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

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

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文件大小： 2.15G
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收录时间：2021-03-14
最近下载：2025-07-30

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文件列表

  • 2. Introduction to Machine Learning/1. Introduction to Machine Learning.mp4 129.8 MB
  • 5. Data Preprocessing/7. EDD in Python.mp4 101.8 MB
  • 5. Data Preprocessing/3. The Dataset and the Data Dictionary.mp4 91.9 MB
  • 6. Classification Models/21. K-Nearest Neighbors classifier.mp4 87.6 MB
  • 3. Basics of Statistics/3. Describing data Graphically.mp4 86.2 MB
  • 4. Setting up Python and Jupyter Notebook/5. Strings in Python Python Basics.mp4 84.5 MB
  • 4. Setting up Python and Jupyter Notebook/6. Lists, Tuples and Directories Python Basics.mp4 77.2 MB
  • 4. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.mp4 76.6 MB
  • 6. Classification Models/11. Making Confusion Matrix in Python.mp4 67.9 MB
  • 6. Classification Models/4. Training a Simple Logistic Model in Python.mp4 64.2 MB
  • 5. Data Preprocessing/10. Outlier treatment in Python.mp4 61.3 MB
  • 4. Setting up Python and Jupyter Notebook/8. Working with Pandas Library of Python.mp4 59.1 MB
  • 4. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.mp4 56.7 MB
  • 6. Classification Models/23. K-Nearest Neighbors in Python Part 2.mp4 54.5 MB
  • 4. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.mp4 53.8 MB
  • 4. Setting up Python and Jupyter Notebook/9. Working with Seaborn Library of Python.mp4 51.2 MB
  • 6. Classification Models/15. Linear Discriminant Analysis.mp4 51.0 MB
  • 6. Classification Models/25. Understanding the results of classification models.mp4 48.2 MB
  • 6. Classification Models/22. K-Nearest Neighbors in Python Part 1.mp4 48.1 MB
  • 6. Classification Models/18. Test-Train Split.mp4 47.9 MB
  • 3. Basics of Statistics/4. Measures of Centers.mp4 47.9 MB
  • 2. Introduction to Machine Learning/2. Building a Machine Learning model.mp4 47.5 MB
  • 6. Classification Models/19. Test-Train Split in Python.mp4 45.2 MB
  • 6. Classification Models/12. Evaluating performance of model.mp4 44.9 MB
  • 5. Data Preprocessing/19. Dummy variable creation Handling qualitative data.mp4 42.6 MB
  • 6. Classification Models/3. Logistic Regression.mp4 41.0 MB
  • 5. Data Preprocessing/17. Variable transformation and Deletion in Python.mp4 37.3 MB
  • 6. Classification Models/8. Training multiple predictor Logistic model in Python.mp4 35.7 MB
  • 5. Data Preprocessing/20. Dummy variable creation in Python.mp4 35.5 MB
  • 6. Classification Models/6. Result of Simple Logistic Regression.mp4 32.7 MB
  • 3. Basics of Statistics/6. Measures of Dispersion.mp4 29.8 MB
  • 5. Data Preprocessing/9. Outlier Treatment.mp4 29.1 MB
  • 5. Data Preprocessing/13. Missing Value Imputation in Python.mp4 29.0 MB
  • 5. Data Preprocessing/12. Missing Value Imputation.mp4 28.9 MB
  • 5. Data Preprocessing/6. Univariate analysis and EDD.mp4 28.6 MB
  • 6. Classification Models/10. Confusion Matrix.mp4 27.9 MB
  • 3. Basics of Statistics/1. Types of Data.mp4 27.1 MB
  • 5. Data Preprocessing/4. Data Import in Python.mp4 26.7 MB
  • 6. Classification Models/26. Summary of the three models.mp4 26.5 MB
