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Machine Learning Data Science with Python Kaggle A Z

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Files:

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
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4. Quiz.html 203 bytes
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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

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