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| 1. Accessing and Making Files Available.mp4 |
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| 1. Competitions on Kaggle Lesson 1.mp4 |
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| 1. Concatenating Pandas Dataframes Concat Function.mp4 |
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| 1. Courses in Kaggle.mp4 |
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| 1. Creating a Pandas Series with a List.mp4 |
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| 1. Creating Pandas DataFrame with List.mp4 |
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| 1. Decision Tree Algorithm Theory.mp4 |
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| 1. Dropping Columns with Low Correlation.mp4 |
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| 1. Element Selection Operations in Pandas DataFrames Lesson 1.mp4 |
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| 1. Examining Missing Values.mp4 |
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| 1. Examining the Code Section in Kaggle Lesson 1.mp4 |
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| 1. First Step to the Project.mp4 |
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| 1. Hierarchical Clustering Algorithm Theory.mp4 |
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| 1. Hyperparameter Optimization Theory.mp4 |
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| 1. Indexing Numpy Arrays.mp4 |
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| 1. Installing Anaconda Distribution for Windows.mp4 |
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| 1. Introduction to NumPy Library.mp4 |
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| 1. Introduction to Pandas Library.mp4 |
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| 1. K-Fold Cross-Validation Theory.mp4 |
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| 1. K Means Clustering Algorithm Theory.mp4 |
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| 1. K Nearest Neighbors Algorithm Theory.mp4 |
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| 1. Linear Regression Algorithm Theory in Machine Learning A-Z.mp4 |
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| 1. Machine Learning & Data Science with Kaggle, Pandas , Numpy.html |
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| 1. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 1.mp4 |
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| 1. Principal Component Analysis (PCA) Theory.mp4 |
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| 2. Competitions on Kaggle Lesson 2.mp4 |
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| 2. Creating a Pandas Series with a Dictionary.mp4 |
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| 2. Creating NumPy Array with Zeros() Function.mp4 |
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| 2. Creating Pandas DataFrame with NumPy Array.mp4 |
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| 2. Cross Validation.mp4 |
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| 2. Data Entry with Csv and Txt Files.mp4 |
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| 2. Decision Tree Algorithm with Python Part 1.mp4 |
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| 2. Element Selection in Multi-Indexed DataFrames.mp4 |
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| 2. Element Selection Operations in Pandas DataFrames Lesson 2.mp4 |
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| 2. Examining the Code Section in Kaggle Lesson 2.mp4 |
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| 2. Examining the Data Set 1.mp4 |
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| 2. Examining Unique Values.mp4 |
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| 2. FAQ about Kaggle.html |
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| 2. Hierarchical Clustering Algorithm with Python Part 1.mp4 |
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| 2. Hyperparameter Optimization with Python.mp4 |
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| 2. Identifying the Largest Element of a Numpy Array.mp4 |
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| 2. K-Fold Cross-Validation with Python.mp4 |
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| 2. K Means Clustering Algorithm with Python Part 1.mp4 |
29.94MB |
| 2. K Nearest Neighbors Algorithm with Python Part 1.mp4 |
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| 2. Linear Regression Algorithm With Python Part 1.mp4 |
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| 2. Loading the Dataset.mp4 |
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| 2. Logistic Regression Algorithm with Python Part 1.mp4 |
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| 2. Machine Learning Model Performance Evaluation Classification Error Metrics.mp4 |
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| 2. Machine Learning Terminology.mp4 |
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| 2. Merge Pandas Dataframes Merge() Function Lesson 1.mp4 |
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| 2. Notebook Project Files Link regarding NumPy Python Programming Language Library.html |
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| 2. Numeric Variables (Analysis with Distplot) Lesson 2.mp4 |
