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[DesireCourse Net] Udemy - Machine Learning Basics Classification models in Python
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1. Introduction (Size: 2.15 GB) (Files: 152)
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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