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| [TGx]Downloaded from torrentgalaxy.to .txt |
585B |
| 0 |
233.61KB |
| 1 |
812.02KB |
| 1. Classification vs Regression in Machine Learning.mp4 |
19.90MB |
| 1. Competitions on Kaggle Lesson 1.mp4 |
188.21MB |
| 1. Courses in Kaggle.mp4 |
52.15MB |
| 1. Datasets on Kaggle.mp4 |
133.18MB |
| 1. Decision Tree Algorithm Theory.mp4 |
35.74MB |
| 1. Dropping Columns with Low Correlation.mp4 |
26.83MB |
| 1. Examining Missing Values.mp4 |
45.77MB |
| 1. Examining the Code Section in Kaggle Lesson 1.mp4 |
79.52MB |
| 1. First Step to the Project.mp4 |
117.10MB |
| 1. Hierarchical Clustering Algorithm Theory.mp4 |
28.56MB |
| 1. Hyperparameter Optimization Theory.mp4 |
33.14MB |
| 1. Installing Anaconda Distribution for Windows.mp4 |
118.33MB |
| 1. K-Fold Cross-Validation Theory.mp4 |
17.44MB |
| 1. K Means Clustering Algorithm Theory.mp4 |
17.13MB |
| 1. K Nearest Neighbors Algorithm Theory.mp4 |
28.65MB |
| 1. Linear Regression Algorithm Theory in Machine Learning A-Z.mp4 |
34.07MB |
| 1. Logistic Regression.mp4 |
29.35MB |
| 1. Machine Learning & Data Science with Python & Kaggle A-Z.html |
277B |
| 1. Numeric Variables (Analysis with Distplot) Lesson 1.mp4 |
80.32MB |
| 1. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 1.mp4 |
49.36MB |
| 1. Principal Component Analysis (PCA) Theory.mp4 |
37.95MB |
| 1. Project Conclusion and Sharing.mp4 |
28.66MB |
| 1. Random Forest Algorithm Theory.mp4 |
22.89MB |
| 1. Required Python Libraries.mp4 |
63.56MB |
| 1. Support Vector Machine Algorithm Theory.mp4 |
21.84MB |
| 1. Unsupervised Learning Overview.mp4 |
16.92MB |
| 1. User Page Review on Kaggle.mp4 |
81.53MB |
| 1. What is Bias Variance Trade-Off.mp4 |
55.04MB |
| 1. What is Discussion on Kaggle.mp4 |
40.60MB |
| 1. What is Kaggle.mp4 |
129.63MB |
| 1. What is Logistic Regression Algorithm in Machine Learning.mp4 |
27.83MB |
| 1. What is Machine Learning.mp4 |
27.58MB |
| 1. What is Supervised Learning in Machine Learning.mp4 |
31.70MB |
| 1. What is the Recommender System Part 1.mp4 |
23.02MB |
| 10 |
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| 10. Creating a New DataFrame with the Melt() Function.mp4 |
52.87MB |
| 10. Feature Scaling with the Robust Scaler Method for Machine Learning Algorithms.mp4 |
11.43MB |
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| 11 |
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| 11. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 1.mp4 |
41.68MB |
| 11. Separating Data into Test and Training Set.mp4 |
29.77MB |
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| 12. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 2.mp4 |
68.10MB |
| 12. Quiz.html |
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| 13. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 1.mp4 |
38.05MB |
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| 14. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 2.mp4 |
35.47MB |
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| 15. Relationships between variables (Analysis with Heatmap) Lesson 1.mp4 |
36.33MB |
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| 16. Relationships between variables (Analysis with Heatmap) Lesson 2.mp4 |
90.65MB |
| 17 |
355.72KB |
| 17. Quiz.html |
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| 18 |
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| 2 |
134.21KB |
| 2. Competitions on Kaggle Lesson 2.mp4 |
191.73MB |
| 2. Cross Validation.mp4 |
30.20MB |
| 2. Decision Tree Algorithm with Python Part 1.mp4 |
31.55MB |
| 2. Examining the Code Section in Kaggle Lesson 2.mp4 |
105.79MB |
| 2. FAQ about Kaggle.html |
10.94KB |
| 2. FAQ about Machine Learning, Data Science.html |
15.29KB |
| 2. Hierarchical Clustering Algorithm with Python Part 1.mp4 |
35.51MB |
| 2. Hyperparameter Optimization with Python.mp4 |
47.45MB |
| 2. Installing Anaconda Distribution for MacOs.mp4 |
