Torrent details for "Machine Learning Data Science with Python Kaggle A Z" Log in to bookmark
Controls:
×
Report Torrent
Please select a reason for reporting this torrent:
Your report will be reviewed by our moderation team.
×
Report Information
Loading report information...
This torrent has been reported 0 times.
Report Summary:
| User | Reason | Date |
|---|
Failed to load report information.
×
Success
Your report has been submitted successfully.
Checked by:
Category:
Language:
English
Total Size:
6.4 GB
Info Hash:
2A545E7471BAC02680D25E2AA085B5128C93F857
Added By:
Added:
July 2, 2023, 12:09 a.m.
Stats:
|
(Last updated: May 21, 2025, 11:24 p.m.)
| File | Size |
|---|---|
| 2. Competitions on Kaggle Lesson 2.mp4 | 191.7 MB |
| TutsNode.net.txt | 63 bytes |
| 2. FAQ about Machine Learning, Data Science.html | 15.3 KB |
| 2. FAQ about Kaggle.html | 10.9 KB |
| 3. Machine Learning Project Files.html | 254 bytes |
| 5. FAQ regarding Machine Learning.html | 6.6 KB |
| [TGx]Downloaded from torrentgalaxy.to .txt | 585 bytes |
| 4. FAQ regarding Python.html | 6.2 KB |
| 1. Machine Learning & Data Science with Python & Kaggle A-Z.html | 277 bytes |
| 5. Quiz.html | 203 bytes |
| 2. Quiz.html | 203 bytes |
| 2. Quiz.html | 203 bytes |
| 7. Quiz.html | 203 bytes |
| 5. Quiz.html | 203 bytes |
| 7. Quiz.html | 203 bytes |
| 6. Quiz.html | 203 bytes |
| 2. Quiz.html | 203 bytes |
| 6. Quiz.html | 203 bytes |
| 4. Quiz.html | 203 bytes |
| 3. Quiz.html | 203 bytes |
| 6. Quiz.html | 203 bytes |
| 3. Quiz.html | 203 bytes |
| 2. Quiz.html | 203 bytes |
| 4. Quiz.html | 203 bytes |
| 2. Quiz.html | 203 bytes |
| 4. Quiz.html | 203 bytes |
| 5. Quiz.html | 203 bytes |
| 7. Quiz.html | 203 bytes |
| 4. Quiz.html | 203 bytes |
| 3. Quiz.html | 203 bytes |
| 6. Quiz.html | 203 bytes |
| 17. Quiz.html | 203 bytes |
| 12. Quiz.html | 203 bytes |
| 9. Quiz.html | 203 bytes |
| 2. Quiz.html | 203 bytes |
| 4. Project Link File - Hearth Attack Prediction Project, Machine Learning.html | 108 bytes |
| 4. Project Link File - Hearth Attack Prediction Project, Machine Learning.html | 108 bytes |
| 0 | 233.6 KB |
| 1. Competitions on Kaggle Lesson 1.mp4 | 188.2 MB |
| 1 | 812.0 KB |
| 3. Examining the Code Section in Kaggle Lesson 3.mp4 | 159.9 MB |
| 2 | 134.2 KB |
| 1. Datasets on Kaggle.mp4 | 133.2 MB |
| 3 | 836.6 KB |
| 1. What is Kaggle.mp4 | 129.6 MB |
| 4 | 383.4 KB |
| 6. Recognizing Variables In Dataset.mp4 | 126.9 MB |
| 5 | 130.0 KB |
| 5. Getting to Know the Kaggle Homepage.mp4 | 122.9 MB |
| 6 | 90.7 KB |
| 1. Installing Anaconda Distribution for Windows.mp4 | 118.3 MB |
| 7 | 681.1 KB |
| 1. First Step to the Project.mp4 | 117.1 MB |
