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

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Tutorialsplanet NET Udemy - Complete Data Science Training with Python for Data Analysis

文件列表
  1. 1. Introduction to the Data Science in Python Bootcamp/1. What is Data Science.mp48.35MB
  2. 1. Introduction to the Data Science in Python Bootcamp/2. Introduction to the Course & Instructor.mp429.62MB
  3. 1. Introduction to the Data Science in Python Bootcamp/4. Introduction to the Python Data Science Tool.mp425.02MB
  4. 1. Introduction to the Data Science in Python Bootcamp/5. For Mac Users.mp410.22MB
  5. 1. Introduction to the Data Science in Python Bootcamp/6. Introduction to the Python Data Science Environment.mp440.32MB
  6. 1. Introduction to the Data Science in Python Bootcamp/7. Some Miscellaneous IPython Usage Facts.mp412.01MB
  7. 1. Introduction to the Data Science in Python Bootcamp/8. Online iPython Interpreter.mp47.73MB
  8. 1. Introduction to the Data Science in Python Bootcamp/9. Conclusion to Section 1.mp46.48MB
  9. 10. Unsupervised Learning in Python/1. Unsupervised Classification- Some Basic Ideas.mp46.17MB
  10. 10. Unsupervised Learning in Python/10. Principal Component Analysis (PCA)-Practical Implementation.mp49.06MB
  11. 10. Unsupervised Learning in Python/11. Conclusions to Section 10.mp45.49MB
  12. 10. Unsupervised Learning in Python/2. KMeans-theory.mp45.15MB
  13. 10. Unsupervised Learning in Python/3. KMeans-implementation on the iris data.mp419.54MB
  14. 10. Unsupervised Learning in Python/4. Quantifying KMeans Clustering Performance.mp49.57MB
  15. 10. Unsupervised Learning in Python/5. KMeans Clustering with Real Data.mp412.08MB
  16. 10. Unsupervised Learning in Python/6. How Do We Select the Number of Clusters.mp419.04MB
  17. 10. Unsupervised Learning in Python/7. Hierarchical Clustering-theory.mp410.23MB
  18. 10. Unsupervised Learning in Python/8. Hierarchical Clustering-practical.mp429.39MB
  19. 10. Unsupervised Learning in Python/9. Principal Component Analysis (PCA)-Theory.mp45.91MB
  20. 11. Supervised Learning/1. What is This Section About.mp424.88MB
  21. 11. Supervised Learning/10. knn-Classification.mp418.2MB
  22. 11. Supervised Learning/11. knn-Regression.mp48.38MB
  23. 11. Supervised Learning/12. Gradient Boosting-classification.mp415.04MB
  24. 11. Supervised Learning/13. Gradient Boosting-regression.mp410.9MB
  25. 11. Supervised Learning/14. Voting Classifier.mp49.53MB
  26. 11. Supervised Learning/15. Conclusions to Section 11.mp47.23MB
  27. 11. Supervised Learning/2. Data Preparation for Supervised Learning.mp428.28MB
  28. 11. Supervised Learning/3. Pointers on Evaluating the Accuracy of Classification and Regression Modelling.mp424MB
  29. 11. Supervised Learning/4. Using Logistic Regression as a Classification Model.mp420.64MB
  30. 11. Supervised Learning/5. RF-Classification.mp428.48MB
  31. 11. Supervised Learning/6. RF-Regression.mp423.63MB
  32. 11. Supervised Learning/7. SVM- Linear Classification.mp47.39MB
  33. 11. Supervised Learning/8. SVM- Non Linear Classification.mp45.12MB
  34. 11. Supervised Learning/9. Support Vector Regression.mp410.19MB
  35. 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/1. Theory Behind ANN and DNN.mp422.56MB
  36. 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/10. Specify the Activation Function.mp46.21MB
  37. 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/11. H2O Deep Learning For Predictions.mp412MB
  38. 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/12. Conclusions to Section 12.mp45.16MB
  39. 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/2. Perceptrons for Binary Classification.mp410.05MB
