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[] Udemy - Complete Data Science Training with Python for Data Analysis

  • 收录时间:2020-02-15 09:53:52
  • 文件大小:2GB
  • 下载次数:26
  • 最近下载:2020-12-12 18:12:26
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文件列表

  1. 1. Introduction to the Data Science in Python Bootcamp/3.1 scriptsLecture.zip.zip 308MB
  2. 13. Miscellaneous Lectures & Information/5. Data Imputation.mp4 56MB
  3. 6. Introduction to Data Visualizations/6. Barplot.mp4 54MB
  4. 4. Introduction to Pandas/6. Read in HTML Data.mp4 51MB
  5. 1. Introduction to the Data Science in Python Bootcamp/6. Introduction to the Python Data Science Environment.mp4 40MB
  6. 6. Introduction to Data Visualizations/8. Line Chart.mp4 37MB
  7. 3. Introduction to Numpy/3. Numpy Operations.mp4 37MB
  8. 8. Statistical Inference & Relationship Between Variables/9. Conditions of Linear Regression-Check in Python.mp4 33MB
  9. 7. Statistical Data Analysis-Basic/5. Grouping & Summarizing Data by Categories.mp4 33MB
  10. 8. Statistical Inference & Relationship Between Variables/7. Linear Regression-Implementation in Python.mp4 30MB
  11. 6. Introduction to Data Visualizations/5. Scatter Plot-Visualize the Relationship Between 2 Continuous Variables.mp4 30MB
  12. 1. Introduction to the Data Science in Python Bootcamp/2. Introduction to the Course & Instructor.mp4 30MB
  13. 6. Introduction to Data Visualizations/3. Histograms-Visualize the Distribution of Continuous Numerical Variables.mp4 29MB
  14. 10. Unsupervised Learning in Python/8. Hierarchical Clustering-practical.mp4 29MB
  15. 5. Data Pre-ProcessingWrangling/12. Merging and Joining Data Frames.mp4 29MB
  16. 8. Statistical Inference & Relationship Between Variables/12. Logistic Regression.mp4 29MB
  17. 11. Supervised Learning/5. RF-Classification.mp4 28MB
  18. 11. Supervised Learning/2. Data Preparation for Supervised Learning.mp4 28MB
  19. 8. Statistical Inference & Relationship Between Variables/3. Test the Difference Between More Than Two Groups.mp4 28MB
  20. 5. Data Pre-ProcessingWrangling/5. Subset and Index Data.mp4 28MB
  21. 5. Data Pre-ProcessingWrangling/6. Basic Data Grouping Based on Qualitative Attributes.mp4 27MB
  22. 7. Statistical Data Analysis-Basic/1. What is Statistical Data Analysis.mp4 25MB
  23. 4. Introduction to Pandas/1. Data Structures in Python.mp4 25MB
  24. 1. Introduction to the Data Science in Python Bootcamp/4. Introduction to the Python Data Science Tool.mp4 25MB
  25. 11. Supervised Learning/1. What is This Section About.mp4 25MB
  26. 8. Statistical Inference & Relationship Between Variables/6. Linear Regression-Theory.mp4 25MB
  27. 5. Data Pre-ProcessingWrangling/10. Rank and Sort Data.mp4 24MB
  28. 5. Data Pre-ProcessingWrangling/8. Reshaping.mp4 24MB
  29. 5. Data Pre-ProcessingWrangling/9. Pivoting.mp4 24MB
  30. 11. Supervised Learning/3. Pointers on Evaluating the Accuracy of Classification and Regression Modelling.mp4 24MB
  31. 5. Data Pre-ProcessingWrangling/11. Concatenate.mp4 24MB
  32. 11. Supervised Learning/6. RF-Regression.mp4 24MB
  33. 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/1. Theory Behind ANN and DNN.mp4 23MB
  34. 3. Introduction to Numpy/2. Create Numpy Arrays.mp4 21MB
  35. 7. Statistical Data Analysis-Basic/2. Some Pointers on Collecting Data for Statistical Studies.mp4 21MB
  36. 8. Statistical Inference & Relationship Between Variables/5. Correlation Analysis.mp4 21MB
  37. 6. Introduction to Data Visualizations/1. What is Data Visualization.mp4 21MB
  38. 11. Supervised Learning/4. Using Logistic Regression as a Classification Model.mp4 21MB
  39. 10. Unsupervised Learning in Python/3. KMeans-implementation on the iris data.mp4 20MB
