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[] Udemy - Complete Data Science & Machine Learning A-Z with Python

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文件列表

  1. 42. Competition Section on Kaggle/2. Competitions on Kaggle Lesson 2.mp4 192MB
  2. 42. Competition Section on Kaggle/1. Competitions on Kaggle Lesson 1.mp4 188MB
  3. 44. Code Section on Kaggle/3. Examining the Code Section in Kaggle Lesson 3.mp4 160MB
  4. 43. Dataset Section on Kaggle/1. Datasets on Kaggle.mp4 133MB
  5. 41. First Contact with Kaggle/1. What is Kaggle.mp4 130MB
  6. 48. Introduction to Machine Learning with Real Hearth Attack Prediction Project/6. Recognizing Variables In Dataset.mp4 127MB
  7. 41. First Contact with Kaggle/5. Getting to Know the Kaggle Homepage.mp4 123MB
  8. 1. Installations/1. Installing Anaconda Distribution for Windows.mp4 118MB
  9. 48. Introduction to Machine Learning with Real Hearth Attack Prediction Project/1. First Step to the Hearth Attack Prediction Project.mp4 117MB
  10. 1. Installations/5. Installing Anaconda Distribution for Linux.mp4 115MB
  11. 21. Matplotlib/8. Basic Plots in Matplotlib I.mp4 111MB
  12. 46. Other Most Used Options on Kaggle/2. Ranking Among Users on Kaggle.mp4 107MB
  13. 27. Linear Regression Algorithm in Machine Learning A-Z/3. Linear Regression Algorithm With Python Part 2.mp4 107MB
  14. 44. Code Section on Kaggle/2. Examining the Code Section in Kaggle Lesson 2.mp4 106MB
  15. 48. Introduction to Machine Learning with Real Hearth Attack Prediction Project/3. Notebook Design to be Used in the Project.mp4 105MB
  16. 25. Evaluation Metrics in Machine Learning/2. Machine Learning Model Performance Evaluation Classification Error Metrics.mp4 100MB
  17. 22. Seaborn/5. Basic Plots in Seaborn.mp4 99MB
  18. 25. Evaluation Metrics in Machine Learning/4. Machine Learning With Python.mp4 92MB
  19. 50. Preparation For Exploratory Data Analysis (EDA) in Data Science/4. Examining Statistics of Variables.mp4 91MB
  20. 14. Functions That Can Be Applied on a DataFrame/3. Aggregation Functions in Pandas DataFrames.mp4 91MB
  21. 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/14. Relationships between variables (Analysis with Heatmap) Lesson 2.mp4 91MB
  22. 27. Linear Regression Algorithm in Machine Learning A-Z/5. Linear Regression Algorithm With Python Part 4.mp4 90MB
  23. 14. Functions That Can Be Applied on a DataFrame/5. Coordinated Use of Grouping and Aggregation Functions in Pandas Dataframes.mp4 88MB
  24. 51. Exploratory Data Analysis (EDA) - Uni-variate Analysis/4. Categoric Variables (Analysis with Pie Chart) Lesson 2.mp4 84MB
  25. 47. Details on Kaggle/1. User Page Review on Kaggle.mp4 82MB
  26. 29. Logistic Regression Algorithm in Machine Learning A-Z/3. Logistic Regression Algorithm with Python Part 2.mp4 81MB
  27. 23. Geoplotlib/3. Example - 2.mp4 81MB
  28. 51. Exploratory Data Analysis (EDA) - Uni-variate Analysis/1. Numeric Variables (Analysis with Distplot) Lesson 1.mp4 80MB
  29. 44. Code Section on Kaggle/1. Examining the Code Section in Kaggle Lesson 1.mp4 80MB
  30. 48. Introduction to Machine Learning with Real Hearth Attack Prediction Project/5. Examining the Project Topic.mp4 77MB
  31. 27. Linear Regression Algorithm in Machine Learning A-Z/2. Linear Regression Algorithm With Python Part 1.mp4 76MB
