03_01_PDE_Business_knowledge.pdf.pdf

10. Outlier treatment in Python.mp4

10. Outlier treatment in Python.vtt

11. Project Exercise 3.html

12. Missing Value Imputation.mp4

12. Missing Value Imputation.vtt

12.1 04_05_PDE_Missing_value.pdf.pdf

13. Missing Value Imputation in Python.mp4

13. Missing Value Imputation in Python.vtt

14. Project Exercise 4.html

15. Seasonality in Data.mp4

15. Seasonality in Data.vtt

15.1 04_07_PDE_Seasonality.pdf.pdf

16. Variable Transformation.mp4

16. Variable Transformation.vtt

16.1 04_07_Variable_Transformation.pdf.pdf

17. Variable transformation and Deletion in Python.mp4

17. Variable transformation and Deletion in Python.vtt

18. Project Exercise 5.html

19. Dummy variable creation Handling qualitative data.mp4

19. Dummy variable creation Handling qualitative data.vtt

19.1 04_11_Dummy_Var.pdf.pdf

2. Data Exploration.mp4

2. Data Exploration.vtt

2.1 03_02_PDE_Data_exploration.pdf.pdf

20. Dummy variable creation in Python.mp4

20. Dummy variable creation in Python.vtt

21. Project Exercise 6.html

3. The Dataset and the Data Dictionary.mp4

3. The Dataset and the Data Dictionary.vtt

4. Data Import in Python.mp4

4. Data Import in Python.vtt

5. Project Exercise 1.html

5.1 Movie_collection.csv.csv

6. Univariate analysis and EDD.mp4

6. Univariate analysis and EDD.vtt

6.1 03_04_PDE_Univariate_Analysis_Uni.pdf.pdf

7. EDD in Python.mp4

7. EDD in Python.vtt

8. Project Exercise 2.html

9. Outlier Treatment.mp4

9. Outlier Treatment.vtt

9.1 04_06_PDE_Outlier_Treatment.pdf.pdf

/6. Classification Models/

1. Three Classifiers and the problem statement.mp4

1. Three Classifiers and the problem statement.vtt

1.1 01_INtro.pdf.pdf

10. Confusion Matrix.mp4

10. Confusion Matrix.vtt

10.1 06_Confusion matrix.pdf.pdf

11. Making Confusion Matrix in Python.mp4

11. Making Confusion Matrix in Python.vtt

12. Evaluating performance of model.mp4

12. Evaluating performance of model.vtt

12.1 08_ROC.pdf.pdf

13. Evaluating model performance in Python.mp4

13. Evaluating model performance in Python.vtt

14. Project Exercise 9.html

15. Linear Discriminant Analysis.mp4

15. Linear Discriminant Analysis.vtt

15.1 07_LDA.pdf.pdf

16. LDA in Python.mp4

16. LDA in Python.vtt

17. Project Exercise 10.html

18. Test-Train Split.mp4

18. Test-Train Split.vtt

18.1 10_Test_Train.pdf.pdf

19. Test-Train Split in Python.mp4

19. Test-Train Split in Python.vtt

2. Why can't we use Linear Regression.mp4

2. Why can't we use Linear Regression.vtt

2.1 02_whynot_linear.pdf.pdf

20. Project Exercise 11.html

21. K-Nearest Neighbors classifier.mp4

21. K-Nearest Neighbors classifier.vtt

21.1 09_KNN.pdf.pdf

22. K-Nearest Neighbors in Python Part 1.mp4

22. K-Nearest Neighbors in Python Part 1.vtt

23. K-Nearest Neighbors in Python Part 2.mp4

23. K-Nearest Neighbors in Python Part 2.vtt

24. Project Exercise 12.html

25. Understanding the results of classification models.mp4

25. Understanding the results of classification models.vtt

25.1 11_results.pdf.pdf

26. Summary of the three models.mp4

26. Summary of the three models.vtt

26.1 12_steps.pdf.pdf

27. The Final Exercise!.html

28. Course Conclusion.html

3. Logistic Regression.mp4

3. Logistic Regression.vtt

3.1 03_logistic.pdf.pdf

4. Training a Simple Logistic Model in Python.mp4

4. Training a Simple Logistic Model in Python.vtt

5. Project Exercise 7.html

6. Result of Simple Logistic Regression.mp4

6. Result of Simple Logistic Regression.vtt

6.1 04_P_value.pdf.pdf

7. Logistic with multiple predictors.mp4

7. Logistic with multiple predictors.vtt

7.1 05_Multiple_predictors.pdf.pdf

8. Training multiple predictor Logistic model in Python.mp4

8. Training multiple predictor Logistic model in Python.vtt

9. Project Exercise 8.html

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