  40. 5. Data Pre-ProcessingWrangling/2. Removing NAsNo Values From Our Data.mp4 19MB
  41. 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/6. MLP with PCA on a Large Dataset.mp4 19MB
  42. 10. Unsupervised Learning in Python/6. How Do We Select the Number of Clusters.mp4 19MB
  43. 4. Introduction to Pandas/5. Reading in JSON Data.mp4 19MB
  44. 11. Supervised Learning/10. knn-Classification.mp4 18MB
  45. 8. Statistical Inference & Relationship Between Variables/2. Test the Difference Between Two Groups.mp4 18MB
  46. 7. Statistical Data Analysis-Basic/4. Explore the Quantitative Data Descriptive Statistics.mp4 17MB
  47. 6. Introduction to Data Visualizations/2. Some Theoretical Principles Behind Data Visualization.mp4 17MB
  48. 7. Statistical Data Analysis-Basic/9. Check for Normal Distribution.mp4 16MB
  49. 3. Introduction to Numpy/4. Matrix Arithmetic and Linear Systems.mp4 16MB
  50. 9. Machine Learning for Data Science/2. What is Machine Learning (ML) About Some Theoretical Pointers.mp4 16MB
  51. 5. Data Pre-ProcessingWrangling/4. Drop ColumnRow.mp4 16MB
  52. 4. Introduction to Pandas/3. Read in CSV Data Using Pandas.mp4 15MB
  53. 11. Supervised Learning/12. Gradient Boosting-classification.mp4 15MB
  54. 3. Introduction to Numpy/9. Numpy for Statistical Operation.mp4 15MB
  55. 5. Data Pre-ProcessingWrangling/3. Basic Data Handling Starting with Conditional Data Selection.mp4 15MB
  56. 3. Introduction to Numpy/6. Numpy for Basic Matrix Arithmetic.mp4 14MB
  57. 7. Statistical Data Analysis-Basic/11. Confidence Interval-Theory.mp4 14MB
  58. 9. Machine Learning for Data Science/1. How is Machine Learning Different from Statistical Data Analysis.mp4 14MB
  59. 7. Statistical Data Analysis-Basic/12. Confidence Interval-Calculation.mp4 14MB
  60. 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/4. Multi-label classification with MLP.mp4 13MB
  61. 6. Introduction to Data Visualizations/4. Boxplots-Visualize the Distribution of Continuous Numerical Variables.mp4 13MB
  62. 8. Statistical Inference & Relationship Between Variables/1. What is Hypothesis Testing.mp4 13MB
  63. 6. Introduction to Data Visualizations/7. Pie Chart.mp4 13MB
  64. 13. Miscellaneous Lectures & Information/3. Read Data from a Database.mp4 12MB
  65. 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/8. Start with H20.mp4 12MB
  66. 10. Unsupervised Learning in Python/5. KMeans Clustering with Real Data.mp4 12MB
  67. 1. Introduction to the Data Science in Python Bootcamp/7. Some Miscellaneous IPython Usage Facts.mp4 12MB
  68. 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/11. H2O Deep Learning For Predictions.mp4 12MB
  69. 8. Statistical Inference & Relationship Between Variables/11. GLM Generalized Linear Model.mp4 12MB
  70. 3. Introduction to Numpy/5. Numpy for Basic Vector Arithmetric.mp4 12MB
  71. 7. Statistical Data Analysis-Basic/7. Common Terms Relating to Descriptive Statistics.mp4 12MB
  72. 7. Statistical Data Analysis-Basic/6. Visualize Descriptive Statistics-Boxplots.mp4 11MB
  73. 3. Introduction to Numpy/8. Solve Equations with Numpy.mp4 11MB
  74. 4. Introduction to Pandas/4. Read in Excel Data Using Pandas.mp4 11MB
  75. 11. Supervised Learning/13. Gradient Boosting-regression.mp4 11MB
  76. 5. Data Pre-ProcessingWrangling/7. Crosstabulation.mp4 11MB
  77. 10. Unsupervised Learning in Python/7. Hierarchical Clustering-theory.mp4 10MB
  78. 1. Introduction to the Data Science in Python Bootcamp/5. For Mac Users.mp4 10MB
  79. 11. Supervised Learning/9. Support Vector Regression.mp4 10MB
  80. 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/2. Perceptrons for Binary Classification.mp4 10MB
  81. 13. Miscellaneous Lectures & Information/4. Naive Bayes Classification.mp4 10MB