| 8 | 924.0 KB |
| 3. Installing Anaconda Distribution for Linux.mp4 | 114.8 MB |
| 9 | 224.4 KB |
| 2. Ranking Among Users on Kaggle.mp4 | 107.0 MB |
| 10 | 972.9 KB |
| 3. Linear Regression Algorithm With Python Part 2.mp4 | 106.9 MB |
| 11 | 74.7 KB |
| 2. Examining the Code Section in Kaggle Lesson 2.mp4 | 105.8 MB |
| 12 | 217.8 KB |
| 3. Notebook Design to be Used in the Project.mp4 | 105.0 MB |
| 13 | 41.8 KB |
| 2. Machine Learning Model Performance Evaluation Classification Error Metrics.mp4 | 100.3 MB |
| 14 | 725.7 KB |
| 4. Machine Learning With Python.mp4 | 92.3 MB |
| 15 | 758.3 KB |
| 8. Examining Statistics of Variables.mp4 | 91.4 MB |
| 16 | 638.2 KB |
| 16. Relationships between variables (Analysis with Heatmap) Lesson 2.mp4 | 90.7 MB |
| 17 | 355.7 KB |
| 5. Linear Regression Algorithm With Python Part 4.mp4 | 90.0 MB |
| 18 | 14.1 KB |
| 4. Categoric Variables (Analysis with Pie Chart) Lesson 2.mp4 | 84.1 MB |
| 19 | 934.4 KB |
| 1. User Page Review on Kaggle.mp4 | 81.5 MB |
| 20 | 477.0 KB |
| 3. Logistic Regression Algorithm with Python Part 2.mp4 | 81.4 MB |
| 21 | 564.3 KB |
| 1. Numeric Variables (Analysis with Distplot) Lesson 1.mp4 | 80.3 MB |
| 22 | 701.2 KB |
| 1. Examining the Code Section in Kaggle Lesson 1.mp4 | 79.5 MB |
| 23 | 491.6 KB |
| 5. Examining the Project Topic.mp4 | 76.5 MB |
| 24 | 524.0 KB |
| 2. Linear Regression Algorithm With Python Part 1.mp4 | 76.2 MB |
| 25 | 842.1 KB |
| 3. Categoric Variables (Analysis with Pie Chart) Lesson 1.mp4 | 74.8 MB |
| 26 | 249.4 KB |
| 2. Treasure in The Kaggle.mp4 | 74.6 MB |
| 27 | 397.0 KB |
| 2. Logistic Regression Algorithm with Python Part 1.mp4 | 72.2 MB |
| 28 | 778.2 KB |
| 4. Linear Regression Algorithm With Python Part 3.mp4 | 70.3 MB |
| 29 | 742.2 KB |
| 12. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 2.mp4 | 68.1 MB |
| 30 | 921.7 KB |
| 3. Initial analysis on the dataset.mp4 | 64.0 MB |
| 31 | 36.0 KB |
| 1. Required Python Libraries.mp4 | 63.6 MB |
| 32 | 451.7 KB |
| 3. K Nearest Neighbors Algorithm with Python Part 2.mp4 | 59.4 MB |
| 33 | 622.8 KB |
| 4. Hyperparameter Optimization (with GridSearchCV).mp4 | 58.8 MB |
| 34 | 242.8 KB |
| 4. What Should Be Done to Achieve Success in Kaggle.mp4 | 58.5 MB |
| 35 | 542.7 KB |
| 4. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 2.mp4 | 56.3 MB |
| 36 | 740.7 KB |
| 1. What is Bias Variance Trade-Off.mp4 | 55.0 MB |
| 37 | 981.4 KB |
| 5. Examining the Missing Data According to the Analysis Result.mp4 | 53.8 MB |
| 38 | 221.3 KB |
| 10. Creating a New DataFrame with the Melt() Function.mp4 | 52.9 MB |
| 39 | 130.5 KB |
