[DesireCourse.Net] Udemy - Machine Learning Basics Classification models in Python

Creation Time	Oct. 31, 2024, 5:19 p.m.
Last Access Time	April 16, 2026, 11:19 a.m.
File Size	2.2 GB
Keywords	Basics Classification models DesireCourse - Udemy Machine Python Learning in Net
Total Requests	411
Total Files	152
Magnet Link	Please wait ...
	Download (Magnet Link) Play (Watch Online)

2. Introduction to Machine Learning/1. Introduction to Machine Learning.mp4 123.8 MB
5. Data Preprocessing/7. EDD in Python.mp4 97.1 MB
5. Data Preprocessing/3. The Dataset and the Data Dictionary.mp4 87.6 MB
6. Classification Models/21. K-Nearest Neighbors classifier.mp4 83.6 MB
3. Basics of Statistics/3. Describing data Graphically.mp4 82.2 MB
4. Setting up Python and Jupyter Notebook/5. Strings in Python Python Basics.mp4 80.6 MB
4. Setting up Python and Jupyter Notebook/6. Lists, Tuples and Directories Python Basics.mp4 73.7 MB
4. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.mp4 73.1 MB
6. Classification Models/11. Making Confusion Matrix in Python.mp4 64.7 MB
6. Classification Models/4. Training a Simple Logistic Model in Python.mp4 61.2 MB
5. Data Preprocessing/10. Outlier treatment in Python.mp4 58.4 MB
4. Setting up Python and Jupyter Notebook/8. Working with Pandas Library of Python.mp4 56.4 MB
4. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.mp4 54.1 MB
6. Classification Models/23. K-Nearest Neighbors in Python Part 2.mp4 52.0 MB
4. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.mp4 51.3 MB
4. Setting up Python and Jupyter Notebook/9. Working with Seaborn Library of Python.mp4 48.9 MB
6. Classification Models/15. Linear Discriminant Analysis.mp4 48.7 MB
6. Classification Models/25. Understanding the results of classification models.mp4 46.0 MB
6. Classification Models/22. K-Nearest Neighbors in Python Part 1.mp4 45.9 MB
6. Classification Models/18. Test-Train Split.mp4 45.7 MB
3. Basics of Statistics/4. Measures of Centers.mp4 45.7 MB
2. Introduction to Machine Learning/2. Building a Machine Learning model.mp4 45.3 MB
6. Classification Models/19. Test-Train Split in Python.mp4 43.1 MB
6. Classification Models/12. Evaluating performance of model.mp4 42.8 MB
5. Data Preprocessing/19. Dummy variable creation Handling qualitative data.mp4 40.6 MB
6. Classification Models/3. Logistic Regression.mp4 39.1 MB
5. Data Preprocessing/17. Variable transformation and Deletion in Python.mp4 35.6 MB
6. Classification Models/8. Training multiple predictor Logistic model in Python.mp4 34.0 MB
5. Data Preprocessing/20. Dummy variable creation in Python.mp4 33.9 MB
6. Classification Models/6. Result of Simple Logistic Regression.mp4 31.1 MB
3. Basics of Statistics/6. Measures of Dispersion.mp4 28.4 MB
5. Data Preprocessing/9. Outlier Treatment.mp4 27.8 MB
5. Data Preprocessing/13. Missing Value Imputation in Python.mp4 27.6 MB
5. Data Preprocessing/12. Missing Value Imputation.mp4 27.6 MB
5. Data Preprocessing/6. Univariate analysis and EDD.mp4 27.3 MB
6. Classification Models/10. Confusion Matrix.mp4 26.7 MB
3. Basics of Statistics/1. Types of Data.mp4 25.9 MB
5. Data Preprocessing/4. Data Import in Python.mp4 25.5 MB
6. Classification Models/26. Summary of the three models.mp4 25.3 MB
5. Data Preprocessing/1. Gathering Business Knowledge.mp4 25.1 MB
5. Data Preprocessing/2. Data Exploration.mp4 23.4 MB
6. Classification Models/1. Three Classifiers and the problem statement.mp4 22.9 MB
5. Data Preprocessing/15. Seasonality in Data.mp4 20.9 MB
6. Classification Models/2. Why can't we use Linear Regression.mp4 20.4 MB
4. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.mp4 18.6 MB