46.32MB |
| 2. K-Fold Cross-Validation with Python.mp4 |
34.67MB |
| 2. K Means Clustering Algorithm with Python Part 1.mp4 |
29.95MB |
| 2. K Nearest Neighbors Algorithm with Python Part 1.mp4 |
35.04MB |
| 2. Linear Regression Algorithm With Python Part 1.mp4 |
76.18MB |
| 2. Loading the Dataset.mp4 |
9.97MB |
| 2. Logistic Regression Algorithm with Python Part 1.mp4 |
72.24MB |
| 2. Machine Learning Model Performance Evaluation Classification Error Metrics.mp4 |
100.29MB |
| 2. Machine Learning Terminology.mp4 |
14.03MB |
| 2. Numeric Variables (Analysis with Distplot) Lesson 2.mp4 |
19.75MB |
| 2. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 2.mp4 |
35.63MB |
| 2. Principal Component Analysis (PCA) with Python Part 1.mp4 |
26.03MB |
| 2. Quiz.html |
203B |
| 2. Quiz.html |
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| 2. Quiz.html |
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| 2. Quiz.html |
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| 2. Quiz.html |
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| 2. Quiz.html |
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| 2. Random Forest Algorithm with Pyhon Part 1.mp4 |
38.59MB |
| 2. Ranking Among Users on Kaggle.mp4 |
107.05MB |
| 2. Separating variables (Numeric or Categorical).mp4 |
15.83MB |
| 2. Support Vector Machine Algorithm with Python Part 1.mp4 |
35.56MB |
| 2. Treasure in The Kaggle.mp4 |
74.61MB |
| 2. Visualizing Outliers.mp4 |
34.88MB |
| 2. What is the Recommender System Part 2.mp4 |
17.96MB |
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| 3 |
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| 3. Blog and Documentation Sections.mp4 |
40.92MB |
| 3. Categoric Variables (Analysis with Pie Chart) Lesson 1.mp4 |
74.76MB |
| 3. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 1.mp4 |
24.12MB |
| 3. Dealing with Outliers – Trtbps Variable Lesson 1.mp4 |
42.84MB |
| 3. Decision Tree Algorithm with Python Part 2.mp4 |
48.94MB |
| 3. Evaluating Performance Regression Error Metrics in Python.mp4 |
45.71MB |
| 3. Examining the Code Section in Kaggle Lesson 3.mp4 |
159.87MB |
| 3. Hierarchical Clustering Algorithm with Python Part 2.mp4 |
28.89MB |
| 3. Initial analysis on the dataset.mp4 |
63.96MB |
| 3. Installing Anaconda Distribution for Linux.mp4 |
114.78MB |
| 3. K Means Clustering Algorithm with Python Part 2.mp4 |
29.64MB |
| 3. K Nearest Neighbors Algorithm with Python Part 2.mp4 |
59.39MB |
| 3. Linear Regression Algorithm With Python Part 2.mp4 |
106.93MB |
| 3. Logistic Regression Algorithm with Python Part 2.mp4 |
81.45MB |
| 3. Machine Learning Project Files.html |
254B |
| 3. Notebook Design to be Used in the Project.mp4 |
104.96MB |
| 3. Principal Component Analysis (PCA) with Python Part 2.mp4 |
8.42MB |
| 3. Publishing Notebooks on Kaggle.mp4 |
38.21MB |
| 3. Quiz.html |
203B |
| 3. Quiz.html |
203B |
| 3. Quiz.html |
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| 3. Random Forest Algorithm with Pyhon Part 2.mp4 |
38.74MB |
| 3. Registering on Kaggle and Member Login Procedures.mp4 |
43.57MB |
| 3. Roc Curve and Area Under Curve (AUC).mp4 |
41.69MB |
| 3. Support Vector Machine Algorithm with Python Part 2.mp4 |
41.72MB |
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| 37 |
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| 38 |
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| 39 |
130.53KB |
| 4 |
383.40KB |
| 4. Categoric Variables (Analysis with Pie Chart) Lesson 2.mp4 |
84.09MB |
| 4. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 2.mp4 |
56.28MB |
| 4. Dealing with Outliers – Trtbps Variable Lesson 2.mp4 |
43.92MB |
| 4. Decision Tree Algorithm with Python Part 3.mp4 |
14.71MB |
| 4. FAQ regarding Python.html |
6.23KB |
| 4. Hyperparameter Optimization (with GridSearchCV).mp4 |
58.76MB |
| 4. K Means Clustering Algorithm with Python Part 3.mp4 |
27.76MB |