| 8. Hyperparameter Optimization (with GridSearchCV).mp4 | 52.7 MB |
| 40 | 357.1 KB |
| 1. Courses in Kaggle.mp4 | 52.1 MB |
| 41 | 871.7 KB |
| 1. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 1.mp4 | 49.4 MB |
| 42 | 654.2 KB |
| 3. Decision Tree Algorithm with Python Part 2.mp4 | 48.9 MB |
| 43 | 60.2 KB |
| 2. Hyperparameter Optimization with Python.mp4 | 47.5 MB |
| 44 | 559.0 KB |
| 4. Support Vector Machine Algorithm with Python Part 3.mp4 | 47.3 MB |
| 45 | 673.2 KB |
| 6. Logistic Regression Algorithm with Python Part 5.mp4 | 47.2 MB |
| 46 | 862.5 KB |
| 7. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 2.mp4 | 47.1 MB |
| 47 | 893.1 KB |
| 2. Installing Anaconda Distribution for MacOs.mp4 | 46.3 MB |
| 48 | 692.0 KB |
| 1. Examining Missing Values.mp4 | 45.8 MB |
| 49 | 231.2 KB |
| 3. Evaluating Performance Regression Error Metrics in Python.mp4 | 45.7 MB |
| 50 | 297.5 KB |
| 6. Examining Unique Values.mp4 | 44.6 MB |
| 51 | 456.5 KB |
| 4. Dealing with Outliers – Trtbps Variable Lesson 2.mp4 | 43.9 MB |
| 52 | 76.9 KB |
| 3. Registering on Kaggle and Member Login Procedures.mp4 | 43.6 MB |
| 53 | 445.3 KB |
| 3. Dealing with Outliers – Trtbps Variable Lesson 1.mp4 | 42.8 MB |
| 54 | 168.2 KB |
| 5. Decision Tree Algorithm with Python Part 4.mp4 | 42.4 MB |
| 55 | 571.0 KB |
| 3. Support Vector Machine Algorithm with Python Part 2.mp4 | 41.7 MB |
| 56 | 287.9 KB |
| 3. Roc Curve and Area Under Curve (AUC).mp4 | 41.7 MB |
| 57 | 315.3 KB |
| 11. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 1.mp4 | 41.7 MB |
| 58 | 324.0 KB |
| 3. Blog and Documentation Sections.mp4 | 40.9 MB |
| 59 | 85.2 KB |
| 1. What is Discussion on Kaggle.mp4 | 40.6 MB |
| 60 | 413.8 KB |
| 3. Random Forest Algorithm with Pyhon Part 2.mp4 | 38.7 MB |
| 61 | 271.1 KB |
| 2. Random Forest Algorithm with Pyhon Part 1.mp4 | 38.6 MB |
| 62 | 417.5 KB |
| 3. Publishing Notebooks on Kaggle.mp4 | 38.2 MB |
| 63 | 813.9 KB |
| 13. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 1.mp4 | 38.1 MB |
| 64 | 968.6 KB |
| 1. Principal Component Analysis (PCA) Theory.mp4 | 38.0 MB |
| 65 | 47.9 KB |
| 5. Logistic Regression Algorithm with Python Part 4.mp4 | 37.6 MB |
| 66 | 457.4 KB |
| 5. Support Vector Machine Algorithm with Python Part 4.mp4 | 37.6 MB |
| 67 | 460.0 KB |
| 4. Principal Component Analysis (PCA) with Python Part 3.mp4 | 37.3 MB |
| 68 | 737.0 KB |
| 15. Relationships between variables (Analysis with Heatmap) Lesson 1.mp4 | 36.3 MB |
| 69 | 689.3 KB |
| 5. Dealing with Outliers – Thalach Variable.mp4 | 36.2 MB |
| 70 | 774.4 KB |
| 6. Dealing with Outliers – Oldpeak Variable.mp4 | 36.1 MB |
| 71 | 957.0 KB |
