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Machine Learning Data Science with Python Kaggle Pandas

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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. Notebook Project Files Link regarding NumPy Python Programming Language Library.html 155 bytes
2. FAQ about Kaggle.html 10.9 KB
5. FAQ regarding Machine Learning.html 6.6 KB
4. FAQ regarding Python.html 6.2 KB
4. 6 Article Advice And Links about Numpy, Numpy Pyhon.html 4.2 KB
[TGx]Downloaded from torrentgalaxy.to .txt 585 bytes
3. Quiz.html 205 bytes
1. Machine Learning & Data Science with Kaggle, Pandas , Numpy.html 266 bytes
3. Machine Learning Project Files.html 254 bytes
10. Quiz.html 205 bytes
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5. Quiz.html 205 bytes
2. Pandas Project Files Link.html 180 bytes
7. Quiz.html 205 bytes
4. Quiz.html 205 bytes
5. Quiz.html 205 bytes
6. Quiz.html 205 bytes
15. Quiz.html 205 bytes
12. Quiz.html 205 bytes
9. Quiz.html 205 bytes
2. Quiz.html 205 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
0 241.7 KB
1. Competitions on Kaggle Lesson 1.mp4 188.2 MB
1 821.5 KB
3. Examining the Code Section in Kaggle Lesson 3.mp4 159.8 MB
2 181.0 KB
1. Datasets on Kaggle.mp4 133.2 MB
3 813.9 KB
1. What is Kaggle.mp4 129.7 MB
4 256.3 KB
6. Recognizing Variables In Dataset.mp4 126.9 MB
5 126.5 KB
5. Getting to Know the Kaggle Homepage.mp4 122.9 MB
6 120.6 KB
1. Installing Anaconda Distribution for Windows.mp4 118.3 MB
7 711.8 KB
1. First Step to the Project.mp4 117.2 MB
8 832.8 KB
5. Installing Anaconda Distribution for Linux.mp4 114.8 MB
9 214.5 KB
2. Ranking Among Users on Kaggle.mp4 107.0 MB
10 2.2 KB
3. Linear Regression Algorithm With Python Part 2.mp4 106.9 MB
11 59.1 KB
2. Examining the Code Section in Kaggle Lesson 2.mp4 105.8 MB
12 199.3 KB
3. Notebook Design to be Used in the Project.mp4 105.0 MB
13 35.9 KB
2. Machine Learning Model Performance Evaluation Classification Error Metrics.mp4 100.3 MB
14 731.5 KB
4. Machine Learning With Python.mp4 92.3 MB
15 757.9 KB
4. Examining Statistics of Variables.mp4 91.4 MB
16 636.0 KB
3. Aggregation Functions in Pandas DataFrames.mp4 90.7 MB
17 300.2 KB
14. Relationships between variables (Analysis with Heatmap) Lesson 2.mp4 90.7 MB
18 352.2 KB
5. Linear Regression Algorithm With Python Part 4.mp4 90.0 MB
19 5.1 KB
5. Coordinated Use of Grouping and Aggregation Functions in Pandas Dataframes.mp4 88.1 MB
20 903.6 KB
4. Categoric Variables (Analysis with Pie Chart) Lesson 2.mp4 84.0 MB
21 986.5 KB
1. User Page Review on Kaggle.mp4 81.6 MB
22 438.4 KB
3. Logistic Regression Algorithm with Python Part 2.mp4 81.5 MB
23 556.6 KB
1. Numeric Variables (Analysis with Distplot) Lesson 1.mp4 80.4 MB
24 612.6 KB
1. Examining the Code Section in Kaggle Lesson 1.mp4 79.5 MB
25 464.9 KB
5. Examining the Project Topic.mp4 76.5 MB
26 485.1 KB
2. Linear Regression Algorithm With Python Part 1.mp4 76.2 MB
27 844.4 KB
3. Categoric Variables (Analysis with Pie Chart) Lesson 1.mp4 74.8 MB
28 240.4 KB
2. Treasure in The Kaggle.mp4 74.7 MB
29 350.4 KB