  • 5. Data Preprocessing/1. Gathering Business Knowledge.mp4 26.3 MB
  • 5. Data Preprocessing/2. Data Exploration.mp4 24.5 MB
  • 6. Classification Models/1. Three Classifiers and the problem statement.mp4 24.0 MB
  • 5. Data Preprocessing/15. Seasonality in Data.mp4 21.9 MB
  • 6. Classification Models/2. Why can't we use Linear Regression.mp4 21.4 MB
  • 4. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.mp4 19.5 MB
  • 1. Introduction/1. Welcome to the course!.mp4 18.5 MB
  • 4. Setting up Python and Jupyter Notebook/4. Arithmetic operators in Python Python Basics.mp4 16.7 MB
  • 5. Data Preprocessing/16. Variable Transformation.mp4 16.0 MB
  • 6. Classification Models/16. LDA in Python.mp4 15.1 MB
  • 3. Basics of Statistics/2. Types of Statistics.mp4 13.9 MB
  • 6. Classification Models/13. Evaluating model performance in Python.mp4 12.3 MB
  • 6. Classification Models/7. Logistic with multiple predictors.mp4 10.5 MB
  • 2. Introduction to Machine Learning/1.1 Lecture_machineLearning.pdf.pdf 1.0 MB
  • 3. Basics of Statistics/5.1 Exercise-1.pdf.pdf 567.1 kB
  • 1. Introduction/1.1 00_Introduction_01_py.pdf.pdf 483.5 kB
  • 3. Basics of Statistics/7.1 Exercise-2.pdf.pdf 481.2 kB
  • 5. Data Preprocessing/16.1 04_07_Variable_Transformation.pdf.pdf 467.1 kB
  • 5. Data Preprocessing/15.1 04_07_PDE_Seasonality.pdf.pdf 372.8 kB
  • 5. Data Preprocessing/9.1 04_06_PDE_Outlier_Treatment.pdf.pdf 363.7 kB
  • 6. Classification Models/3.1 03_logistic.pdf.pdf 361.2 kB
  • 5. Data Preprocessing/6.1 03_04_PDE_Univariate_Analysis_Uni.pdf.pdf 341.4 kB
  • 5. Data Preprocessing/2.1 03_02_PDE_Data_exploration.pdf.pdf 330.7 kB
  • 3. Basics of Statistics/3.1 01_03_Lecture_DataSummaryandGraph.pdf.pdf 325.5 kB
  • 5. Data Preprocessing/12.1 04_05_PDE_Missing_value.pdf.pdf 323.3 kB
  • 3. Basics of Statistics/4.1 01_04_Lecture_Centers.pdf.pdf 320.5 kB
  • 6. Classification Models/18.1 10_Test_Train.pdf.pdf 244.5 kB
  • 6. Classification Models/21.1 09_KNN.pdf.pdf 242.4 kB
  • 6. Classification Models/6.1 04_P_value.pdf.pdf 233.5 kB
  • 6. Classification Models/10.1 06_Confusion matrix.pdf.pdf 227.7 kB
  • 3. Basics of Statistics/6.1 01_05_Lecture_Dispersion.pdf.pdf 215.6 kB
  • 6. Classification Models/1.1 01_INtro.pdf.pdf 194.9 kB
  • 6. Classification Models/12.1 08_ROC.pdf.pdf 187.4 kB
  • 6. Classification Models/15.1 07_LDA.pdf.pdf 187.4 kB
  • 3. Basics of Statistics/1.1 01_01_Lecture_TypesOfData.pdf.pdf 182.0 kB
  • 3. Basics of Statistics/2.1 01_02_Lecture_TypesOfStatistics.pdf.pdf 175.9 kB
  • 6. Classification Models/25.1 11_results.pdf.pdf 175.0 kB
  • 5. Data Preprocessing/19.1 04_11_Dummy_Var.pdf.pdf 166.9 kB
  • 6. Classification Models/2.1 02_whynot_linear.pdf.pdf 159.1 kB
  • 5. Data Preprocessing/1.1 03_01_PDE_Business_knowledge.pdf.pdf 157.6 kB
  • 6. Classification Models/7.1 05_Multiple_predictors.pdf.pdf 154.9 kB
  • 6. Classification Models/26.1 12_steps.pdf.pdf 151.7 kB
  • 5. Data Preprocessing/5.1 Movie_collection.csv.csv 57.1 kB
  • 2. Introduction to Machine Learning/1. Introduction to Machine Learning.vtt 16.7 kB
  • 4. Setting up Python and Jupyter Notebook/6. Lists, Tuples and Directories Python Basics.vtt 15.0 kB
  • 5. Data Preprocessing/7. EDD in Python.vtt 14.7 kB
  • 4. Setting up Python and Jupyter Notebook/5. Strings in Python Python Basics.vtt 14.6 kB