  32. 19. Fundamentals of Python 3/5. Lists, Tuples, Dictionaries and Sets in pyhton.mp4 75MB
  33. 51. Exploratory Data Analysis (EDA) - Uni-variate Analysis/3. Categoric Variables (Analysis with Pie Chart) Lesson 1.mp4 75MB
  34. 47. Details on Kaggle/2. Treasure in The Kaggle.mp4 75MB
  35. 29. Logistic Regression Algorithm in Machine Learning A-Z/2. Logistic Regression Algorithm with Python Part 1.mp4 72MB
  36. 6. Operations in Numpy Library/2. Arithmetic Operations in Numpy.mp4 72MB
  37. 27. Linear Regression Algorithm in Machine Learning A-Z/4. Linear Regression Algorithm With Python Part 3.mp4 70MB
  38. 21. Matplotlib/4. Figure, Subplot and Axex.mp4 70MB
  39. 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/10. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 2.mp4 68MB
  40. 11. Structural Operations on Pandas DataFrame/3. Null Values in Pandas Dataframes.mp4 67MB
  41. 16. File Operations in Pandas Library/2. Data Entry with Csv and Txt Files.mp4 64MB
  42. 49. First Organization/3. Initial analysis on the dataset.mp4 64MB
  43. 13. Structural Concatenation Operations in Pandas DataFrame/1. Concatenating Pandas Dataframes Concat Function.mp4 64MB
  44. 49. First Organization/1. Required Python Libraries.mp4 64MB
  45. 21. Matplotlib/5. Figure Customization.mp4 63MB
  46. 20. Object Oriented Programming (OOP)/5. Overriding and Overloading in Object Oriented Programming (OOP).mp4 63MB
  47. 13. Structural Concatenation Operations in Pandas DataFrame/4. Merge Pandas Dataframes Merge() Function Lesson 3.mp4 60MB
  48. 22. Seaborn/7. Regression Plots and Squarify in Seaborn.mp4 60MB
  49. 2. NumPy Library Introduction/2. The Power of NumPy.mp4 60MB
  50. 31. K Nearest Neighbors Algorithm in Machine Learning A-Z/3. K Nearest Neighbors Algorithm with Python Part 2.mp4 59MB
  51. 19. Fundamentals of Python 3/4. Loops in Python.mp4 59MB
  52. 54. Modelling for Machine Learning/4. Hyperparameter Optimization (with GridSearchCV).mp4 59MB
  53. 47. Details on Kaggle/4. What Should Be Done to Achieve Success in Kaggle.mp4 58MB
  54. 13. Structural Concatenation Operations in Pandas DataFrame/2. Merge Pandas Dataframes Merge() Function Lesson 1.mp4 57MB
  55. 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/4. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 2.mp4 56MB
  56. 13. Structural Concatenation Operations in Pandas DataFrame/6. Joining Pandas Dataframes Join() Function.mp4 56MB
  57. 28. Bias Variance Trade-Off in Machine Learning/1. What is Bias Variance Trade-Off.mp4 55MB
  58. 22. Seaborn/3. Example in Seaborn.mp4 55MB
  59. 21. Matplotlib/9. Basic Plots in Matplotlib II.mp4 55MB
  60. 15. Pivot Tables in Pandas Library/2. Pivot Tables in Pandas Library.mp4 54MB
  61. 51. Exploratory Data Analysis (EDA) - Uni-variate Analysis/5. Examining the Missing Data According to the Analysis Result.mp4 54MB
  62. 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/8. Creating a New DataFrame with the Melt() Function.mp4 53MB
  63. 54. Modelling for Machine Learning/8. Hyperparameter Optimization (with GridSearchCV).mp4 53MB
  64. 46. Other Most Used Options on Kaggle/1. Courses in Kaggle.mp4 52MB
  65. 19. Fundamentals of Python 3/10. Exercise - Solution in Python.mp4 52MB