2. Logistic Regression Algorithm with Python Part 1.mp4 72.2 MB
30 790.2 KB
2. Arithmetic Operations in Numpy.mp4 71.9 MB
31 129.9 KB
4. Linear Regression Algorithm With Python Part 3.mp4 70.3 MB
32 734.3 KB
10. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 2.mp4 68.1 MB
33 949.3 KB
3. Null Values in Pandas Dataframes.mp4 67.0 MB
34 50.8 KB
2. Data Entry with Csv and Txt Files.mp4 64.4 MB
35 659.1 KB
3. Initial analysis on the dataset.mp4 64.0 MB
36 19.4 KB
1. Concatenating Pandas Dataframes Concat Function.mp4 63.9 MB
37 125.5 KB
1. Required Python Libraries.mp4 63.6 MB
38 438.0 KB
4. Merge Pandas Dataframes Merge() Function Lesson 3.mp4 60.1 MB
39 883.1 KB
2. The Power of NumPy.mp4 59.9 MB
40 137.8 KB
3. K Nearest Neighbors Algorithm with Python Part 2.mp4 59.4 MB
41 623.9 KB
4. Hyperparameter Optimization (with GridSearchCV).mp4 58.8 MB
42 237.4 KB
4. What Should Be Done to Achieve Success in Kaggle.mp4 58.4 MB
43 594.2 KB
2. Merge Pandas Dataframes Merge() Function Lesson 1.mp4 57.3 MB
44 717.7 KB
4. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 2.mp4 56.3 MB
45 741.4 KB
6. Joining Pandas Dataframes Join() Function.mp4 56.1 MB
46 971.3 KB
1. What is Bias Variance Trade-Off.mp4 55.0 MB
47 983.0 KB
2. Pivot Tables in Pandas Library.mp4 54.2 MB
48 787.8 KB
5. Examining the Missing Data According to the Analysis Result.mp4 53.8 MB
49 223.0 KB
8. Creating a New DataFrame with the Melt() Function.mp4 52.9 MB
50 120.7 KB
8. Hyperparameter Optimization (with GridSearchCV).mp4 52.7 MB
51 334.2 KB
1. Courses in Kaggle.mp4 52.2 MB
52 855.0 KB
5. Filling Null Values Fillna() Function.mp4 51.6 MB
53 395.3 KB
1. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 1.mp4 49.3 MB
54 678.8 KB
3. Decision Tree Algorithm with Python Part 2.mp4 49.0 MB
55 31.5 KB
6. Most Applied Methods on Pandas Series.mp4 48.2 MB
56 826.0 KB
2. Hyperparameter Optimization with Python.mp4 47.5 MB
57 546.1 KB
4. Support Vector Machine Algorithm with Python Part 3.mp4 47.3 MB
58 670.5 KB
5. Logistic Regression Algorithm with Python Part 4.mp4 47.2 MB
59 855.9 KB
6. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 2.mp4 47.2 MB
60 861.6 KB
8. Advanced Aggregation Functions Transform() Function.mp4 47.1 MB
61 925.7 KB
4. Examining the Data Set 2.mp4 46.6 MB
62 442.6 KB
6. Element Selection with Conditional Operations in.mp4 46.4 MB
63 647.6 KB
3. Installing Anaconda Distribution for MacOs.mp4 46.3 MB
64 671.0 KB
1. Examining Missing Values.mp4 45.8 MB
65 224.7 KB
7. Fancy Indexing of Two-Dimensional Arrrays.mp4 45.7 MB
66 283.6 KB
3. Evaluating Performance Regression Error Metrics in Python.mp4 45.7 MB
67 296.8 KB
1. Introduction to NumPy Library.mp4 45.3 MB
68 714.8 KB
2. Examining Unique Values.mp4 44.6 MB
69 449.2 KB
4. Dealing with Outliers – Trtbps Variable Lesson 2.mp4 43.9 MB
70 88.3 KB
3. Registering on Kaggle and Member Login Procedures.mp4 43.5 MB
71 461.2 KB
8. Creating NumPy Array with Random() Function.mp4 43.3 MB
72 720.3 KB
2. Examining the Data Set 1.mp4 42.9 MB
73 124.6 KB
3. Dealing with Outliers – Trtbps Variable Lesson 1.mp4 42.8 MB
74 167.5 KB