  • 3. Basics of Statistics/3. Describing data Graphically.vtt 11.6 kB
  • 4. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.vtt 11.0 kB
  • 6. Classification Models/15. Linear Discriminant Analysis.vtt 9.9 kB
  • 4. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.vtt 9.3 kB
  • 6. Classification Models/18. Test-Train Split.vtt 9.1 kB
  • 6. Classification Models/11. Making Confusion Matrix in Python.vtt 9.0 kB
  • 6. Classification Models/4. Training a Simple Logistic Model in Python.vtt 8.8 kB
  • 2. Introduction to Machine Learning/2. Building a Machine Learning model.vtt 8.8 kB
  • 6. Classification Models/21. K-Nearest Neighbors classifier.vtt 8.5 kB
  • 4. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.vtt 8.2 kB
  • 6. Classification Models/12. Evaluating performance of model.vtt 7.7 kB
  • 5. Data Preprocessing/3. The Dataset and the Data Dictionary.vtt 7.6 kB
  • 4. Setting up Python and Jupyter Notebook/8. Working with Pandas Library of Python.vtt 7.4 kB
  • 6. Classification Models/3. Logistic Regression.vtt 7.4 kB
  • 5. Data Preprocessing/10. Outlier treatment in Python.vtt 7.1 kB
  • 4. Setting up Python and Jupyter Notebook/9. Working with Seaborn Library of Python.vtt 6.7 kB
  • 3. Basics of Statistics/4. Measures of Centers.vtt 6.6 kB
  • 6. Classification Models/25. Understanding the results of classification models.vtt 6.4 kB
  • 6. Classification Models/19. Test-Train Split in Python.vtt 6.3 kB
  • 6. Classification Models/23. K-Nearest Neighbors in Python Part 2.vtt 6.0 kB
  • 6. Classification Models/8. Training multiple predictor Logistic model in Python.vtt 5.0 kB
  • 6. Classification Models/22. K-Nearest Neighbors in Python Part 1.vtt 5.0 kB
  • 6. Classification Models/6. Result of Simple Logistic Regression.vtt 4.9 kB
  • 6. Classification Models/26. Summary of the three models.vtt 4.9 kB
  • 3. Basics of Statistics/6. Measures of Dispersion.vtt 4.8 kB
  • 5. Data Preprocessing/20. Dummy variable creation in Python.vtt 4.8 kB
  • 6. Classification Models/2. Why can't we use Linear Regression.vtt 4.6 kB
  • 3. Basics of Statistics/1. Types of Data.vtt 4.4 kB
  • 5. Data Preprocessing/19. Dummy variable creation Handling qualitative data.vtt 4.4 kB
  • 5. Data Preprocessing/9. Outlier Treatment.vtt 4.1 kB
  • 5. Data Preprocessing/4. Data Import in Python.vtt 4.0 kB
  • 6. Classification Models/10. Confusion Matrix.vtt 3.8 kB
  • 5. Data Preprocessing/12. Missing Value Imputation.vtt 3.7 kB
  • 5. Data Preprocessing/13. Missing Value Imputation in Python.vtt 3.7 kB
  • 4. Setting up Python and Jupyter Notebook/4. Arithmetic operators in Python Python Basics.vtt 3.6 kB
  • 5. Data Preprocessing/1. Gathering Business Knowledge.vtt 3.5 kB
  • 5. Data Preprocessing/17. Variable transformation and Deletion in Python.vtt 3.5 kB
  • 6. Classification Models/1. Three Classifiers and the problem statement.vtt 3.4 kB
  • 1. Introduction/1. Welcome to the course!.vtt 3.4 kB
  • 5. Data Preprocessing/15. Seasonality in Data.vtt 3.4 kB
  • 5. Data Preprocessing/2. Data Exploration.vtt 3.3 kB
  • 5. Data Preprocessing/6. Univariate analysis and EDD.vtt 3.2 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 2.3 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

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