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| 2. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 2.mp4 |
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| 2. Pandas Project Files Link.html |
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| 2. Pivot Tables in Pandas Library.mp4 |
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| 2. Principal Component Analysis (PCA) with Python Part 1.mp4 |
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| 2. Random Forest Algorithm with Pyhon Part 1.mp4 |
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| 2. Ranking Among Users on Kaggle.mp4 |
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| 2. Removing Rows and Columns from Pandas Data frames.mp4 |
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| 2. Slicing One-Dimensional Numpy Arrays.mp4 |
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| 2. Support Vector Machine Algorithm with Python Part 1.mp4 |
35.56MB |
| 2. The Power of NumPy.mp4 |
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| 2. Treasure in The Kaggle.mp4 |
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| 2. Visualizing Outliers.mp4 |
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| 2. What is the Recommender System Part 2.mp4 |
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| 3. Aggregation Functions in Pandas DataFrames.mp4 |
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| 3. Blog and Documentation Sections.mp4 |
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| 3. Categoric Variables (Analysis with Pie Chart) Lesson 1.mp4 |
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| 3. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 1.mp4 |
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| 3. Creating NumPy Array with Ones() Function.mp4 |
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| 3. Creating Pandas DataFrame with Dictionary.mp4 |
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| 3. Creating Pandas Series with NumPy Array.mp4 |
11.96MB |
| 3. Data Entry with Excel Files.mp4 |
21.83MB |
| 3. Dealing with Outliers – Trtbps Variable Lesson 1.mp4 |
42.84MB |
| 3. Decision Tree Algorithm with Python Part 2.mp4 |
48.97MB |
| 3. Detecting Least Element of Numpy Array Min(), Ar.mp4 |
10.19MB |
| 3. Evaluating Performance Regression Error Metrics in Python.mp4 |
45.71MB |
| 3. Examining the Code Section in Kaggle Lesson 3.mp4 |
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| 3. Hierarchical Clustering Algorithm with Python Part 2.mp4 |
28.90MB |
| 3. Initial analysis on the dataset.mp4 |
63.98MB |
| 3. Installing Anaconda Distribution for MacOs.mp4 |
46.34MB |
| 3. K Means Clustering Algorithm with Python Part 2.mp4 |
29.65MB |
| 3. K Nearest Neighbors Algorithm with Python Part 2.mp4 |
59.39MB |
| 3. Linear Regression Algorithm With Python Part 2.mp4 |
106.94MB |
| 3. Logistic Regression Algorithm with Python Part 2.mp4 |
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| 3. Machine Learning Project Files.html |
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| 3. Merge Pandas Dataframes Merge() Function Lesson 2.mp4 |
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| 3. Notebook Design to be Used in the Project.mp4 |
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| 3. Null Values in Pandas Dataframes.mp4 |
66.95MB |
| 3. Principal Component Analysis (PCA) with Python Part 2.mp4 |
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| 3. Publishing Notebooks on Kaggle.mp4 |
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| 3. Random Forest Algorithm with Pyhon Part 2.mp4 |
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| 3. Registering on Kaggle and Member Login Procedures.mp4 |
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| 3. Roc Curve and Area Under Curve (AUC).mp4 |
41.68MB |
| 3. Selecting Elements Using the xs() Function in Multi-Indexed DataFrames.mp4 |
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| 3. Separating variables (Numeric or Categorical).mp4 |
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| 3. Slicing Two-Dimensional Numpy Arrays.mp4 |
34.27MB |
| 3. Statistical Operations in Numpy.mp4 |
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| 3. Support Vector Machine Algorithm with Python Part 2.mp4 |
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| 3. Top Level Element Selection in Pandas DataFramesLesson 1.mp4 |
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| 4. 6 Article Advice And Links about Numpy, Numpy Pyhon.html |
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| 4. Assigning Value to One-Dimensional Arrays.mp4 |
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| 4. Categoric Variables (Analysis with Pie Chart) Lesson 2.mp4 |
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| 4. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 2.mp4 |
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| 4. Concatenating Numpy Arrays Concatenate() Function.mp4 |
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| 4. Creating NumPy Array with Full() Function.mp4 |
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| 4. Dealing with Outliers – Trtbps Variable Lesson 2.mp4 |
43.91MB |
| 4. Decision Tree Algorithm with Python Part 3.mp4 |
14.71MB |
| 4. Dropping Null Values Dropna() Function.mp4 |