1. Multi-Index and Index Hierarchy in Pandas DataFrames.mp4 42.7 MB
75 348.6 KB
5. Decision Tree Algorithm with Python Part 4.mp4 42.5 MB
76 517.6 KB
3. Support Vector Machine Algorithm with Python Part 2.mp4 41.7 MB
77 288.1 KB
9. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 1.mp4 41.7 MB
78 305.5 KB
3. Roc Curve and Area Under Curve (AUC).mp4 41.7 MB
79 323.0 KB
9. Advanced Aggregation Functions Apply() Function.mp4 41.4 MB
80 586.6 KB
3. Blog and Documentation Sections.mp4 40.9 MB
81 92.7 KB
5. Merge Pandas Dataframes Merge() Function Lesson 4.mp4 40.7 MB
82 308.3 KB
1. What is Discussion on Kaggle.mp4 40.6 MB
83 359.9 KB
6. Setting Index in Pandas DataFrames.mp4 39.7 MB
84 306.3 KB
6. Logistic Regression Algorithm with Python Part 5.mp4 39.4 MB
85 658.6 KB
1. Creating a Pandas Series with a List.mp4 39.2 MB
86 819.2 KB
1. Examining the Data Set 3.mp4 39.1 MB
87 900.1 KB
3. Random Forest Algorithm with Pyhon Part 2.mp4 38.7 MB
88 270.0 KB
2. Random Forest Algorithm with Pyhon Part 1.mp4 38.6 MB
89 418.6 KB
4. Concatenating Numpy Arrays Concatenate() Function.mp4 38.4 MB
90 638.4 KB
3. Top Level Element Selection in Pandas DataFramesLesson 1.mp4 38.3 MB
91 709.9 KB
3. Publishing Notebooks on Kaggle.mp4 38.2 MB
92 810.0 KB
11. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 1.mp4 38.1 MB
93 955.4 KB
1. Principal Component Analysis (PCA) Theory.mp4 38.0 MB
94 49.1 KB
1. Loading a Dataset from the Seaborn Library.mp4 37.7 MB
95 288.7 KB
5. Support Vector Machine Algorithm with Python Part 4.mp4 37.6 MB
96 453.1 KB
4. Principal Component Analysis (PCA) with Python Part 3.mp4 37.3 MB
97 744.2 KB
13. Relationships between variables (Analysis with Heatmap) Lesson 1.mp4 36.4 MB
98 646.8 KB
5. Dealing with Outliers – Thalach Variable.mp4 36.2 MB
99 789.3 KB
6. Dealing with Outliers – Oldpeak Variable.mp4 36.1 MB
100 938.9 KB
1. Decision Tree Algorithm Theory.mp4 35.8 MB
101 249.1 KB
6. Splitting Two-Dimensional Numpy Arrays Split(),.mp4 35.7 MB
102 286.5 KB
4. Outputting as an CSV Extension.mp4 35.7 MB
103 298.0 KB
2. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 2.mp4 35.6 MB
104 389.3 KB
2. Support Vector Machine Algorithm with Python Part 1.mp4 35.6 MB
105 445.7 KB
2. Hierarchical Clustering Algorithm with Python Part 1.mp4 35.5 MB
106 496.9 KB
12. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 2.mp4 35.5 MB
107 552.4 KB
5. Assigning Value to Two-Dimensional Array.mp4 35.4 MB
108 602.0 KB
7. Feature Scaling with the Robust Scaler Method.mp4 35.2 MB
109 824.9 KB
2. K Nearest Neighbors Algorithm with Python Part 1.mp4 35.1 MB
110 970.8 KB
2. Visualizing Outliers.mp4 34.9 MB
111 137.2 KB
4. Logistic Regression Algorithm with Python Part 3.mp4 34.8 MB
112 229.2 KB
2. K-Fold Cross-Validation with Python.mp4 34.7 MB
113 349.0 KB
1. Accessing and Making Files Available.mp4 34.6 MB
114 384.1 KB
4. Dropping Null Values Dropna() Function.mp4 34.5 MB
115 481.8 KB
3. Slicing Two-Dimensional Numpy Arrays.mp4 34.3 MB
116 744.1 KB
1. Linear Regression Algorithm Theory in Machine Learning A-Z.mp4 34.1 MB
117 958.0 KB