  82. 7. Statistical Data Analysis-Basic/10. Standard Normal Distribution and Z-scores.mp4 10MB
  83. 7. Statistical Data Analysis-Basic/8. Data Distribution- Normal Distribution.mp4 10MB
  84. 10. Unsupervised Learning in Python/4. Quantifying KMeans Clustering Performance.mp4 10MB
  85. 11. Supervised Learning/14. Voting Classifier.mp4 10MB
  86. 8. Statistical Inference & Relationship Between Variables/4. Explore the Relationship Between Two Quantitative Variables.mp4 9MB
  87. 2. Introduction to Python Pre-Requisites for Data Science/2. Different Types of Data Used in Statistical & ML Analysis.mp4 9MB
  88. 8. Statistical Inference & Relationship Between Variables/10. Polynomial Regression.mp4 9MB
  89. 10. Unsupervised Learning in Python/10. Principal Component Analysis (PCA)-Practical Implementation.mp4 9MB
  90. 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/5. Regression with MLP.mp4 9MB
  91. 3. Introduction to Numpy/7. Broadcasting with Numpy.mp4 9MB
  92. 3. Introduction to Numpy/1. Numpy Introduction.mp4 9MB
  93. 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/3. Getting Started with ANN-binary classification.mp4 8MB
  94. 11. Supervised Learning/11. knn-Regression.mp4 8MB
  95. 1. Introduction to the Data Science in Python Bootcamp/1. What is Data Science.mp4 8MB
  96. 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/9. Default H2O Deep Learning Algorithm.mp4 8MB
  97. 5. Data Pre-ProcessingWrangling/1. Rationale behind this section.mp4 8MB
  98. 2. Introduction to Python Pre-Requisites for Data Science/4. Python Data Science Packages To Be Used.mp4 8MB
  99. 2. Introduction to Python Pre-Requisites for Data Science/3. Different Types of Data Used Programatically.mp4 8MB
  100. 1. Introduction to the Data Science in Python Bootcamp/8. Online iPython Interpreter.mp4 8MB
  101. 11. Supervised Learning/7. SVM- Linear Classification.mp4 7MB
  102. 11. Supervised Learning/15. Conclusions to Section 11.mp4 7MB
  103. 13. Miscellaneous Lectures & Information/2. Read in Data from Online CSV.mp4 7MB
  104. 1. Introduction to the Data Science in Python Bootcamp/9. Conclusion to Section 1.mp4 6MB
  105. 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/10. Specify the Activation Function.mp4 6MB
  106. 10. Unsupervised Learning in Python/1. Unsupervised Classification- Some Basic Ideas.mp4 6MB
  107. 3. Introduction to Numpy/10. Conclusion to Section 3.mp4 6MB
  108. 10. Unsupervised Learning in Python/9. Principal Component Analysis (PCA)-Theory.mp4 6MB
  109. 6. Introduction to Data Visualizations/9. Conclusions to Section 6.mp4 6MB
  110. 10. Unsupervised Learning in Python/11. Conclusions to Section 10.mp4 5MB
  111. 4. Introduction to Pandas/7. Conclusion to Section 4.mp4 5MB
  112. 5. Data Pre-ProcessingWrangling/13. Conclusion to Section 5.mp4 5MB
  113. 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/12. Conclusions to Section 12.mp4 5MB
  114. 10. Unsupervised Learning in Python/2. KMeans-theory.mp4 5MB
  115. 11. Supervised Learning/8. SVM- Non Linear Classification.mp4 5MB
  116. 8. Statistical Inference & Relationship Between Variables/13. Conclusions to Section 8.mp4 5MB
  117. 2. Introduction to Python Pre-Requisites for Data Science/5. Conclusions to Section 2.mp4 5MB
  118. 7. Statistical Data Analysis-Basic/13. Conclusions to Section 7.mp4 4MB
  119. 8. Statistical Inference & Relationship Between Variables/8. Conditions of Linear Regression.mp4 3MB
  120. 6. Introduction to Data Visualizations/6. Barplot.vtt 22KB
  121. 1. Introduction to the Data Science in Python Bootcamp/6. Introduction to the Python Data Science Environment.vtt 17KB
  122. 3. Introduction to Numpy/3. Numpy Operations.vtt 15KB
