Complete Data Science Machine Learning with Python |
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Name |
DOWNLOAD Copy Link | |
Total Size |
11.6 GB |
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Total Files |
541 |
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Last Seen |
2025-01-04 23:39 |
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Hash |
BC0472873552620FDDD652080DF0D3C605DDCDBA |
/.../42. Competition Section on Kaggle/ |
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201.0 MB |
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197.3 MB |
/ |
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/.../48. Introduction to Machine Learning with Real Hearth Attack Prediction Project/ |
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4. Project Link File - Hearth Attack Prediction Project, Machine Learning.html |
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133.0 MB |
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122.8 MB |
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110.0 MB |
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/1. Installations/ |
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2. Notebook Project Files Link regarding NumPy Python Programming Language Library.html |
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124.1 MB |
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120.3 MB |
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48.6 MB |
/.../41. First Contact with Kaggle/ |
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4. Project Link File - Hearth Attack Prediction Project, Machine Learning.html |
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136.0 MB |
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128.9 MB |
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45.6 MB |
/.../18. Introduction to Data Visualization with Python/ |
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8.8 KB |
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13.5 MB |
/.../24. First Contact with Machine Learning/ |
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6.8 KB |
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28.9 MB |
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14.7 MB |
/.../2. NumPy Library Introduction/ |
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62.8 MB |
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47.5 MB |
/56. Extra/ |
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1. Complete Data Science & Machine Learning A-Z with Python.html |
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/.../3. Creating NumPy Array in Python/ |
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45.4 MB |
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30.9 MB |
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25.2 MB |
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23.0 MB |
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16.7 MB |
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11.7 MB |
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/.../4. Functions in the NumPy Library/ |
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40.2 MB |
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37.5 MB |
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27.4 MB |
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21.9 MB |
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17.8 MB |
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15.9 MB |
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/.../7. Pandas Library Introduction/ |
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35.6 MB |
/.../8. Series Structures in the Pandas Library/ |
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50.6 MB |
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41.1 MB |
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31.3 MB |
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20.5 MB |
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19.9 MB |
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/.../9. DataFrame Structures in Pandas Library/ |
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27.2 MB |
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23.7 MB |
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16.6 MB |
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12.7 MB |
/.../10. Element Selection Operations in DataFrame Structures/ |
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48.6 MB |
3. Top Level Element Selection in Pandas DataFramesLesson 1.mp4 |
40.1 MB |
2. Element Selection Operations in Pandas DataFrames Lesson 2.mp4 |
33.4 MB |
4. Top Level Element Selection in Pandas DataFramesLesson 2.mp4 |
32.9 MB |
1. Element Selection Operations in Pandas DataFrames Lesson 1.mp4 |
31.3 MB |
5. Top Level Element Selection in Pandas DataFramesLesson 3.mp4 |
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/.../11. Structural Operations on Pandas DataFrame/ |
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70.2 MB |
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54.1 MB |
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41.6 MB |
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36.2 MB |
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35.2 MB |
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16.3 MB |
/.../12. Multi-Indexed DataFrame Structures/ |
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44.7 MB |
3. Selecting Elements Using the xs() Function in Multi-Indexed DataFrames.mp4 |
32.8 MB |
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25.8 MB |
/.../13. Structural Concatenation Operations in Pandas DataFrame/ |
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66.9 MB |
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63.1 MB |
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60.1 MB |
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58.8 MB |
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42.7 MB |
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32.0 MB |
/.../14. Functions That Can Be Applied on a DataFrame/ |
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95.1 MB |
5. Coordinated Use of Grouping and Aggregation Functions in Pandas Dataframes.mp4 |
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49.4 MB |
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48.8 MB |
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45.0 MB |
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43.4 MB |
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39.6 MB |
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30.6 MB |
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25.6 MB |
/.../15. Pivot Tables in Pandas Library/ |
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56.9 MB |
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41.0 MB |
/.../16. File Operations in Pandas Library/ |
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67.5 MB |
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37.4 MB |
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36.3 MB |
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22.9 MB |
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20.7 MB |
/.../19. Fundamentals of Python 3/ |
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79.0 MB |
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30.3 MB |
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25.1 MB |
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/.../20. Object Oriented Programming (OOP)/ |
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5. Overriding and Overloading in Object Oriented Programming (OOP).mp4 |
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37.6 MB |
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36.3 MB |
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26.3 MB |
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18.2 MB |
/21. Matplotlib/ |
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/22. Seaborn/ |
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63.0 MB |
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/23. Geoplotlib/ |
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53.8 MB |
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40.7 MB |
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35.8 MB |
/.../25. Evaluation Metrics in Machine Learning/ |
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2. Machine Learning Model Performance Evaluation Classification Error Metrics.mp4 |
105.1 MB |
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96.7 MB |
3. Evaluating Performance Regression Error Metrics in Python.mp4 |
47.9 MB |
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20.9 MB |
/.../26. Supervised Learning with Machine Learning/ |
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33.2 MB |
/.../28. Bias Variance Trade-Off in Machine Learning/ |
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57.7 MB |