  66. 11. Structural Operations on Pandas DataFrame/5. Filling Null Values Fillna() Function.mp4 52MB
  67. 23. Geoplotlib/4. Example - 3.mp4 51MB
  68. 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/1. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 1.mp4 49MB
  69. 33. Decision Tree Algorithm in Machine Learning A-Z/3. Decision Tree Algorithm with Python Part 2.mp4 49MB
  70. 22. Seaborn/4. Color Palettes in Seaborn.mp4 48MB
  71. 8. Series Structures in the Pandas Library/6. Most Applied Methods on Pandas Series.mp4 48MB
  72. 32. Hyperparameter Optimization/2. Hyperparameter Optimization with Python.mp4 47MB
  73. 29. Logistic Regression Algorithm in Machine Learning A-Z/4. Logistic Regression Algorithm with Python Part 3.mp4 47MB
  74. 29. Logistic Regression Algorithm in Machine Learning A-Z/5. Logistic Regression Algorithm with Python Part 4.mp4 47MB
  75. 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/6. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 2.mp4 47MB
  76. 14. Functions That Can Be Applied on a DataFrame/8. Advanced Aggregation Functions Transform() Function.mp4 47MB
  77. 19. Fundamentals of Python 3/1. Data Types in Python.mp4 47MB
  78. 14. Functions That Can Be Applied on a DataFrame/4. Examining the Data Set 2.mp4 47MB
  79. 10. Element Selection Operations in DataFrame Structures/6. Element Selection with Conditional Operations in.mp4 46MB
  80. 1. Installations/3. Installing Anaconda Distribution for MacOs.mp4 46MB
  81. 50. Preparation For Exploratory Data Analysis (EDA) in Data Science/1. Examining Missing Values.mp4 46MB
  82. 5. Indexing, Slicing, and Assigning NumPy Arrays/7. Fancy Indexing of Two-Dimensional Arrrays.mp4 46MB
  83. 25. Evaluation Metrics in Machine Learning/3. Evaluating Performance Regression Error Metrics in Python.mp4 46MB
  84. 2. NumPy Library Introduction/1. Introduction to NumPy Library.mp4 45MB
  85. 50. Preparation For Exploratory Data Analysis (EDA) in Data Science/2. Examining Unique Values.mp4 45MB
  86. 53. Preparation for Modelling in Machine Learning/4. Dealing with Outliers – Trtbps Variable Lesson 2.mp4 44MB
  87. 19. Fundamentals of Python 3/6. Data Type Operators and Methods in Python.mp4 44MB
  88. 41. First Contact with Kaggle/3. Registering on Kaggle and Member Login Procedures.mp4 43MB
  89. 3. Creating NumPy Array in Python/8. Creating NumPy Array with Random() Function.mp4 43MB
  90. 22. Seaborn/6. Multi-Plots in Seaborn.mp4 43MB
  91. 14. Functions That Can Be Applied on a DataFrame/2. Examining the Data Set 1.mp4 43MB
  92. 53. Preparation for Modelling in Machine Learning/3. Dealing with Outliers – Trtbps Variable Lesson 1.mp4 43MB
  93. 12. Multi-Indexed DataFrame Structures/1. Multi-Index and Index Hierarchy in Pandas DataFrames.mp4 43MB
  94. 33. Decision Tree Algorithm in Machine Learning A-Z/5. Decision Tree Algorithm with Python Part 4.mp4 42MB
  95. 22. Seaborn/2. Controlling Figure Aesthetics in Seaborn.mp4 42MB
  96. 35. Support Vector Machine Algorithm in Machine Learning A-Z/3. Support Vector Machine Algorithm with Python Part 2.mp4 42MB
  97. 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/9. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 1.mp4 42MB
  98. 54. Modelling for Machine Learning/3. Roc Curve and Area Under Curve (AUC).mp4 42MB
