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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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188.2 MB
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1. Courses in Kaggle.mp4
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52.1 MB
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1. Datasets on Kaggle.mp4
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133.2 MB
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1. Decision Tree Algorithm Theory.mp4
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35.7 MB
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1. Dropping Columns with Low Correlation.mp4
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26.8 MB
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1. Examining Missing Values.mp4
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45.8 MB
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1. Examining the Code Section in Kaggle Lesson 1.mp4
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79.5 MB
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1. First Step to the Project.mp4
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117.1 MB
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1. Hierarchical Clustering Algorithm Theory.mp4
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28.6 MB
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1. Hyperparameter Optimization Theory.mp4
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33.1 MB
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1. Installing Anaconda Distribution for Windows.mp4
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118.3 MB
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1. K Means Clustering Algorithm Theory.mp4
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17.1 MB
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1. K Nearest Neighbors Algorithm Theory.mp4
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28.7 MB
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1. K-Fold Cross-Validation Theory.mp4
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17.4 MB
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1. Linear Regression Algorithm Theory in Machine Learning A-Z.mp4
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34.1 MB
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1. Logistic Regression.mp4
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29.4 MB
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1. Machine Learning & Data Science with Python & Kaggle A-Z.html
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HTML
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1. Numeric Variables (Analysis with Distplot) Lesson 1.mp4
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80.3 MB
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1. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 1.mp4
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49.4 MB
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1. Principal Component Analysis (PCA) Theory.mp4
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38 MB
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1. Project Conclusion and Sharing.mp4
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28.7 MB
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1. Random Forest Algorithm Theory.mp4
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22.9 MB
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1. Required Python Libraries.mp4
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63.6 MB
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1. Support Vector Machine Algorithm Theory.mp4
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21.8 MB
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1. Unsupervised Learning Overview.mp4
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1. User Page Review on Kaggle.mp4
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81.5 MB
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1. What is Bias Variance Trade-Off.mp4
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55 MB
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1. What is Discussion on Kaggle.mp4
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40.6 MB
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1. What is Kaggle.mp4
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129.6 MB
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1. What is Logistic Regression Algorithm in Machine Learning.mp4
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27.8 MB
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1. What is Machine Learning.mp4
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27.6 MB
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1. What is Supervised Learning in Machine Learning.mp4
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1. What is the Recommender System Part 1.mp4
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10. Creating a New DataFrame with the Melt() Function.mp4
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10. Feature Scaling with the Robust Scaler Method for Machine Learning Algorithms.mp4
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11.4 MB
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11. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 1.mp4
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11. Separating Data into Test and Training Set.mp4
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29.8 MB
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12. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 2.mp4
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68.1 MB
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12. Quiz.html
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13. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 1.mp4
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38.1 MB
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14. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 2.mp4
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35.5 MB
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15. Relationships between variables (Analysis with Heatmap) Lesson 1.mp4
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36.3 MB
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16. Relationships between variables (Analysis with Heatmap) Lesson 2.mp4
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17. Quiz.html
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2. Competitions on Kaggle Lesson 2.mp4
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2. Cross Validation.mp4
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30.2 MB
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2. Decision Tree Algorithm with Python Part 1.mp4
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31.6 MB
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2. Examining the Code Section in Kaggle Lesson 2.mp4
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105.8 MB
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2. FAQ about Kaggle.html
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2. FAQ about Machine Learning, Data Science.html
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15.3 KB
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2. Hierarchical Clustering Algorithm with Python Part 1.mp4
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35.5 MB
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2. Hyperparameter Optimization with Python.mp4
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47.5 MB
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2. Installing Anaconda Distribution for MacOs.mp4
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46.3 MB
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2. K Means Clustering Algorithm with Python Part 1.mp4
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30 MB
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2. K Nearest Neighbors Algorithm with Python Part 1.mp4
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35 MB
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2. K-Fold Cross-Validation with Python.mp4
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34.7 MB
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2. Linear Regression Algorithm With Python Part 1.mp4
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76.2 MB
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2. Loading the Dataset.mp4
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10 MB
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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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100.3 MB
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2. Machine Learning Terminology.mp4
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14 MB
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2. Numeric Variables (Analysis with Distplot) Lesson 2.mp4
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19.7 MB
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2. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 2.mp4
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35.6 MB
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2. Principal Component Analysis (PCA) with Python Part 1.mp4
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26 MB
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2. Quiz.html
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2. Random Forest Algorithm with Pyhon Part 1.mp4
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38.6 MB
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2. Ranking Among Users on Kaggle.mp4
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107 MB
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2. Separating variables (Numeric or Categorical).mp4
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15.8 MB
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2. Support Vector Machine Algorithm with Python Part 1.mp4
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35.6 MB
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2. Treasure in The Kaggle.mp4
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74.6 MB
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2. Visualizing Outliers.mp4
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34.9 MB