| 1. Decision Tree Algorithm Theory.mp4 | 35.7 MB |
| 72 | 261.2 KB |
| 2. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 2.mp4 | 35.6 MB |
| 73 | 374.4 KB |
| 2. Support Vector Machine Algorithm with Python Part 1.mp4 | 35.6 MB |
| 74 | 449.6 KB |
| 2. Hierarchical Clustering Algorithm with Python Part 1.mp4 | 35.5 MB |
| 75 | 502.6 KB |
| 14. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 2.mp4 | 35.5 MB |
| 76 | 546.1 KB |
| 9. Feature Scaling with the Robust Scaler Method.mp4 | 35.2 MB |
| 77 | 825.4 KB |
| 2. K Nearest Neighbors Algorithm with Python Part 1.mp4 | 35.0 MB |
| 78 | 982.1 KB |
| 2. Visualizing Outliers.mp4 | 34.9 MB |
| 79 | 125.7 KB |
| 4. Logistic Regression Algorithm with Python Part 3.mp4 | 34.8 MB |
| 80 | 226.5 KB |
| 2. K-Fold Cross-Validation with Python.mp4 | 34.7 MB |
| 81 | 338.3 KB |
| 1. Linear Regression Algorithm Theory in Machine Learning A-Z.mp4 | 34.1 MB |
| 82 | 957.4 KB |
| 1. Hyperparameter Optimization Theory.mp4 | 33.1 MB |
| 83 | 881.6 KB |
| 6. Decision Tree Algorithm with Python Part 5.mp4 | 32.7 MB |
| 84 | 354.8 KB |
| 1. What is Supervised Learning in Machine Learning.mp4 | 31.7 MB |
| 85 | 309.6 KB |
| 2. Decision Tree Algorithm with Python Part 1.mp4 | 31.6 MB |
| 86 | 458.3 KB |
| 4. K Nearest Neighbors Algorithm with Python Part 3.mp4 | 31.4 MB |
| 87 | 610.9 KB |
| 2. Cross Validation.mp4 | 30.2 MB |
| 88 | 815.3 KB |
| 2. K Means Clustering Algorithm with Python Part 1.mp4 | 30.0 MB |
| 89 | 47.3 KB |
| 7. Random Forest Algorithm.mp4 | 29.8 MB |
| 90 | 232.8 KB |
| 11. Separating Data into Test and Training Set.mp4 | 29.8 MB |
| 91 | 234.8 KB |
| 3. K Means Clustering Algorithm with Python Part 2.mp4 | 29.6 MB |
| 92 | 367.2 KB |
| 1. Logistic Regression.mp4 | 29.4 MB |
| 93 | 663.3 KB |
| 5. K Means Clustering Algorithm with Python Part 4.mp4 | 29.0 MB |
| 94 | 991.0 KB |
| 3. Hierarchical Clustering Algorithm with Python Part 2.mp4 | 28.9 MB |
| 95 | 114.7 KB |
| 1. Project Conclusion and Sharing.mp4 | 28.7 MB |
| 96 | 350.8 KB |
| 1. K Nearest Neighbors Algorithm Theory.mp4 | 28.7 MB |
| 97 | 355.5 KB |
| 1. Hierarchical Clustering Algorithm Theory.mp4 | 28.6 MB |
| 98 | 454.4 KB |
| 5. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 1.mp4 | 28.3 MB |
| 99 | 670.7 KB |
| 1. What is Logistic Regression Algorithm in Machine Learning.mp4 | 27.8 MB |
| 100 | 174.0 KB |
| 4. K Means Clustering Algorithm with Python Part 3.mp4 | 27.8 MB |
| 101 | 241.9 KB |
| 1. What is Machine Learning.mp4 | 27.6 MB |
| 102 | 429.8 KB |
| 4. Overview of Jupyter Notebook and Google Colab.mp4 | 27.4 MB |
| 103 | 655.6 KB |
| 1. Dropping Columns with Low Correlation.mp4 | 26.8 MB |