  99. 14. Functions That Can Be Applied on a DataFrame/9. Advanced Aggregation Functions Apply() Function.mp4 41MB
  100. 19. Fundamentals of Python 3/3. Conditionals in Python.mp4 41MB
  101. 46. Other Most Used Options on Kaggle/3. Blog and Documentation Sections.mp4 41MB
  102. 13. Structural Concatenation Operations in Pandas DataFrame/5. Merge Pandas Dataframes Merge() Function Lesson 4.mp4 41MB
  103. 45. Discussion Section on Kaggle/1. What is Discussion on Kaggle.mp4 41MB
  104. 11. Structural Operations on Pandas DataFrame/6. Setting Index in Pandas DataFrames.mp4 40MB
  105. 29. Logistic Regression Algorithm in Machine Learning A-Z/6. Logistic Regression Algorithm with Python Part 5.mp4 39MB
  106. 8. Series Structures in the Pandas Library/1. Creating a Pandas Series with a List.mp4 39MB
  107. 15. Pivot Tables in Pandas Library/1. Examining the Data Set 3.mp4 39MB
  108. 23. Geoplotlib/2. Example - 1.mp4 39MB
  109. 34. Random Forest Algorithm in Machine Learning A-Z/3. Random Forest Algorithm with Pyhon Part 2.mp4 39MB
  110. 34. Random Forest Algorithm in Machine Learning A-Z/2. Random Forest Algorithm with Pyhon Part 1.mp4 39MB
  111. 4. Functions in the NumPy Library/4. Concatenating Numpy Arrays Concatenate() Functio.mp4 38MB
  112. 10. Element Selection Operations in DataFrame Structures/3. Top Level Element Selection in Pandas DataFramesLesson 1.mp4 38MB
  113. 47. Details on Kaggle/3. Publishing Notebooks on Kaggle.mp4 38MB
  114. 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/11. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 1.mp4 38MB
  115. 39. Principal Component Analysis (PCA) in Machine Learning A-Z/1. Principal Component Analysis (PCA) Theory.mp4 38MB
  116. 14. Functions That Can Be Applied on a DataFrame/1. Loading a Dataset from the Seaborn Library.mp4 38MB
  117. 35. Support Vector Machine Algorithm in Machine Learning A-Z/5. Support Vector Machine Algorithm with Python Part 4.mp4 38MB
  118. 39. Principal Component Analysis (PCA) in Machine Learning A-Z/4. Principal Component Analysis (PCA) with Python Part 3.mp4 37MB
  119. 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/13. Relationships between variables (Analysis with Heatmap) Lesson 1.mp4 36MB
  120. 53. Preparation for Modelling in Machine Learning/5. Dealing with Outliers – Thalach Variable.mp4 36MB
  121. 53. Preparation for Modelling in Machine Learning/6. Dealing with Outliers – Oldpeak Variable.mp4 36MB
  122. 20. Object Oriented Programming (OOP)/2. Constructor in Object Oriented Programming (OOP).mp4 36MB
  123. 33. Decision Tree Algorithm in Machine Learning A-Z/1. Decision Tree Algorithm Theory.mp4 36MB
  124. 4. Functions in the NumPy Library/6. Splitting Two-Dimensional Numpy Arrays Split(),.mp4 36MB
  125. 19. Fundamentals of Python 3/2. Operators in Python.mp4 36MB
  126. 16. File Operations in Pandas Library/4. Outputting as an CSV Extension.mp4 36MB
  127. 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/2. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 2.mp4 36MB
  128. 35. Support Vector Machine Algorithm in Machine Learning A-Z/2. Support Vector Machine Algorithm with Python Part 1.mp4 36MB
  129. 38. Hierarchical Clustering Algorithm in machine learning data science/2. Hierarchical Clustering Algorithm with Python Part 2.mp4 36MB