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| 4. Examining Statistics of Variables.mp4 |
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| 4. Examining the Data Set 2.mp4 |
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| 4. Examining the Properties of Pandas DataFrames.mp4 |
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| 4. FAQ regarding Python.html |
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| 4. Hyperparameter Optimization (with GridSearchCV).mp4 |
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| 4. K Means Clustering Algorithm with Python Part 3.mp4 |
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| 4. K Nearest Neighbors Algorithm with Python Part 3.mp4 |
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| 4. Linear Regression Algorithm With Python Part 3.mp4 |
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| 4. Logistic Regression Algorithm with Python Part 3.mp4 |
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| 4. Machine Learning With Python.mp4 |
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| 4. Merge Pandas Dataframes Merge() Function Lesson 3.mp4 |
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| 4. Object Types in Series.mp4 |
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| 4. Outputting as an CSV Extension.mp4 |
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| 4. Principal Component Analysis (PCA) with Python Part 3.mp4 |
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| 4. Project Link File - Hearth Attack Prediction Project, Machine Learning.html |
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| 4. Project Link File - Hearth Attack Prediction Project, Machine Learning.html |
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| 4. Solving Second-Degree Equations with NumPy.mp4 |
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| 4. Support Vector Machine Algorithm with Python Part 3.mp4 |
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| 4. Top Level Element Selection in Pandas DataFramesLesson 2.mp4 |
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| 4. What Should Be Done to Achieve Success in Kaggle.mp4 |
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| 5. Creating NumPy Array with Arange() Function.mp4 |
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| 5. Dealing with Outliers – Thalach Variable.mp4 |
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| 5. Decision Tree Algorithm.mp4 |
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| 5. Decision Tree Algorithm with Python Part 4.mp4 |
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| 5. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 1.mp4 |
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| 5. Examining the Missing Data According to the Analysis Result.mp4 |
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| 5. Examining the Primary Features of the Pandas Seri.mp4 |
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| 5. Filling Null Values Fillna() Function.mp4 |
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| 5. Getting to Know the Kaggle Homepage.mp4 |
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| 5. Installing Anaconda Distribution for Linux.mp4 |
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| 5. K Means Clustering Algorithm with Python Part 4.mp4 |
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| 5. Linear Regression Algorithm With Python Part 4.mp4 |
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| 5. Logistic Regression Algorithm with Python Part 4.mp4 |
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| 5. Merge Pandas Dataframes Merge() Function Lesson 4.mp4 |
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| 5. Splitting One-Dimensional Numpy Arrays The Split.mp4 |
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| 5. Support Vector Machine Algorithm with Python Part 4.mp4 |
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| 6. Decision Tree Algorithm with Python Part 5.mp4 |
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| 6. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 2.mp4 |
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| 6. Fancy Indexing of One-Dimensional Arrrays.mp4 |
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| 6. Joining Pandas Dataframes Join() Function.mp4 |
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| 6. Logistic Regression Algorithm with Python Part 5.mp4 |
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| 6. Recognizing Variables In Dataset.mp4 |
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| 7. Fancy Indexing of Two-Dimensional Arrrays.mp4 |
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| 7. Feature Scaling with the Robust Scaler Method.mp4 |
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| 7. Random Forest Algorithm.mp4 |
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214.46KB |
| 9. Advanced Aggregation Functions Apply() Function.mp4 |
41.43MB |
| 9. Applying One Hot Encoding Method to Categorical Variables.mp4 |
24.09MB |
| 9. Combining Fancy Index with Normal Slicing.mp4 |
16.46MB |
| 9. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 1.mp4 |
41.70MB |
| 9. Properties of NumPy Array.mp4 |
22.01MB |
| 9. Quiz.html |
205B |
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| TutsNode.net.txt |
63B |