/.../29. Logistic Regression Algorithm in Machine Learning A-Z/ |
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85.4 MB |
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75.7 MB |
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49.6 MB |
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49.5 MB |
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41.3 MB |
1. What is Logistic Regression Algorithm in Machine Learning.mp4 |
29.2 MB |
/.../31. K Nearest Neighbors Algorithm in Machine Learning A-Z/ |
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62.3 MB |
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36.7 MB |
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32.9 MB |
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30.0 MB |
/.../33. Decision Tree Algorithm in Machine Learning A-Z/ |
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51.3 MB |
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44.6 MB |
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37.5 MB |
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34.3 MB |
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33.1 MB |
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15.4 MB |
/.../35. Support Vector Machine Algorithm in Machine Learning A-Z/ |
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43.7 MB |
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39.4 MB |
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37.3 MB |
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36.5 MB |
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22.9 MB |
/.../37. K Means Clustering Algorithm in Machine Learning A-Z/ |
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31.4 MB |
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31.1 MB |
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30.4 MB |
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29.1 MB |
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18.0 MB |
/.../43. Dataset Section on Kaggle/ |
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139.7 MB |
/.../44. Code Section on Kaggle/ |
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167.7 MB |
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111.0 MB |
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83.4 MB |
/.../45. Discussion Section on Kaggle/ |
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42.6 MB |
/.../46. Other Most Used Options on Kaggle/ |
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112.2 MB |
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54.7 MB |
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42.8 MB |
/.../47. Details on Kaggle/ |
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85.5 MB |
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78.3 MB |
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61.3 MB |
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40.1 MB |
/.../49. First Organization/ |
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67.1 MB |
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66.6 MB |
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10.5 MB |
/.../50. Preparation For Exploratory Data Analysis (EDA) in Data Science/ |
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95.8 MB |
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48.0 MB |
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46.7 MB |
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16.6 MB |
/.../51. Exploratory Data Analysis (EDA) - Uni-variate Analysis/ |
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4. Categoric Variables (Analysis with Pie Chart) Lesson 2.mp4 |
88.1 MB |
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84.3 MB |
3. Categoric Variables (Analysis with Pie Chart) Lesson 1.mp4 |
78.4 MB |
5. Examining the Missing Data According to the Analysis Result.mp4 |
56.4 MB |
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20.7 MB |
/.../52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/ |
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14. Relationships between variables (Analysis with Heatmap) Lesson 2.mp4 |
95.1 MB |
10. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 2.mp4 |
71.4 MB |
4. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 2.mp4 |
59.0 MB |
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55.5 MB |
1. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 1.mp4 |
51.8 MB |
6. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 2.mp4 |
49.4 MB |
9. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 1.mp4 |
43.7 MB |
11. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 1.mp4 |
39.9 MB |
13. Relationships between variables (Analysis with Heatmap) Lesson 1.mp4 |
38.1 MB |
2. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 2.mp4 |
37.4 MB |
12. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 2.mp4 |
37.2 MB |
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36.9 MB |
5. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 1.mp4 |
29.7 MB |
3. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 1.mp4 |
25.3 MB |
/.../53. Preparation for Modelling in Machine Learning/ |
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37.8 MB |
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28.1 MB |
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26.4 MB |
9. Applying One Hot Encoding Method to Categorical Variables.mp4 |
25.3 MB |
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25.2 MB |
10. Feature Scaling with the Robust Scaler Method for Machine Learning Algorithms.mp4 |
12.0 MB |
/.../54. Modelling for Machine Learning/ |
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61.6 MB |
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55.2 MB |
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31.7 MB |
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/55. Conclusion/ |
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/.../17. Code Files And Resources Python data analysis and visualization/ |
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/.../27. Linear Regression Algorithm in Machine Learning A-Z/ |
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112.1 MB |
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94.4 MB |
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79.9 MB |
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73.7 MB |
1. Linear Regression Algorithm Theory in Machine Learning A-Z.mp4 |
35.7 MB |
/.../6. Operations in Numpy Library/ |
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75.3 MB |
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33.6 MB |
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25.4 MB |
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22.2 MB |
/.../32. Hyperparameter Optimization/ |
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49.8 MB |
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34.7 MB |
/.../5. Indexing, Slicing, and Assigning NumPy Arrays/ |
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48.0 MB |
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37.1 MB |
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35.9 MB |
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27.8 MB |
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23.4 MB |
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21.5 MB |
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19.1 MB |
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17.3 MB |
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13.3 MB |
/.../34. Random Forest Algorithm in Machine Learning A-Z/ |
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40.6 MB |
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40.5 MB |
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24.0 MB |
/.../39. Principal Component Analysis (PCA) in Machine Learning A-Z/ |
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39.8 MB |
4. Principal Component Analysis (PCA) with Python Part 3.mp4 |
39.1 MB |
2. Principal Component Analysis (PCA) with Python Part 1.mp4 |
27.3 MB |
3. Principal Component Analysis (PCA) with Python Part 2.mp4 |
8.8 MB |
/.../38. Hierarchical Clustering Algorithm in machine learning data science/ |
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37.2 MB |
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30.3 MB |
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29.9 MB |
/.../30. K-fold Cross-Validation in Machine Learning A-Z/ |
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36.3 MB |
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18.3 MB |
/.../40. Recommender System Algorithm in Machine Learning A-Z/ |
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24.2 MB |
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18.8 MB |
/.../36. Unsupervised Learning with Machine Learning/ |
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17.7 MB |
Total files 541 |
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