  130. 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/12. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 2.mp4 35MB
  131. 5. Indexing, Slicing, and Assigning NumPy Arrays/5. Assigning Value to Two-Dimensional Array.mp4 35MB
  132. 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/7. Feature Scaling with the Robust Scaler Method.mp4 35MB
  133. 31. K Nearest Neighbors Algorithm in Machine Learning A-Z/2. K Nearest Neighbors Algorithm with Python Part 1.mp4 35MB
  134. 53. Preparation for Modelling in Machine Learning/2. Visualizing Outliers.mp4 35MB
  135. 35. Support Vector Machine Algorithm in Machine Learning A-Z/4. Support Vector Machine Algorithm with Python Part 3.mp4 35MB
  136. 30. K-fold Cross-Validation in Machine Learning A-Z/2. K-Fold Cross-Validation with Python.mp4 35MB
  137. 16. File Operations in Pandas Library/1. Accessing and Making Files Available.mp4 35MB
  138. 20. Object Oriented Programming (OOP)/4. Inheritance in Object Oriented Programming (OOP).mp4 35MB
  139. 11. Structural Operations on Pandas DataFrame/4. Dropping Null Values Dropna() Function.mp4 35MB
  140. 5. Indexing, Slicing, and Assigning NumPy Arrays/3. Slicing Two-Dimensional Numpy Arrays.mp4 34MB
  141. 23. Geoplotlib/1. What is Geoplotlib.mp4 34MB
  142. 27. Linear Regression Algorithm in Machine Learning A-Z/1. Linear Regression Algorithm Theory in Machine Learning A-Z.mp4 34MB
  143. 7. Pandas Library Introduction/1. Introduction to Pandas Library.mp4 34MB
  144. 11. Structural Operations on Pandas DataFrame/1. Adding Columns to Pandas Data Frames.mp4 34MB
  145. 32. Hyperparameter Optimization/1. Hyperparameter Optimization Theory.mp4 33MB
  146. 33. Decision Tree Algorithm in Machine Learning A-Z/6. Decision Tree Algorithm with Python Part 5.mp4 33MB
  147. 6. Operations in Numpy Library/3. Statistical Operations in Numpy.mp4 32MB
  148. 10. Element Selection Operations in DataFrame Structures/2. Element Selection Operations in Pandas DataFrames Lesson 2.mp4 32MB
  149. 26. Supervised Learning with Machine Learning/1. What is Supervised Learning in Machine Learning.mp4 32MB
  150. 33. Decision Tree Algorithm in Machine Learning A-Z/2. Decision Tree Algorithm with Python Part 1.mp4 32MB
  151. 10. Element Selection Operations in DataFrame Structures/4. Top Level Element Selection in Pandas DataFramesLesson 2.mp4 31MB
  152. 31. K Nearest Neighbors Algorithm in Machine Learning A-Z/4. K Nearest Neighbors Algorithm with Python Part 3.mp4 31MB
  153. 12. Multi-Indexed DataFrame Structures/3. Selecting Elements Using the xs() Function in Multi-Indexed DataFrames.mp4 31MB
  154. 13. Structural Concatenation Operations in Pandas DataFrame/3. Merge Pandas Dataframes Merge() Function Lesson 2.mp4 31MB
  155. 54. Modelling for Machine Learning/2. Cross Validation.mp4 30MB
  156. 37. K Means Clustering Algorithm in Machine Learning A-Z/2. K Means Clustering Algorithm with Python Part 1.mp4 30MB
  157. 10. Element Selection Operations in DataFrame Structures/1. Element Selection Operations in Pandas DataFrames Lesson 1.mp4 30MB
  158. 8. Series Structures in the Pandas Library/7. Indexing and Slicing Pandas Series.mp4 30MB
  159. 54. Modelling for Machine Learning/7. Random Forest Algorithm.mp4 30MB
  160. 53. Preparation for Modelling in Machine Learning/11. Separating Data into Test and Training Set.mp4 30MB