1. Introduction/1. Welcome to the course!.mp4 17.6 MB
4. Setting up Python and Jupyter Notebook/4. Arithmetic operators in Python Python Basics.mp4 16.0 MB
5. Data Preprocessing/16. Variable Transformation.mp4 15.3 MB
6. Classification Models/16. LDA in Python.mp4 14.4 MB
3. Basics of Statistics/2. Types of Statistics.mp4 13.2 MB
6. Classification Models/13. Evaluating model performance in Python.mp4 11.8 MB
6. Classification Models/7. Logistic with multiple predictors.mp4 10.0 MB
2. Introduction to Machine Learning/1.1 Lecture_machineLearning.pdf.pdf 991.6 KB
3. Basics of Statistics/5.1 Exercise-1.pdf.pdf 553.8 KB
1. Introduction/1.1 00_Introduction_01_py.pdf.pdf 472.2 KB
3. Basics of Statistics/7.1 Exercise-2.pdf.pdf 469.9 KB
5. Data Preprocessing/16.1 04_07_Variable_Transformation.pdf.pdf 456.1 KB
5. Data Preprocessing/15.1 04_07_PDE_Seasonality.pdf.pdf 364.1 KB
5. Data Preprocessing/9.1 04_06_PDE_Outlier_Treatment.pdf.pdf 355.1 KB
6. Classification Models/3.1 03_logistic.pdf.pdf 352.7 KB
5. Data Preprocessing/6.1 03_04_PDE_Univariate_Analysis_Uni.pdf.pdf 333.4 KB
5. Data Preprocessing/2.1 03_02_PDE_Data_exploration.pdf.pdf 322.9 KB
3. Basics of Statistics/3.1 01_03_Lecture_DataSummaryandGraph.pdf.pdf 317.9 KB
5. Data Preprocessing/12.1 04_05_PDE_Missing_value.pdf.pdf 315.7 KB
3. Basics of Statistics/4.1 01_04_Lecture_Centers.pdf.pdf 313.0 KB
6. Classification Models/18.1 10_Test_Train.pdf.pdf 238.7 KB
6. Classification Models/21.1 09_KNN.pdf.pdf 236.7 KB
6. Classification Models/6.1 04_P_value.pdf.pdf 228.0 KB
6. Classification Models/10.1 06_Confusion matrix.pdf.pdf 222.3 KB
3. Basics of Statistics/6.1 01_05_Lecture_Dispersion.pdf.pdf 210.6 KB
6. Classification Models/1.1 01_INtro.pdf.pdf 190.4 KB
6. Classification Models/12.1 08_ROC.pdf.pdf 183.1 KB
6. Classification Models/15.1 07_LDA.pdf.pdf 183.1 KB
3. Basics of Statistics/1.1 01_01_Lecture_TypesOfData.pdf.pdf 177.7 KB
3. Basics of Statistics/2.1 01_02_Lecture_TypesOfStatistics.pdf.pdf 171.7 KB
6. Classification Models/25.1 11_results.pdf.pdf 170.9 KB
5. Data Preprocessing/19.1 04_11_Dummy_Var.pdf.pdf 163.0 KB
6. Classification Models/2.1 02_whynot_linear.pdf.pdf 155.3 KB
5. Data Preprocessing/1.1 03_01_PDE_Business_knowledge.pdf.pdf 153.9 KB
6. Classification Models/7.1 05_Multiple_predictors.pdf.pdf 151.3 KB
6. Classification Models/26.1 12_steps.pdf.pdf 148.1 KB
5. Data Preprocessing/5.1 Movie_collection.csv.csv 55.8 KB
2. Introduction to Machine Learning/1. Introduction to Machine Learning.vtt 16.3 KB
4. Setting up Python and Jupyter Notebook/6. Lists, Tuples and Directories Python Basics.vtt 14.6 KB
5. Data Preprocessing/7. EDD in Python.vtt 14.4 KB
4. Setting up Python and Jupyter Notebook/5. Strings in Python Python Basics.vtt 14.3 KB
3. Basics of Statistics/3. Describing data Graphically.vtt 11.3 KB
4. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.vtt 10.7 KB
6. Classification Models/15. Linear Discriminant Analysis.vtt 9.7 KB
4. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.vtt 9.1 KB
6. Classification Models/18. Test-Train Split.vtt 8.9 KB
6. Classification Models/11. Making Confusion Matrix in Python.vtt 8.8 KB
6. Classification Models/4. Training a Simple Logistic Model in Python.vtt 8.6 KB
2. Introduction to Machine Learning/2. Building a Machine Learning model.vtt 8.6 KB
6. Classification Models/21. K-Nearest Neighbors classifier.vtt 8.3 KB
4. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.vtt 8.0 KB
6. Classification Models/12. Evaluating performance of model.vtt 7.5 KB
5. Data Preprocessing/3. The Dataset and the Data Dictionary.vtt 7.5 KB
4. Setting up Python and Jupyter Notebook/8. Working with Pandas Library of Python.vtt 7.2 KB