  123. 1. Introduction to the Data Science in Python Bootcamp/2. Introduction to the Course & Instructor.vtt 13KB
  124. 8. Statistical Inference & Relationship Between Variables/9. Conditions of Linear Regression-Check in Python.vtt 13KB
  125. 11. Supervised Learning/5. RF-Classification.vtt 12KB
  126. 6. Introduction to Data Visualizations/5. Scatter Plot-Visualize the Relationship Between 2 Continuous Variables.vtt 12KB
  127. 6. Introduction to Data Visualizations/8. Line Chart.vtt 12KB
  128. 6. Introduction to Data Visualizations/3. Histograms-Visualize the Distribution of Continuous Numerical Variables.vtt 12KB
  129. 8. Statistical Inference & Relationship Between Variables/7. Linear Regression-Implementation in Python.vtt 12KB
  130. 11. Supervised Learning/1. What is This Section About.vtt 11KB
  131. 4. Introduction to Pandas/6. Read in HTML Data.vtt 11KB
  132. 8. Statistical Inference & Relationship Between Variables/12. Logistic Regression.vtt 11KB
  133. 8. Statistical Inference & Relationship Between Variables/3. Test the Difference Between More Than Two Groups.vtt 11KB
  134. 5. Data Pre-ProcessingWrangling/12. Merging and Joining Data Frames.vtt 11KB
  135. 11. Supervised Learning/3. Pointers on Evaluating the Accuracy of Classification and Regression Modelling.vtt 10KB
  136. 7. Statistical Data Analysis-Basic/5. Grouping & Summarizing Data by Categories.vtt 10KB
  137. 1. Introduction to the Data Science in Python Bootcamp/4. Introduction to the Python Data Science Tool.vtt 10KB
  138. 11. Supervised Learning/2. Data Preparation for Supervised Learning.vtt 10KB
  139. 4. Introduction to Pandas/1. Data Structures in Python.vtt 10KB
  140. 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/1. Theory Behind ANN and DNN.vtt 10KB
  141. 8. Statistical Inference & Relationship Between Variables/6. Linear Regression-Theory.vtt 10KB
  142. 6. Introduction to Data Visualizations/1. What is Data Visualization.vtt 10KB
  143. 11. Supervised Learning/6. RF-Regression.vtt 10KB
  144. 5. Data Pre-ProcessingWrangling/8. Reshaping.vtt 10KB
  145. 7. Statistical Data Analysis-Basic/1. What is Statistical Data Analysis.vtt 10KB
  146. 10. Unsupervised Learning in Python/8. Hierarchical Clustering-practical.vtt 10KB
  147. 7. Statistical Data Analysis-Basic/2. Some Pointers on Collecting Data for Statistical Studies.vtt 9KB
  148. 13. Miscellaneous Lectures & Information/5. Data Imputation.vtt 9KB
  149. 11. Supervised Learning/4. Using Logistic Regression as a Classification Model.vtt 9KB
  150. 8. Statistical Inference & Relationship Between Variables/5. Correlation Analysis.vtt 9KB
  151. 5. Data Pre-ProcessingWrangling/9. Pivoting.vtt 8KB
  152. 5. Data Pre-ProcessingWrangling/6. Basic Data Grouping Based on Qualitative Attributes.vtt 8KB
  153. 11. Supervised Learning/10. knn-Classification.vtt 8KB
  154. 5. Data Pre-ProcessingWrangling/11. Concatenate.vtt 8KB
  155. 13. Miscellaneous Lectures & Information/3. Read Data from a Database.vtt 8KB
  156. 5. Data Pre-ProcessingWrangling/5. Subset and Index Data.vtt 8KB
  157. 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/6. MLP with PCA on a Large Dataset.vtt 8KB
  158. 7. Statistical Data Analysis-Basic/4. Explore the Quantitative Data Descriptive Statistics.vtt 8KB
  159. 10. Unsupervised Learning in Python/3. KMeans-implementation on the iris data.vtt 8KB
  160. 8. Statistical Inference & Relationship Between Variables/2. Test the Difference Between Two Groups.vtt 7KB
  161. 5. Data Pre-ProcessingWrangling/10. Rank and Sort Data.vtt 7KB
  162. 6. Introduction to Data Visualizations/2. Some Theoretical Principles Behind Data Visualization.vtt 7KB
  163. 13. Miscellaneous Lectures & Information/4. Naive Bayes Classification.vtt 7KB