| 4. K Nearest Neighbors Algorithm with Python Part 3.mp4 |
31.40MB |
| 4. Linear Regression Algorithm With Python Part 3.mp4 |
70.28MB |
| 4. Logistic Regression Algorithm with Python Part 3.mp4 |
34.78MB |
| 4. Machine Learning With Python.mp4 |
92.26MB |
| 4. Overview of Jupyter Notebook and Google Colab.mp4 |
27.36MB |
| 4. Principal Component Analysis (PCA) with Python Part 3.mp4 |
37.28MB |
| 4. Project Link File - Hearth Attack Prediction Project, Machine Learning.html |
108B |
| 4. Project Link File - Hearth Attack Prediction Project, Machine Learning.html |
108B |
| 4. Quiz.html |
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| 4. Quiz.html |
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| 4. Quiz.html |
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| 4. Quiz.html |
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| 4. Support Vector Machine Algorithm with Python Part 3.mp4 |
47.34MB |
| 4. What Should Be Done to Achieve Success in Kaggle.mp4 |
58.47MB |
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| 49 |
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| 5 |
129.99KB |
| 5. Dealing with Outliers – Thalach Variable.mp4 |
36.24MB |
| 5. Decision Tree Algorithm.mp4 |
25.69MB |
| 5. Decision Tree Algorithm with Python Part 4.mp4 |
42.44MB |
| 5. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 1.mp4 |
28.35MB |
| 5. Examining the Missing Data According to the Analysis Result.mp4 |
53.78MB |
| 5. Examining the Project Topic.mp4 |
76.49MB |
| 5. FAQ regarding Machine Learning.html |
6.59KB |
| 5. Getting to Know the Kaggle Homepage.mp4 |
122.91MB |
| 5. K Means Clustering Algorithm with Python Part 4.mp4 |
29.03MB |
| 5. Linear Regression Algorithm With Python Part 4.mp4 |
89.99MB |
| 5. Logistic Regression Algorithm with Python Part 4.mp4 |
37.55MB |
| 5. Quiz.html |
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| 5. Quiz.html |
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| 5. Quiz.html |
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| 5. Support Vector Machine Algorithm with Python Part 4.mp4 |
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| 6. Dealing with Outliers – Oldpeak Variable.mp4 |
36.07MB |
| 6. Decision Tree Algorithm with Python Part 5.mp4 |
32.65MB |
| 6. Examining Unique Values.mp4 |
44.55MB |
| 6. Logistic Regression Algorithm with Python Part 5.mp4 |
47.16MB |
| 6. Quiz.html |
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| 6. Quiz.html |
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| 6. Quiz.html |
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| 6. Quiz.html |
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| 6. Recognizing Variables In Dataset.mp4 |
126.87MB |
| 6. Support Vector Machine Algorithm.mp4 |
24.51MB |
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| 7. Determining Distributions of Numeric Variables.mp4 |
25.17MB |
| 7. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 2.mp4 |
47.13MB |
| 7. Quiz.html |
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| 7. Quiz.html |
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| 7. Quiz.html |
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| 7. Random Forest Algorithm.mp4 |
29.77MB |
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| 8. Examining Statistics of Variables.mp4 |
91.38MB |
| 8. Hyperparameter Optimization (with GridSearchCV).mp4 |
52.65MB |
| 8. Transformation Operations on Unsymmetrical Data.mp4 |
24.00MB |
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| 9. Applying One Hot Encoding Method to Categorical Variables.mp4 |
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| 9. Feature Scaling with the Robust Scaler Method.mp4 |
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| 9. Quiz.html |
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| TutsNode.net.txt |
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