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Machine Learning Data Science with Python Kaggle A Z

Torrent Info
Category Other
Added Sunday 2nd July 2023 12:09:00 AM (3 years ago)
Size6.4 GB
Peers4 Peers (4 Seeders and 0 leechers) update
Downloaded380 times
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Torrent Files (289)
FilenameSize
2. Competitions on Kaggle Lesson 2.mp4191.7 MB
TutsNode.net.txt63 bytes
2. FAQ about Machine Learning, Data Science.html15.3 KB
2. FAQ about Kaggle.html10.9 KB
3. Machine Learning Project Files.html254 bytes
5. FAQ regarding Machine Learning.html6.6 KB
[TGx]Downloaded from torrentgalaxy.to .txt585 bytes
4. FAQ regarding Python.html6.2 KB
1. Machine Learning & Data Science with Python & Kaggle A-Z.html277 bytes
5. Quiz.html203 bytes
2. Quiz.html203 bytes
2. Quiz.html203 bytes
7. Quiz.html203 bytes
5. Quiz.html203 bytes
7. Quiz.html203 bytes
6. Quiz.html203 bytes
2. Quiz.html203 bytes
6. Quiz.html203 bytes
4. Quiz.html203 bytes
3. Quiz.html203 bytes
6. Quiz.html203 bytes
3. Quiz.html203 bytes
2. Quiz.html203 bytes
4. Quiz.html203 bytes
2. Quiz.html203 bytes
4. Quiz.html203 bytes
5. Quiz.html203 bytes
7. Quiz.html203 bytes
4. Quiz.html203 bytes
3. Quiz.html203 bytes
6. Quiz.html203 bytes
17. Quiz.html203 bytes
12. Quiz.html203 bytes
9. Quiz.html203 bytes
2. Quiz.html203 bytes
4. Project Link File - Hearth Attack Prediction Project, Machine Learning.html108 bytes
4. Project Link File - Hearth Attack Prediction Project, Machine Learning.html108 bytes
0233.6 KB
1. Competitions on Kaggle Lesson 1.mp4188.2 MB
1812.0 KB
3. Examining the Code Section in Kaggle Lesson 3.mp4159.9 MB
2134.2 KB
1. Datasets on Kaggle.mp4133.2 MB
3836.6 KB
1. What is Kaggle.mp4129.6 MB
4383.4 KB
6. Recognizing Variables In Dataset.mp4126.9 MB
5130.0 KB
5. Getting to Know the Kaggle Homepage.mp4122.9 MB
690.7 KB
1. Installing Anaconda Distribution for Windows.mp4118.3 MB
7681.1 KB
1. First Step to the Project.mp4117.1 MB
8924.0 KB
3. Installing Anaconda Distribution for Linux.mp4114.8 MB
9224.4 KB
2. Ranking Among Users on Kaggle.mp4107.0 MB
10972.9 KB
3. Linear Regression Algorithm With Python Part 2.mp4106.9 MB
1174.7 KB
2. Examining the Code Section in Kaggle Lesson 2.mp4105.8 MB
12217.8 KB
3. Notebook Design to be Used in the Project.mp4105.0 MB
1341.8 KB
2. Machine Learning Model Performance Evaluation Classification Error Metrics.mp4100.3 MB
14725.7 KB
4. Machine Learning With Python.mp492.3 MB
15758.3 KB
8. Examining Statistics of Variables.mp491.4 MB
16638.2 KB
16. Relationships between variables (Analysis with Heatmap) Lesson 2.mp490.7 MB
17355.7 KB
5. Linear Regression Algorithm With Python Part 4.mp490.0 MB
1814.1 KB
4. Categoric Variables (Analysis with Pie Chart) Lesson 2.mp484.1 MB
19934.4 KB
1. User Page Review on Kaggle.mp481.5 MB
20477.0 KB
3. Logistic Regression Algorithm with Python Part 2.mp481.4 MB
21564.3 KB
1. Numeric Variables (Analysis with Distplot) Lesson 1.mp480.3 MB
22701.2 KB
1. Examining the Code Section in Kaggle Lesson 1.mp479.5 MB
23491.6 KB
5. Examining the Project Topic.mp476.5 MB
24524.0 KB
2. Linear Regression Algorithm With Python Part 1.mp476.2 MB
25842.1 KB
3. Categoric Variables (Analysis with Pie Chart) Lesson 1.mp474.8 MB
26249.4 KB
2. Treasure in The Kaggle.mp474.6 MB
27397.0 KB
2. Logistic Regression Algorithm with Python Part 1.mp472.2 MB
28778.2 KB