  40. 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/3. Getting Started with ANN-binary classification.mp48.46MB
  41. 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/4. Multi-label classification with MLP.mp413.49MB
  42. 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/5. Regression with MLP.mp49.02MB
  43. 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/6. MLP with PCA on a Large Dataset.mp419.25MB
  44. 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/8. Start with H20.mp412.12MB
  45. 12. Artificial Neural Networks (ANN) and Deep Learning (DL)/9. Default H2O Deep Learning Algorithm.mp48.23MB
  46. 13. Miscellaneous Lectures & Information/2. Read in Data from Online CSV.mp46.66MB
  47. 13. Miscellaneous Lectures & Information/3. Read Data from a Database.mp412.26MB
  48. 13. Miscellaneous Lectures & Information/4. Naive Bayes Classification.mp49.86MB
  49. 13. Miscellaneous Lectures & Information/5. Data Imputation.mp456.41MB
  50. 2. Introduction to Python Pre-Requisites for Data Science/2. Different Types of Data Used in Statistical & ML Analysis.mp49.36MB
  51. 2. Introduction to Python Pre-Requisites for Data Science/3. Different Types of Data Used Programatically.mp47.74MB
  52. 2. Introduction to Python Pre-Requisites for Data Science/4. Python Data Science Packages To Be Used.mp47.93MB
  53. 2. Introduction to Python Pre-Requisites for Data Science/5. Conclusions to Section 2.mp44.88MB
  54. 3. Introduction to Numpy/1. Numpy Introduction.mp48.7MB
  55. 3. Introduction to Numpy/10. Conclusion to Section 3.mp46.17MB
  56. 3. Introduction to Numpy/2. Create Numpy Arrays.mp420.91MB
  57. 3. Introduction to Numpy/3. Numpy Operations.mp436.71MB
  58. 3. Introduction to Numpy/4. Matrix Arithmetic and Linear Systems.mp415.83MB
  59. 3. Introduction to Numpy/5. Numpy for Basic Vector Arithmetric.mp411.75MB
  60. 3. Introduction to Numpy/6. Numpy for Basic Matrix Arithmetic.mp413.89MB
  61. 3. Introduction to Numpy/7. Broadcasting with Numpy.mp48.95MB
  62. 3. Introduction to Numpy/8. Solve Equations with Numpy.mp411.44MB
  63. 3. Introduction to Numpy/9. Numpy for Statistical Operation.mp414.95MB
  64. 4. Introduction to Pandas/1. Data Structures in Python.mp425.07MB
  65. 4. Introduction to Pandas/3. Read in CSV Data Using Pandas.mp415.32MB
  66. 4. Introduction to Pandas/4. Read in Excel Data Using Pandas.mp411.38MB
  67. 4. Introduction to Pandas/5. Reading in JSON Data.mp418.72MB
  68. 4. Introduction to Pandas/6. Read in HTML Data.mp451.31MB
  69. 4. Introduction to Pandas/7. Conclusion to Section 4.mp45.4MB
  70. 5. Data Pre-ProcessingWrangling/1. Rationale behind this section.mp48.11MB
  71. 5. Data Pre-ProcessingWrangling/10. Rank and Sort Data.mp424.32MB
  72. 5. Data Pre-ProcessingWrangling/11. Concatenate.mp423.74MB
  73. 5. Data Pre-ProcessingWrangling/12. Merging and Joining Data Frames.mp428.8MB
  74. 5. Data Pre-ProcessingWrangling/13. Conclusion to Section 5.mp45.39MB
  75. 5. Data Pre-ProcessingWrangling/2. Removing NAsNo Values From Our Data.mp419.29MB
  76. 5. Data Pre-ProcessingWrangling/3. Basic Data Handling Starting with Conditional Data Selection.mp414.85MB
  77. 5. Data Pre-ProcessingWrangling/4. Drop ColumnRow.mp415.7MB
  78. 5. Data Pre-ProcessingWrangling/5. Subset and Index Data.mp428MB
  79. 5. Data Pre-ProcessingWrangling/6. Basic Data Grouping Based on Qualitative Attributes.mp426.62MB
  80. 5. Data Pre-ProcessingWrangling/7. Crosstabulation.mp410.88MB
  81. 5. Data Pre-ProcessingWrangling/8. Reshaping.mp424.27MB
  82. 5. Data Pre-ProcessingWrangling/9. Pivoting.mp424.04MB
  83. 6. Introduction to Data Visualizations/1. What is Data Visualization.mp420.72MB