| 104 | 174.1 KB |
| 2. Principal Component Analysis (PCA) with Python Part 1.mp4 | 26.0 MB |
| 105 | 995.7 KB |
| 5. Decision Tree Algorithm.mp4 | 25.7 MB |
| 106 | 317.5 KB |
| 7. Determining Distributions of Numeric Variables.mp4 | 25.2 MB |
| 107 | 849.5 KB |
| 6. Support Vector Machine Algorithm.mp4 | 24.5 MB |
| 108 | 501.1 KB |
| 3. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 1.mp4 | 24.1 MB |
| 109 | 901.0 KB |
| 9. Applying One Hot Encoding Method to Categorical Variables.mp4 | 24.1 MB |
| 110 | 915.2 KB |
| 8. Transformation Operations on Unsymmetrical Data.mp4 | 24.0 MB |
| 111 | 3.2 KB |
| 1. What is the Recommender System Part 1.mp4 | 23.0 MB |
| 112 | 1001.0 KB |
| 1. Random Forest Algorithm Theory.mp4 | 22.9 MB |
| 113 | 113.1 KB |
| 1. Support Vector Machine Algorithm Theory.mp4 | 21.8 MB |
| 114 | 162.8 KB |
| 1. Classification vs Regression in Machine Learning.mp4 | 19.9 MB |
| 115 | 101.4 KB |
| 2. Numeric Variables (Analysis with Distplot) Lesson 2.mp4 | 19.7 MB |
| 116 | 260.4 KB |
| 2. What is the Recommender System Part 2.mp4 | 18.0 MB |
| 117 | 39.8 KB |
| 1. K-Fold Cross-Validation Theory.mp4 | 17.4 MB |
| 118 | 572.5 KB |
| 1. K Means Clustering Algorithm Theory.mp4 | 17.1 MB |
| 119 | 890.7 KB |
| 1. Unsupervised Learning Overview.mp4 | 16.9 MB |
| 120 | 82.7 KB |
| 2. Separating variables (Numeric or Categorical).mp4 | 15.8 MB |
| 121 | 169.7 KB |
| 4. Decision Tree Algorithm with Python Part 3.mp4 | 14.7 MB |
| 122 | 296.7 KB |
| 2. Machine Learning Terminology.mp4 | 14.0 MB |
| 123 | 997.7 KB |
| 10. Feature Scaling with the Robust Scaler Method for Machine Learning Algorithms.mp4 | 11.4 MB |
| 124 | 582.0 KB |
| 2. Loading the Dataset.mp4 | 10.0 MB |
| 125 | 31.2 KB |
| 3. Principal Component Analysis (PCA) with Python Part 2.mp4 | 8.4 MB |
Name
DL
Uploader
Size
S/L
Added
-
10.0 MB
[8
/
1]
2023-06-23
| Uploaded by FreeCourseWeb | Size 10.0 MB | Health [ 8 /1 ] | Added 2023-06-23 |
-
97.6 MB
[0
/
0]
2023-07-01
| Uploaded by FreeCourseWeb | Size 97.6 MB | Health [ 0 /0 ] | Added 2023-07-01 |
-
37.1 MB
[12
/
9]
2023-07-01
| Uploaded by FreeCourseWeb | Size 37.1 MB | Health [ 12 /9 ] | Added 2023-07-01 |
-
11.1 MB
[12
/
4]
2023-07-01
| Uploaded by FreeCourseWeb | Size 11.1 MB | Health [ 12 /4 ] | Added 2023-07-01 |
-
13.2 MB
[9
/
1]
2023-07-01
| Uploaded by FreeCourseWeb | Size 13.2 MB | Health [ 9 /1 ] | Added 2023-07-01 |
NOTE
SOURCE: Machine Learning Data Science with Python Kaggle A Z
-----------------------------------------------------------------------------------
COVER
-----------------------------------------------------------------------------------
MEDIAINFO
None
×