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[DesireCourse Com] Udemy - Machine Learning Basics Building Regression Model in Python
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1. Introduction (Size: 2.74 GB) (Files: 158)
1. Introduction
1. Welcome to the course!.mp4
1. Welcome to the course!.vtt
2. Course contents.mp4
2. Course contents.vtt
2.1 00_Introduction_01.pdf.pdf
2. 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
3. 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 Panda Library of Python.mp4
8. Working with Panda Library of Python.vtt
8.1 Customer.csv.csv
9. Working with Seaborn Library of Python.mp4
9. Working with Seaborn Library of Python.vtt
4. 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
2.1 Lecture_machineLearning.pdf.pdf
3. Introduction to Machine learning quiz.html
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. Bi-variate analysis and Variable transformation.mp4
16. Bi-variate analysis and 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. Non-usable variables.mp4
19. Non-usable variables.vtt
19.1 04_08_PDE_Non_Usable_var.pdf.pdf
2. Data Exploration.mp4
2. Data Exploration.vtt
2.1 03_02_PDE_Data_exploration.pdf.pdf
20. Dummy variable creation Handling qualitative data.mp4
20. Dummy variable creation Handling qualitative data.vtt
20.1 04_11_Dummy_Var.pdf.pdf
21. Dummy variable creation in Python.mp4
21. Dummy variable creation in Python.vtt
22. Project Exercise 6.html
23. Correlation Analysis.mp4
23. Correlation Analysis.vtt
23.1 04_10_Correlation.pdf.pdf
24. Correlation Analysis in Python.mp4
24. Correlation Analysis in Python.vtt
25. Project Exercise 7.html
3. The Dataset and the Data Dictionary.mp4
3. The Dataset and the Data Dictionary.vtt
3.1 03_03_PDE_Raw_Data_Analysis_Uni.pdf.pdf
3.2 House_Price.csv.csv
4. Importing Data in Python.mp4
4. Importing Data in Python.vtt
4.1 House_Price.csv.csv
5. Project exercise 1.html
5.1 Movie_collection_train.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. Linear Regression
1. The Problem Statement.mp4
1. The Problem Statement.vtt
1.1 05_01_Intro.pdf.pdf
10. Multiple Linear Regression in Python.mp4
10. Multiple Linear Regression in Python.vtt
11. Project Exercise 9.html
12. Test-train split.mp4
12. Test-train split.vtt
12.1 05_12_Test_Train.pdf.pdf
13. Bias Variance trade-off.mp4
13. Bias Variance trade-off.vtt
13.1 05_13_Bias_Var_tradeoff.pdf.pdf
14. Test train split in Python.mp4
14. Test train split in Python.vtt
15. Linear models other than OLS.mp4
15. Linear models other than OLS.vtt
15.1 05_09_Other_lin_model.pdf.pdf
16. Subset selection techniques.mp4
16. Subset selection techniques.vtt
16.1 05_10_Subset_Selection.pdf.pdf
17. Shrinkage methods Ridge and Lasso.mp4
17. Shrinkage methods Ridge and Lasso.vtt
17.1 05_11_Shrinkage_methods.pdf.pdf
18. Ridge regression and Lasso in Python.mp4
18. Ridge regression and Lasso in Python.vtt
19. Project Exercise 10.html
2. Basic Equations and Ordinary Least Squares (OLS) method.mp4
2. Basic Equations and Ordinary Least Squares (OLS) method.vtt
2.1 05_02_Simple_lin_reg.pdf.pdf
20. Final Project Exercise.html
20.1 Movie_collection_test.csv.csv
21. Course Conclusion.html
3. Assessing accuracy of predicted coefficients.mp4
3. Assessing accuracy of predicted coefficients.vtt
3.1 05_03_Simple_lin_reg_Accuracy.pdf.pdf
4. Assessing Model Accuracy RSE and R squared.mp4
4. Assessing Model Accuracy RSE and R squared.vtt
4.1 05_03_Simple_lin_reg_Accuracy.pdf.pdf
5. Simple Linear Regression in Python.mp4
5. Simple Linear Regression in Python.vtt
6. Project Exercise 8.html
7. Multiple Linear Regression.mp4
7. Multiple Linear Regression.vtt
7.1 05_04_Multiple_lin_reg.pdf.pdf
8. The F - statistic.mp4
8. The F - statistic.vtt
8.1 05_05_F_stat.pdf.pdf
9. Interpreting results of Categorical variables.mp4
9. Interpreting results of Categorical variables.vtt
9.1 05_06_Cat_var.pdf.pdf
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