1. Introduction to Pandas Library.mp4 33.9 MB
118 76.0 KB
1. Adding Columns to Pandas Data Frames.mp4 33.6 MB
119 434.2 KB
1. Hyperparameter Optimization Theory.mp4 33.1 MB
120 874.9 KB
6. Decision Tree Algorithm with Python Part 5.mp4 32.7 MB
121 331.5 KB
3. Statistical Operations in Numpy.mp4 32.0 MB
122 21.4 KB
2. Element Selection Operations in Pandas DataFrames Lesson 2.mp4 31.8 MB
123 178.3 KB
1. What is Supervised Learning in Machine Learning.mp4 31.7 MB
124 320.3 KB
2. Decision Tree Algorithm with Python Part 1.mp4 31.5 MB
125 467.7 KB
4. Top Level Element Selection in Pandas DataFramesLesson 2.mp4 31.4 MB
126 592.9 KB
4. K Nearest Neighbors Algorithm with Python Part 3.mp4 31.4 MB
127 623.1 KB
3. Selecting Elements Using the xs() Function in Multi-Indexed DataFrames.mp4 31.3 MB
128 733.9 KB
3. Merge Pandas Dataframes Merge() Function Lesson 2.mp4 30.5 MB
129 495.7 KB
2. Cross Validation.mp4 30.2 MB
130 811.8 KB
2. K Means Clustering Algorithm with Python Part 1.mp4 29.9 MB
131 64.6 KB
7. Indexing and Slicing Pandas Series.mp4 29.9 MB
132 89.7 KB
1. Element Selection Operations in Pandas DataFrames Lesson 1.mp4 29.9 MB
133 120.0 KB
7. Random Forest Algorithm.mp4 29.8 MB
134 228.4 KB
11. Separating Data into Test and Training Set.mp4 29.8 MB
135 248.0 KB
3. K Means Clustering Algorithm with Python Part 2.mp4 29.7 MB
136 356.3 KB
1. Creating NumPy Array with The Array() Function.mp4 29.5 MB
137 525.2 KB
1. Logistic Regression.mp4 29.3 MB
138 673.5 KB
6. Advanced Aggregation Functions Aggregate() Function.mp4 29.2 MB
139 785.6 KB
5. K Means Clustering Algorithm with Python Part 4.mp4 29.0 MB
140 997.7 KB
3. Hierarchical Clustering Algorithm with Python Part 2.mp4 28.9 MB
141 102.5 KB
1. K Nearest Neighbors Algorithm Theory.mp4 28.7 MB
142 337.9 KB
1. Project Conclusion and Sharing.mp4 28.7 MB
143 354.9 KB
1. Hierarchical Clustering Algorithm Theory.mp4 28.6 MB
144 443.2 KB
5. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 1.mp4 28.4 MB
145 656.2 KB
1. What is Logistic Regression Algorithm in Machine Learning.mp4 27.8 MB
146 164.2 KB
4. K Means Clustering Algorithm with Python Part 3.mp4 27.8 MB
147 246.5 KB
1. What is Machine Learning.mp4 27.6 MB
148 431.5 KB
1. Dropping Columns with Low Correlation.mp4 26.8 MB
149 187.8 KB
1. Indexing Numpy Arrays.mp4 26.6 MB
150 409.6 KB
1. Reshaping a NumPy Array Reshape() Function.mp4 26.2 MB
151 869.1 KB
2. Principal Component Analysis (PCA) with Python Part 1.mp4 26.0 MB
152 1002.7 KB
4. Examining the Properties of Pandas DataFrames.mp4 25.9 MB
153 53.6 KB
5. Decision Tree Algorithm.mp4 25.7 MB
154 307.3 KB
7. Determining Distributions of Numeric Variables.mp4 25.2 MB
155 817.3 KB
2. Element Selection in Multi-Indexed DataFrames.mp4 24.6 MB
156 418.4 KB
6. Support Vector Machine Algorithm.mp4 24.5 MB
157 513.2 KB
7. Advanced Aggregation Functions Filter() Function.mp4 24.5 MB
158 547.0 KB
4. Solving Second-Degree Equations with NumPy.mp4 24.2 MB
159 816.6 KB
3. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 1.mp4 24.1 MB
160 896.7 KB
9. Applying One Hot Encoding Method to Categorical Variables.mp4 24.1 MB
