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Udemy - Machine Learning Basics: Classification models in Python [DC]

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Udemy - Machine Learning Basics: Classification models in Python [DC] (Size: 2.2 GB)
  1. Gathering Business Knowledge.mp4 25.1 MB
  1. Gathering Business Knowledge.vtt 3.4 KB
  1. Installing Python and Anaconda.mp4 18.6 MB
  1. Installing Python and Anaconda.vtt 2.2 KB
  1. Introduction to Machine Learning.mp4 123.8 MB
  1. Introduction to Machine Learning.vtt 16.3 KB
  1. Three Classifiers and the problem statement.mp4 22.9 MB
  1. Three Classifiers and the problem statement.vtt 3.3 KB
  1. Types of Data.mp4 25.9 MB
  1. Types of Data.vtt 4.3 KB
  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
  1.1 01_01_Lecture_TypesOfData.pdf.pdf 177.7 KB
  1.1 01_INtro.pdf.pdf 190.4 KB
  1.1 03_01_PDE_Business_knowledge.pdf.pdf 153.9 KB
  1.1 Lecture_machineLearning.pdf.pdf 991.6 KB
  10. Confusion Matrix.mp4 26.7 MB
  10. Confusion Matrix.vtt 3.7 KB
  10. Outlier treatment in Python.mp4 58.4 MB
  10. Outlier treatment in Python.vtt 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
  11. Project Exercise 3.html 204.8 B
  12. Evaluating performance of model.mp4 42.8 MB
  12. Evaluating performance of model.vtt 7.5 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
  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
  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 204.8 B
  14. Project Exercise 9.html 204.8 B
  15. Linear Discriminant Analysis.mp4 48.7 MB
  15. Linear Discriminant Analysis.vtt 9.7 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
  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
  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. Project Exercise 10.html 204.8 B
  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 204.8 B
  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. Dummy variable creation Handling qualitative data.mp4 40.6 MB
  19. Dummy variable creation Handling qualitative data.vtt 4.3 KB
  19. Test-Train Split in Python.mp4 43.1 MB
  19. Test-Train Split in Python.vtt 6.1 KB
  19.1 04_11_Dummy_Var.pdf.pdf 163 KB
  2. Building a Machine Learning model.mp4 45.3 MB
  2. Building a Machine Learning model.vtt 8.6 KB
  2. Data Exploration.mp4 23.4 MB
  2. Data Exploration.vtt 3.2 KB
  2. Opening Jupyter Notebook.mp4 73.1 MB
  2. Opening Jupyter Notebook.vtt 8 KB
  2. Types of Statistics.mp4 13.2 MB
  2. Types of Statistics.vtt 2.7 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 01_02_Lecture_TypesOfStatistics.pdf.pdf 171.7 KB
  2.1 02_whynot_linear.pdf.pdf 155.3 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
  20. Project Exercise 11.html 204.8 B
  21. K-Nearest Neighbors classifier.mp4 83.6 MB
  21. K-Nearest Neighbors classifier.vtt 8.3 KB
  21. Project Exercise 6.html 204.8 B
  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 MB
  23. K-Nearest Neighbors in Python Part 2.vtt 5.8 KB
  24. Project Exercise 12.html 204.8 B
  25. Understanding the results of classification models.mp4 46 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 KB
  3. Describing data Graphically.mp4 82.2 MB
  3. Describing data Graphically.vtt 11.3 KB
  3. Introduction to Jupyter.mp4 51.3 MB
  3. Introduction to Jupyter.vtt 10.7 KB
  3. Logistic Regression.mp4 39.1 MB
  3. Logistic Regression.vtt 7.2 KB
  3. The Dataset and the Data Dictionary.mp4 87.6 MB
  3. The Dataset and the Data Dictionary.vtt 7.5 KB
  3.1 01_03_Lecture_DataSummaryandGraph.pdf.pdf 317.9 KB
  3.1 03_logistic.pdf.pdf 352.7 KB
  4. Arithmetic operators in Python Python Basics.mp4 16 MB
  4. Arithmetic operators in Python Python Basics.vtt 3.5 KB
  4. Data Import in Python.mp4 25.5 MB
  4. Data Import in Python.vtt 3.9 KB
  4. Measures of Centers.mp4 45.7 MB
  4. Measures of Centers.vtt 6.4 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
  4.1 01_04_Lecture_Centers.pdf.pdf 313 KB
  5. Practice Exercise 1.html 307.2 B
  5. Project Exercise 1.html 512 B
  5. Project Exercise 7.html 307.2 B
  5. Strings in Python Python Basics.mp4 80.6 MB
  5. Strings in Python Python Basics.vtt 14.3 KB
  5.1 Exercise-1.pdf.pdf 553.8 KB
  5.1 Movie_collection.csv.csv 55.8 KB
  6. Lists, Tuples and Directories Python Basics.mp4 73.7 MB
  6. Lists, Tuples and Directories Python Basics.vtt 14.6 KB
  6. Measures of Dispersion.mp4 28.4 MB
  6. Measures of Dispersion.vtt 4.7 KB
  6. Result of Simple Logistic Regression.mp4 31.1 MB
  6. Result of Simple Logistic Regression.vtt 4.8 KB
  6. Univariate analysis and EDD.mp4 27.3 MB
  6. Univariate analysis and EDD.vtt 3.1 KB
  6.1 01_05_Lecture_Dispersion.pdf.pdf 210.6 KB
  6.1 03_04_PDE_Univariate_Analysis_Uni.pdf.pdf 333.4 KB
  6.1 04_P_value.pdf.pdf 228 KB
  7. EDD in Python.mp4 97.1 MB
  7. EDD in Python.vtt 14.4 KB
  7. Logistic with multiple predictors.mp4 10 MB
  7. Logistic with multiple predictors.vtt 2.5 KB
  7. Practice Exercise 2.html 307.2 B
  7. Working with Numpy Library of Python.mp4 54.1 MB
  7. Working with Numpy Library of Python.vtt 9.1 KB
  7.1 05_Multiple_predictors.pdf.pdf 151.3 KB
  7.1 Exercise-2.pdf.pdf 469.9 KB
  8. Project Exercise 2.html 204.8 B
  8. Training multiple predictor Logistic model in Python.mp4 34 MB
  8. Training multiple predictor Logistic model in Python.vtt 4.9 KB
  8. Working with Pandas Library of Python.mp4 56.4 MB
  8. Working with Pandas Library of Python.vtt 7.2 KB
  9. Outlier Treatment.mp4 27.8 MB
  9. Outlier Treatment.vtt 4 KB
  9. Project Exercise 8.html 307.2 B
  9. Working with Seaborn Library of Python.mp4 48.9 MB
  9. Working with Seaborn Library of Python.vtt 6.5 KB
  9.1 04_06_PDE_Outlier_Treatment.pdf.pdf 355.1 KB
  [CourseClub.Me].url 0 B
  [DesireCourse.Net].url 0 B
  ▲ 152 total files

Description


Machine Learning Basics: Classification models in Python

Use classification to solve business problems and master the basics of Machine Learning classification in Python

For More Courses Visit: https://desirecourse.net

For More Courses Visit: https://courseclub.me

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