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1. Classification vs Regression in Machine Learning.mp4 |
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1. Competitions on Kaggle Lesson 1.mp4 |
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1. Courses in Kaggle.mp4 |
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1. Decision Tree Algorithm Theory.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. 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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2. Decision Tree Algorithm with Python Part 1.mp4 |
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2. FAQ about Kaggle.html |
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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 |
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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. Logistic Regression Algorithm with Python Part 1.mp4 |
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2. Numeric Variables (Analysis with Distplot) Lesson 2.mp4 |
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2. Random Forest Algorithm with Pyhon Part 1.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 |
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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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3. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 1.mp4 |
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3. Dealing with Outliers – Trtbps Variable Lesson 1.mp4 |
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3. Decision Tree Algorithm with Python Part 2.mp4 |
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3. Detecting Least Element of Numpy Array Min(), Ar.mp4 |
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3. Evaluating Performance Regression Error Metrics in Python.mp4 |
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3. Examining the Code Section in Kaggle Lesson 3.mp4 |
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3. Hierarchical Clustering Algorithm with Python Part 2.mp4 |
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3. Initial analysis on the dataset.mp4 |
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3. Installing Anaconda Distribution for MacOs.mp4 |
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3. K Means Clustering Algorithm with Python Part 2.mp4 |
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3. K Nearest Neighbors Algorithm with Python Part 2.mp4 |
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3. Linear Regression Algorithm With Python Part 2.mp4 |
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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 |
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3. Principal Component Analysis (PCA) with Python Part 2.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 |
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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 |
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3. Statistical Operations in Numpy.mp4 |
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3. Support Vector Machine Algorithm with Python Part 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 |
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4. Decision Tree Algorithm with Python Part 3.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. 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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