161 932.9 KB
2. Creating NumPy Array with Zeros() Function.mp4 24.1 MB
162 961.1 KB
8. Transformation Operations on Unsymmetrical Data.mp4 24.0 MB
163 2.6 KB
1. What is the Recommender System Part 1.mp4 23.0 MB
164 1001.6 KB
1. Random Forest Algorithm Theory.mp4 22.9 MB
165 112.4 KB
1. Creating Pandas DataFrame with List.mp4 22.6 MB
166 450.4 KB
2. Slicing One-Dimensional Numpy Arrays.mp4 22.3 MB
167 728.0 KB
5. Top Level Element Selection in Pandas DataFramesLesson 3.mp4 22.1 MB
168 941.4 KB
9. Properties of NumPy Array.mp4 22.0 MB
169 1012.2 KB
1. Support Vector Machine Algorithm Theory.mp4 21.8 MB
170 162.2 KB
3. Data Entry with Excel Files.mp4 21.8 MB
171 171.1 KB
1. Operations with Comparison Operators.mp4 21.2 MB
172 854.5 KB
5. Splitting One-Dimensional Numpy Arrays The Split.mp4 20.9 MB
173 91.4 KB
6. Fancy Indexing of One-Dimensional Arrrays.mp4 20.5 MB
174 527.8 KB
1. Classification vs Regression in Machine Learning.mp4 19.9 MB
175 96.5 KB
5. Outputting as an Excel File.mp4 19.8 MB
176 245.8 KB
2. Numeric Variables (Analysis with Distplot) Lesson 2.mp4 19.7 MB
177 276.2 KB
4. Object Types in Series.mp4 19.5 MB
178 461.5 KB
5. Examining the Primary Features of the Pandas Seri.mp4 18.9 MB
179 67.0 KB
2. Creating a Pandas Series with a Dictionary.mp4 18.3 MB
180 726.8 KB
4. Assigning Value to One-Dimensional Arrays.mp4 18.2 MB
181 815.7 KB
2. What is the Recommender System Part 2.mp4 18.0 MB
182 42.7 KB
1. K-Fold Cross-Validation Theory.mp4 17.5 MB
183 550.7 KB
1. K Means Clustering Algorithm Theory.mp4 17.1 MB
184 878.1 KB
7. Sorting Numpy Arrays Sort() Function.mp4 17.0 MB
185 985.8 KB
1. Unsupervised Learning Overview.mp4 16.9 MB
186 81.9 KB
9. Combining Fancy Index with Normal Slicing.mp4 16.5 MB
187 555.6 KB
3. Creating NumPy Array with Ones() Function.mp4 15.8 MB
188 163.3 KB
3. Separating variables (Numeric or Categorical).mp4 15.8 MB
189 168.4 KB
3. Creating Pandas DataFrame with Dictionary.mp4 15.8 MB
190 179.0 KB
2. Removing Rows and Columns from Pandas Data frames.mp4 15.6 MB
191 439.4 KB
2. Identifying the Largest Element of a Numpy Array.mp4 15.1 MB
192 878.1 KB
4. Decision Tree Algorithm with Python Part 3.mp4 14.7 MB
193 296.7 KB
2. Machine Learning Terminology.mp4 14.0 MB
194 985.0 KB
8. Combining Fancy Index with Normal Indexing.mp4 12.6 MB
195 362.3 KB
6. Creating NumPy Array with Eye() Function.mp4 12.6 MB
196 436.6 KB
2. Creating Pandas DataFrame with NumPy Array.mp4 12.1 MB
197 918.6 KB
5. Creating NumPy Array with Arange() Function.mp4 12.1 MB
198 930.5 KB
3. Creating Pandas Series with NumPy Array.mp4 12.0 MB
199 36.8 KB
10. Feature Scaling with the Robust Scaler Method for Machine Learning Algorithms.mp4 11.5 MB
200 554.9 KB
4. Creating NumPy Array with Full() Function.mp4 11.2 MB
201 826.6 KB
3. Detecting Least Element of Numpy Array Min(), Ar.mp4 10.2 MB
202 830.4 KB
2. Loading the Dataset.mp4 10.0 MB
203 13.9 KB
3. Principal Component Analysis (PCA) with Python Part 2.mp4 8.4 MB
204 584.6 KB
7. Creating NumPy Array with Linspace() Function.mp4 7.3 MB

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