6. Classification Models/3. Logistic Regression.vtt 7.2 KB
5. Data Preprocessing/10. Outlier treatment in Python.vtt 7.0 KB
4. Setting up Python and Jupyter Notebook/9. Working with Seaborn Library of Python.vtt 6.5 KB
3. Basics of Statistics/4. Measures of Centers.vtt 6.4 KB
6. Classification Models/25. Understanding the results of classification models.vtt 6.3 KB
6. Classification Models/19. Test-Train Split in Python.vtt 6.1 KB
6. Classification Models/23. K-Nearest Neighbors in Python Part 2.vtt 5.8 KB
6. Classification Models/8. Training multiple predictor Logistic model in Python.vtt 4.9 KB
6. Classification Models/22. K-Nearest Neighbors in Python Part 1.vtt 4.9 KB
6. Classification Models/6. Result of Simple Logistic Regression.vtt 4.8 KB
6. Classification Models/26. Summary of the three models.vtt 4.8 KB
3. Basics of Statistics/6. Measures of Dispersion.vtt 4.7 KB
5. Data Preprocessing/20. Dummy variable creation in Python.vtt 4.7 KB
6. Classification Models/2. Why can't we use Linear Regression.vtt 4.5 KB
3. Basics of Statistics/1. Types of Data.vtt 4.3 KB
5. Data Preprocessing/19. Dummy variable creation Handling qualitative data.vtt 4.3 KB
5. Data Preprocessing/9. Outlier Treatment.vtt 4.0 KB
5. Data Preprocessing/4. Data Import in Python.vtt 3.9 KB
6. Classification Models/10. Confusion Matrix.vtt 3.7 KB
5. Data Preprocessing/12. Missing Value Imputation.vtt 3.6 KB
5. Data Preprocessing/13. Missing Value Imputation in Python.vtt 3.6 KB
4. Setting up Python and Jupyter Notebook/4. Arithmetic operators in Python Python Basics.vtt 3.5 KB
5. Data Preprocessing/1. Gathering Business Knowledge.vtt 3.4 KB
5. Data Preprocessing/17. Variable transformation and Deletion in Python.vtt 3.4 KB
6. Classification Models/1. Three Classifiers and the problem statement.vtt 3.3 KB
1. Introduction/1. Welcome to the course!.vtt 3.3 KB
5. Data Preprocessing/15. Seasonality in Data.vtt 3.3 KB
5. Data Preprocessing/2. Data Exploration.vtt 3.2 KB
5. Data Preprocessing/6. Univariate analysis and EDD.vtt 3.1 KB
3. Basics of Statistics/2. Types of Statistics.vtt 2.7 KB
6. Classification Models/7. Logistic with multiple predictors.vtt 2.5 KB
4. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.vtt 2.2 KB
6. Classification Models/16. LDA in Python.vtt 2.1 KB
6. Classification Models/13. Evaluating model performance in Python.vtt 2.1 KB
6. Classification Models/27. The Final Exercise!.html 1.8 KB
5. Data Preprocessing/16. Variable Transformation.vtt 1.2 KB
6. Classification Models/28. Course Conclusion.html 1.0 KB
5. Data Preprocessing/5. Project Exercise 1.html 481 bytes
3. Basics of Statistics/5. Practice Exercise 1.html 354 bytes
6. Classification Models/5. Project Exercise 7.html 320 bytes
6. Classification Models/9. Project Exercise 8.html 306 bytes
3. Basics of Statistics/7. Practice Exercise 2.html 295 bytes
5. Data Preprocessing/14. Project Exercise 4.html 238 bytes
5. Data Preprocessing/11. Project Exercise 3.html 233 bytes
5. Data Preprocessing/18. Project Exercise 5.html 225 bytes
6. Classification Models/20. Project Exercise 11.html 207 bytes
5. Data Preprocessing/21. Project Exercise 6.html 202 bytes
5. Data Preprocessing/8. Project Exercise 2.html 177 bytes
6. Classification Models/14. Project Exercise 9.html 175 bytes
6. Classification Models/24. Project Exercise 12.html 174 bytes
6. Classification Models/17. Project Exercise 10.html 165 bytes
[DesireCourse.Net].url 51 bytes
[CourseClub.Me].url 48 bytes

[DesireCourse.Net] Udemy - Machine Learning Basics Classification models in Python

File List

Download Torrent

Related Resources

Copyright Infringement