4. Linear Regression Algorithm With Python Part 3.mp470.3 MB
29742.2 KB
12. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 2.mp468.1 MB
30921.7 KB
3. Initial analysis on the dataset.mp464.0 MB
3136.0 KB
1. Required Python Libraries.mp463.6 MB
32451.7 KB
3. K Nearest Neighbors Algorithm with Python Part 2.mp459.4 MB
33622.8 KB
4. Hyperparameter Optimization (with GridSearchCV).mp458.8 MB
34242.8 KB
4. What Should Be Done to Achieve Success in Kaggle.mp458.5 MB
35542.7 KB
4. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 2.mp456.3 MB
36740.7 KB
1. What is Bias Variance Trade-Off.mp455.0 MB
37981.4 KB
5. Examining the Missing Data According to the Analysis Result.mp453.8 MB
38221.3 KB
10. Creating a New DataFrame with the Melt() Function.mp452.9 MB
39130.5 KB
8. Hyperparameter Optimization (with GridSearchCV).mp452.7 MB
40357.1 KB
1. Courses in Kaggle.mp452.1 MB
41871.7 KB
1. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 1.mp449.4 MB
42654.2 KB
3. Decision Tree Algorithm with Python Part 2.mp448.9 MB
4360.2 KB
2. Hyperparameter Optimization with Python.mp447.5 MB
44559.0 KB
4. Support Vector Machine Algorithm with Python Part 3.mp447.3 MB
45673.2 KB
6. Logistic Regression Algorithm with Python Part 5.mp447.2 MB
46862.5 KB
7. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 2.mp447.1 MB
47893.1 KB
2. Installing Anaconda Distribution for MacOs.mp446.3 MB
48692.0 KB
1. Examining Missing Values.mp445.8 MB
49231.2 KB
3. Evaluating Performance Regression Error Metrics in Python.mp445.7 MB
50297.5 KB
6. Examining Unique Values.mp444.6 MB
51456.5 KB
4. Dealing with Outliers – Trtbps Variable Lesson 2.mp443.9 MB
5276.9 KB
3. Registering on Kaggle and Member Login Procedures.mp443.6 MB
53445.3 KB
3. Dealing with Outliers – Trtbps Variable Lesson 1.mp442.8 MB
54168.2 KB
5. Decision Tree Algorithm with Python Part 4.mp442.4 MB
55571.0 KB
3. Support Vector Machine Algorithm with Python Part 2.mp441.7 MB
56287.9 KB
3. Roc Curve and Area Under Curve (AUC).mp441.7 MB
57315.3 KB
11. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 1.mp441.7 MB
58324.0 KB
3. Blog and Documentation Sections.mp440.9 MB
5985.2 KB
1. What is Discussion on Kaggle.mp440.6 MB
60413.8 KB
3. Random Forest Algorithm with Pyhon Part 2.mp438.7 MB
61271.1 KB
2. Random Forest Algorithm with Pyhon Part 1.mp438.6 MB
62417.5 KB
3. Publishing Notebooks on Kaggle.mp438.2 MB
63813.9 KB
13. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 1.mp438.1 MB
64968.6 KB
1. Principal Component Analysis (PCA) Theory.mp438.0 MB
6547.9 KB
5. Logistic Regression Algorithm with Python Part 4.mp437.6 MB
66457.4 KB
5. Support Vector Machine Algorithm with Python Part 4.mp437.6 MB
67460.0 KB
4. Principal Component Analysis (PCA) with Python Part 3.mp437.3 MB
68737.0 KB
15. Relationships between variables (Analysis with Heatmap) Lesson 1.mp436.3 MB
69689.3 KB
5. Dealing with Outliers – Thalach Variable.mp436.2 MB
70774.4 KB
6. Dealing with Outliers – Oldpeak Variable.mp436.1 MB
71957.0 KB
1. Decision Tree Algorithm Theory.mp435.7 MB
72261.2 KB
2. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 2.mp435.6 MB
73374.4 KB
2. Support Vector Machine Algorithm with Python Part 1.mp435.6 MB
74449.6 KB
2. Hierarchical Clustering Algorithm with Python Part 1.mp435.5 MB
75502.6 KB
14. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 2.mp435.5 MB
76546.1 KB