  164. 3. Introduction to Numpy/9. Numpy for Statistical Operation.vtt 7KB
  165. 9. Machine Learning for Data Science/2. What is Machine Learning (ML) About Some Theoretical Pointers.vtt 7KB
  166. 3. Introduction to Numpy/4. Matrix Arithmetic and Linear Systems.vtt 6KB
  167. 5. Data Pre-ProcessingWrangling/2. Removing NAsNo Values From Our Data.vtt 6KB
  168. 9. Machine Learning for Data Science/1. How is Machine Learning Different from Statistical Data Analysis.vtt 6KB
  169. 11. Supervised Learning/12. Gradient Boosting-classification.vtt 6KB
  170. 3. Introduction to Numpy/2. Create Numpy Arrays.vtt 6KB
  171. 7. Statistical Data Analysis-Basic/11. Confidence Interval-Theory.vtt 6KB
  172. 8. Statistical Inference & Relationship Between Variables/1. What is Hypothesis Testing.vtt 6KB
  173. 4. Introduction to Pandas/3. Read in CSV Data Using Pandas.vtt 6KB
  174. 7. Statistical Data Analysis-Basic/12. Confidence Interval-Calculation.vtt 6KB
  175. 7. Statistical Data Analysis-Basic/9. Check for Normal Distribution.vtt 6KB
  176. 6. Introduction to Data Visualizations/7. Pie Chart.vtt 6KB
  177. 7. Statistical Data Analysis-Basic/7. Common Terms Relating to Descriptive Statistics.vtt 6KB
  178. 6. Introduction to Data Visualizations/4. Boxplots-Visualize the Distribution of Continuous Numerical Variables.vtt 5KB
  179. 7. Statistical Data Analysis-Basic/6. Visualize Descriptive Statistics-Boxplots.vtt 5KB
  180. 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/11. H2O Deep Learning For Predictions.vtt 5KB
  181. 8. Statistical Inference & Relationship Between Variables/11. GLM Generalized Linear Model.vtt 5KB
  182. 3. Introduction to Numpy/6. Numpy for Basic Matrix Arithmetic.vtt 5KB
  183. 10. Unsupervised Learning in Python/7. Hierarchical Clustering-theory.vtt 5KB
  184. 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/4. Multi-label classification with MLP.vtt 5KB
  185. 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/2. Perceptrons for Binary Classification.vtt 5KB
  186. 5. Data Pre-ProcessingWrangling/1. Rationale behind this section.vtt 5KB
  187. 1. Introduction to the Data Science in Python Bootcamp/7. Some Miscellaneous IPython Usage Facts.vtt 5KB
  188. 10. Unsupervised Learning in Python/5. KMeans Clustering with Real Data.vtt 4KB
  189. 8. Statistical Inference & Relationship Between Variables/4. Explore the Relationship Between Two Quantitative Variables.vtt 4KB
  190. 10. Unsupervised Learning in Python/4. Quantifying KMeans Clustering Performance.vtt 4KB
  191. 5. Data Pre-ProcessingWrangling/4. Drop ColumnRow.vtt 4KB
  192. 11. Supervised Learning/9. Support Vector Regression.vtt 4KB
  193. 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/8. Start with H20.vtt 4KB
  194. 10. Unsupervised Learning in Python/6. How Do We Select the Number of Clusters.vtt 4KB
  195. 7. Statistical Data Analysis-Basic/10. Standard Normal Distribution and Z-scores.vtt 4KB
  196. 3. Introduction to Numpy/8. Solve Equations with Numpy.vtt 4KB
  197. 10. Unsupervised Learning in Python/10. Principal Component Analysis (PCA)-Practical Implementation.vtt 4KB
  198. 5. Data Pre-ProcessingWrangling/3. Basic Data Handling Starting with Conditional Data Selection.vtt 4KB
  199. 1. Introduction to the Data Science in Python Bootcamp/1. What is Data Science.vtt 4KB
  200. 11. Supervised Learning/11. knn-Regression.vtt 4KB
  201. 7. Statistical Data Analysis-Basic/8. Data Distribution- Normal Distribution.vtt 4KB
  202. 1. Introduction to the Data Science in Python Bootcamp/5. For Mac Users.vtt 4KB
  203. 13. Miscellaneous Lectures & Information/2. Read in Data from Online CSV.vtt 4KB
  204. 5. Data Pre-ProcessingWrangling/7. Crosstabulation.vtt 4KB
  205. 3. Introduction to Numpy/1. Numpy Introduction.vtt 4KB