  84. 6. Introduction to Data Visualizations/2. Some Theoretical Principles Behind Data Visualization.mp416.56MB
  85. 6. Introduction to Data Visualizations/3. Histograms-Visualize the Distribution of Continuous Numerical Variables.mp429.41MB
  86. 6. Introduction to Data Visualizations/4. Boxplots-Visualize the Distribution of Continuous Numerical Variables.mp413.44MB
  87. 6. Introduction to Data Visualizations/5. Scatter Plot-Visualize the Relationship Between 2 Continuous Variables.mp429.82MB
  88. 6. Introduction to Data Visualizations/6. Barplot.mp453.81MB
  89. 6. Introduction to Data Visualizations/7. Pie Chart.mp412.8MB
  90. 6. Introduction to Data Visualizations/8. Line Chart.mp437.09MB
  91. 6. Introduction to Data Visualizations/9. Conclusions to Section 6.mp45.83MB
  92. 7. Statistical Data Analysis-Basic/1. What is Statistical Data Analysis.mp425.29MB
  93. 7. Statistical Data Analysis-Basic/10. Standard Normal Distribution and Z-scores.mp49.81MB
  94. 7. Statistical Data Analysis-Basic/11. Confidence Interval-Theory.mp413.72MB
  95. 7. Statistical Data Analysis-Basic/12. Confidence Interval-Calculation.mp413.65MB
  96. 7. Statistical Data Analysis-Basic/13. Conclusions to Section 7.mp43.82MB
  97. 7. Statistical Data Analysis-Basic/2. Some Pointers on Collecting Data for Statistical Studies.mp420.9MB
  98. 7. Statistical Data Analysis-Basic/4. Explore the Quantitative Data Descriptive Statistics.mp417.39MB
  99. 7. Statistical Data Analysis-Basic/5. Grouping & Summarizing Data by Categories.mp433.05MB
  100. 7. Statistical Data Analysis-Basic/6. Visualize Descriptive Statistics-Boxplots.mp411.5MB
  101. 7. Statistical Data Analysis-Basic/7. Common Terms Relating to Descriptive Statistics.mp411.6MB
  102. 7. Statistical Data Analysis-Basic/8. Data Distribution- Normal Distribution.mp49.6MB
  103. 7. Statistical Data Analysis-Basic/9. Check for Normal Distribution.mp416.47MB
  104. 8. Statistical Inference & Relationship Between Variables/1. What is Hypothesis Testing.mp413.41MB
  105. 8. Statistical Inference & Relationship Between Variables/10. Polynomial Regression.mp49.23MB
  106. 8. Statistical Inference & Relationship Between Variables/11. GLM Generalized Linear Model.mp411.84MB
  107. 8. Statistical Inference & Relationship Between Variables/12. Logistic Regression.mp428.78MB
  108. 8. Statistical Inference & Relationship Between Variables/13. Conclusions to Section 8.mp44.94MB
  109. 8. Statistical Inference & Relationship Between Variables/2. Test the Difference Between Two Groups.mp417.78MB
  110. 8. Statistical Inference & Relationship Between Variables/3. Test the Difference Between More Than Two Groups.mp428.28MB
  111. 8. Statistical Inference & Relationship Between Variables/4. Explore the Relationship Between Two Quantitative Variables.mp49.44MB
  112. 8. Statistical Inference & Relationship Between Variables/5. Correlation Analysis.mp420.73MB
  113. 8. Statistical Inference & Relationship Between Variables/6. Linear Regression-Theory.mp424.87MB
  114. 8. Statistical Inference & Relationship Between Variables/7. Linear Regression-Implementation in Python.mp430.15MB
  115. 8. Statistical Inference & Relationship Between Variables/8. Conditions of Linear Regression.mp42.98MB
  116. 8. Statistical Inference & Relationship Between Variables/9. Conditions of Linear Regression-Check in Python.mp433.36MB
  117. 9. Machine Learning for Data Science/1. How is Machine Learning Different from Statistical Data Analysis.mp413.71MB
  118. 9. Machine Learning for Data Science/2. What is Machine Learning (ML) About Some Theoretical Pointers.mp415.75MB
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