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2. What is the Recommender System Part 2.mp4
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18 MB
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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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74.8 MB
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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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48.9 MB
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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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159.9 MB
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3. Hierarchical Clustering Algorithm with Python Part 2.mp4
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28.9 MB
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3. Initial analysis on the dataset.mp4
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64 MB
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3. Installing Anaconda Distribution for Linux.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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59.4 MB
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3. Linear Regression Algorithm With Python Part 2.mp4
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106.9 MB
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3. Logistic Regression Algorithm with Python Part 2.mp4
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81.4 MB
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3. Machine Learning Project Files.html
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3. Notebook Design to be Used in the Project.mp4
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105 MB
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3. Principal Component Analysis (PCA) with Python Part 2.mp4
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8.4 MB
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3. Publishing Notebooks on Kaggle.mp4
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3. Quiz.html
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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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41.7 MB
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3. Support Vector Machine Algorithm with Python Part 2.mp4
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4. Categoric Variables (Analysis with Pie Chart) Lesson 2.mp4
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84.1 MB
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4. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 2.mp4
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56.3 MB
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4. Dealing with Outliers – Trtbps Variable Lesson 2.mp4
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43.9 MB
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4. Decision Tree Algorithm with Python Part 3.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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27.8 MB
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4. K Nearest Neighbors Algorithm with Python Part 3.mp4
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31.4 MB
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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. Overview of Jupyter Notebook and Google Colab.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. Support Vector Machine Algorithm with Python Part 3.mp4
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47.3 MB
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4. What Should Be Done to Achieve Success in Kaggle.mp4
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58.5 MB
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5. Dealing with Outliers – Thalach Variable.mp4
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5. Decision Tree Algorithm with Python Part 4.mp4
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5. Decision Tree Algorithm.mp4
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5. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 1.mp4
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28.3 MB
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5. Examining the Missing Data According to the Analysis Result.mp4
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53.8 MB
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5. Examining the Project Topic.mp4
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5. FAQ regarding Machine Learning.html
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5. Getting to Know the Kaggle Homepage.mp4
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5. K Means Clustering Algorithm with Python Part 4.mp4
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29 MB
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5. Linear Regression Algorithm With Python Part 4.mp4
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90 MB
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5. Logistic Regression Algorithm with Python Part 4.mp4
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5. Support Vector Machine Algorithm with Python Part 4.mp4
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37.6 MB
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6. Dealing with Outliers – Oldpeak Variable.mp4
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36.1 MB
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6. Decision Tree Algorithm with Python Part 5.mp4
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32.7 MB
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6. Examining Unique Values.mp4
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44.6 MB
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6. Quiz.html
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6. Recognizing Variables In Dataset.mp4
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6. Logistic Regression Algorithm with Python Part 5.mp4
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6. Support Vector Machine Algorithm.mp4
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24.5 MB
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7. Determining Distributions of Numeric Variables.mp4
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25.2 MB
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7. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 2.mp4
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47.1 MB
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7. Quiz.html
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7. Random Forest Algorithm.mp4
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29.8 MB
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8. Examining Statistics of Variables.mp4
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91.4 MB
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8. Hyperparameter Optimization (with GridSearchCV).mp4
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52.7 MB
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8. Transformation Operations on Unsymmetrical Data.mp4
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24 MB
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9. Applying One Hot Encoding Method to Categorical Variables.mp4
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24.1 MB
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9. Feature Scaling with the Robust Scaler Method.mp4
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35.2 MB
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TXT
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101
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241.9 KB
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102
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429.8 KB
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103
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655.6 KB
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104
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174.1 KB
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105
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995.7 KB
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106
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317.5 KB
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107
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849.5 KB
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108
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501.1 KB
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109
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901 KB
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110
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915.2 KB
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111
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3.2 KB
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113
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113.1 KB
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114
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162.8 KB
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115
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101.4 KB
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116
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260.4 KB
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117
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39.8 KB
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118
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572.5 KB
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119
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890.7 KB
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120
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82.7 KB
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121
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169.7 KB
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122
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296.7 KB
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123
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997.7 KB
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124
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582 KB
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125
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31.2 KB
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