  206. 2. Introduction to Python Pre-Requisites for Data Science/4. Python Data Science Packages To Be Used.vtt 4KB
  207. 3. Introduction to Numpy/5. Numpy for Basic Vector Arithmetric.vtt 4KB
  208. 3. Introduction to Numpy/7. Broadcasting with Numpy.vtt 4KB
  209. 4. Introduction to Pandas/4. Read in Excel Data Using Pandas.vtt 4KB
  210. 11. Supervised Learning/14. Voting Classifier.vtt 4KB
  211. 8. Statistical Inference & Relationship Between Variables/10. Polynomial Regression.vtt 4KB
  212. 11. Supervised Learning/13. Gradient Boosting-regression.vtt 4KB
  213. 2. Introduction to Python Pre-Requisites for Data Science/2. Different Types of Data Used in Statistical & ML Analysis.vtt 4KB
  214. 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/5. Regression with MLP.vtt 3KB
  215. 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/3. Getting Started with ANN-binary classification.vtt 3KB
  216. 1. Introduction to the Data Science in Python Bootcamp/8. Online iPython Interpreter.vtt 3KB
  217. 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/9. Default H2O Deep Learning Algorithm.vtt 3KB
  218. 11. Supervised Learning/7. SVM- Linear Classification.vtt 3KB
  219. 4. Introduction to Pandas/5. Reading in JSON Data.vtt 3KB
  220. 1. Introduction to the Data Science in Python Bootcamp/9. Conclusion to Section 1.vtt 3KB
  221. 2. Introduction to Python Pre-Requisites for Data Science/3. Different Types of Data Used Programatically.vtt 3KB
  222. 10. Unsupervised Learning in Python/9. Principal Component Analysis (PCA)-Theory.vtt 3KB
  223. 11. Supervised Learning/15. Conclusions to Section 11.vtt 3KB
  224. 3. Introduction to Numpy/10. Conclusion to Section 3.vtt 3KB
  225. 10. Unsupervised Learning in Python/2. KMeans-theory.vtt 3KB
  226. 10. Unsupervised Learning in Python/11. Conclusions to Section 10.vtt 2KB
  227. 2. Introduction to Python Pre-Requisites for Data Science/5. Conclusions to Section 2.vtt 2KB
  228. 11. Supervised Learning/8. SVM- Non Linear Classification.vtt 2KB
  229. 4. Introduction to Pandas/7. Conclusion to Section 4.vtt 2KB
  230. 6. Introduction to Data Visualizations/9. Conclusions to Section 6.vtt 2KB
  231. 5. Data Pre-ProcessingWrangling/13. Conclusion to Section 5.vtt 2KB
  232. 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/10. Specify the Activation Function.vtt 2KB
  233. 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/12. Conclusions to Section 12.vtt 2KB
  234. 8. Statistical Inference & Relationship Between Variables/13. Conclusions to Section 8.vtt 2KB
  235. 8. Statistical Inference & Relationship Between Variables/8. Conditions of Linear Regression.vtt 2KB
  236. 10. Unsupervised Learning in Python/1. Unsupervised Classification- Some Basic Ideas.vtt 2KB
  237. 7. Statistical Data Analysis-Basic/13. Conclusions to Section 7.vtt 2KB
  238. 7. Statistical Data Analysis-Basic/3. Some Pointers on Exploring Quantitative Data.html 517B
  239. 2. Introduction to Python Pre-Requisites for Data Science/1. Rationale Behind This Section.html 429B
  240. 4. Introduction to Pandas/2. Read in Data.html 246B
  241. 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/7. Start With Deep Neural Network (DNN).html 229B
  242. 11. Supervised Learning/16. Section 11 Quiz.html 163B
  243. 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/13. Section 12 Quiz.html 163B
  244. 3. Introduction to Numpy/11. Section 3 Quiz.html 163B
  245. 8. Statistical Inference & Relationship Between Variables/14. Section 8 Quiz.html 163B
  246. 13. Miscellaneous Lectures & Information/1. Data For This Section.html 137B
  247. [Tutorialsplanet.NET].url 128B
  248. 1. Introduction to the Data Science in Python Bootcamp/3. Data For the Course.html 98B