9. Feature Scaling with the Robust Scaler Method.mp435.2 MB
77825.4 KB
2. K Nearest Neighbors Algorithm with Python Part 1.mp435.0 MB
78982.1 KB
2. Visualizing Outliers.mp434.9 MB
79125.7 KB
4. Logistic Regression Algorithm with Python Part 3.mp434.8 MB
80226.5 KB
2. K-Fold Cross-Validation with Python.mp434.7 MB
81338.3 KB
1. Linear Regression Algorithm Theory in Machine Learning A-Z.mp434.1 MB
82957.4 KB
1. Hyperparameter Optimization Theory.mp433.1 MB
83881.6 KB
6. Decision Tree Algorithm with Python Part 5.mp432.7 MB
84354.8 KB
1. What is Supervised Learning in Machine Learning.mp431.7 MB
85309.6 KB
2. Decision Tree Algorithm with Python Part 1.mp431.6 MB
86458.3 KB
4. K Nearest Neighbors Algorithm with Python Part 3.mp431.4 MB
87610.9 KB
2. Cross Validation.mp430.2 MB
88815.3 KB
2. K Means Clustering Algorithm with Python Part 1.mp430.0 MB
8947.3 KB
7. Random Forest Algorithm.mp429.8 MB
90232.8 KB
11. Separating Data into Test and Training Set.mp429.8 MB
91234.8 KB
3. K Means Clustering Algorithm with Python Part 2.mp429.6 MB
92367.2 KB
1. Logistic Regression.mp429.4 MB
93663.3 KB
5. K Means Clustering Algorithm with Python Part 4.mp429.0 MB
94991.0 KB
3. Hierarchical Clustering Algorithm with Python Part 2.mp428.9 MB
95114.7 KB
1. Project Conclusion and Sharing.mp428.7 MB
96350.8 KB
1. K Nearest Neighbors Algorithm Theory.mp428.7 MB
97355.5 KB
1. Hierarchical Clustering Algorithm Theory.mp428.6 MB
98454.4 KB
5. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 1.mp428.3 MB
99670.7 KB
1. What is Logistic Regression Algorithm in Machine Learning.mp427.8 MB
100174.0 KB
4. K Means Clustering Algorithm with Python Part 3.mp427.8 MB
101241.9 KB
1. What is Machine Learning.mp427.6 MB
102429.8 KB
4. Overview of Jupyter Notebook and Google Colab.mp427.4 MB
103655.6 KB
1. Dropping Columns with Low Correlation.mp426.8 MB
104174.1 KB
2. Principal Component Analysis (PCA) with Python Part 1.mp426.0 MB
105995.7 KB
5. Decision Tree Algorithm.mp425.7 MB
106317.5 KB
7. Determining Distributions of Numeric Variables.mp425.2 MB
107849.5 KB
6. Support Vector Machine Algorithm.mp424.5 MB
108501.1 KB
3. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 1.mp424.1 MB
109901.0 KB
9. Applying One Hot Encoding Method to Categorical Variables.mp424.1 MB
110915.2 KB
8. Transformation Operations on Unsymmetrical Data.mp424.0 MB
1113.2 KB
1. What is the Recommender System Part 1.mp423.0 MB
1121001.0 KB
1. Random Forest Algorithm Theory.mp422.9 MB
113113.1 KB
1. Support Vector Machine Algorithm Theory.mp421.8 MB
114162.8 KB
1. Classification vs Regression in Machine Learning.mp419.9 MB
115101.4 KB
2. Numeric Variables (Analysis with Distplot) Lesson 2.mp419.7 MB
116260.4 KB
2. What is the Recommender System Part 2.mp418.0 MB
11739.8 KB
1. K-Fold Cross-Validation Theory.mp417.4 MB
118572.5 KB
1. K Means Clustering Algorithm Theory.mp417.1 MB
119890.7 KB
1. Unsupervised Learning Overview.mp416.9 MB
12082.7 KB
2. Separating variables (Numeric or Categorical).mp415.8 MB
121169.7 KB
4. Decision Tree Algorithm with Python Part 3.mp414.7 MB
122296.7 KB
2. Machine Learning Terminology.mp414.0 MB
123997.7 KB
10. Feature Scaling with the Robust Scaler Method for Machine Learning Algorithms.mp411.4 MB
124582.0 KB
2. Loading the Dataset.mp410.0 MB
12531.2 KB
3. Principal Component Analysis (PCA) with Python Part 2.mp48.4 MB
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