  161. 37. K Means Clustering Algorithm in Machine Learning A-Z/3. K Means Clustering Algorithm with Python Part 2.mp4 30MB
  162. 3. Creating NumPy Array in Python/1. Creating NumPy Array with The Array() Function.mp4 29MB
  163. 54. Modelling for Machine Learning/1. Logistic Regression.mp4 29MB
  164. 14. Functions That Can Be Applied on a DataFrame/6. Advanced Aggregation Functions Aggregate() Function.mp4 29MB
  165. 37. K Means Clustering Algorithm in Machine Learning A-Z/5. K Means Clustering Algorithm with Python Part 4.mp4 29MB
  166. 19. Fundamentals of Python 3/8. Functions in Python.mp4 29MB
  167. 38. Hierarchical Clustering Algorithm in machine learning data science/3. Hierarchical Clustering Algorithm with Python Part 2.mp4 29MB
  168. 55. Conclusion/1. Project Conclusion and Sharing.mp4 29MB
  169. 31. K Nearest Neighbors Algorithm in Machine Learning A-Z/1. K Nearest Neighbors Algorithm Theory.mp4 29MB
  170. 38. Hierarchical Clustering Algorithm in machine learning data science/1. Hierarchical Clustering Algorithm Theory.mp4 29MB
  171. 21. Matplotlib/3. Pyplot – Pylab - Matplotlib.mp4 28MB
  172. 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/5. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 1.mp4 28MB
  173. 21. Matplotlib/2. Using Pyplot.mp4 28MB
  174. 29. Logistic Regression Algorithm in Machine Learning A-Z/1. What is Logistic Regression Algorithm in Machine Learning.mp4 28MB
  175. 37. K Means Clustering Algorithm in Machine Learning A-Z/4. K Means Clustering Algorithm with Python Part 3.mp4 28MB
  176. 24. First Contact with Machine Learning/1. What is Machine Learning.mp4 28MB
  177. 21. Matplotlib/6. Plot Customization.mp4 27MB
  178. 53. Preparation for Modelling in Machine Learning/1. Dropping Columns with Low Correlation.mp4 27MB
  179. 5. Indexing, Slicing, and Assigning NumPy Arrays/1. Indexing Numpy Arrays,.mp4 27MB
  180. 4. Functions in the NumPy Library/1. Reshaping a NumPy Array Reshape() Function.mp4 26MB
  181. 39. Principal Component Analysis (PCA) in Machine Learning A-Z/2. Principal Component Analysis (PCA) with Python Part 1.mp4 26MB
  182. 9. DataFrame Structures in Pandas Library/4. Examining the Properties of Pandas DataFrames.mp4 26MB
  183. 54. Modelling for Machine Learning/5. Decision Tree Algorithm.mp4 26MB
  184. 53. Preparation for Modelling in Machine Learning/7. Determining Distributions of Numeric Variables.mp4 25MB
  185. 20. Object Oriented Programming (OOP)/3. Methods in Object Oriented Programming (OOP).mp4 25MB
  186. 12. Multi-Indexed DataFrame Structures/2. Element Selection in Multi-Indexed DataFrames.mp4 25MB
  187. 54. Modelling for Machine Learning/6. Support Vector Machine Algorithm.mp4 25MB
  188. 14. Functions That Can Be Applied on a DataFrame/7. Advanced Aggregation Functions Filter() Function.mp4 24MB
  189. 6. Operations in Numpy Library/4. Solving Second-Degree Equations with NumPy.mp4 24MB
  190. 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/3. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 1.mp4 24MB
  191. 53. Preparation for Modelling in Machine Learning/9. Applying One Hot Encoding Method to Categorical Variables.mp4 24MB
  192. 3. Creating NumPy Array in Python/2. Creating NumPy Array with Zeros() Function.mp4 24MB
  193. 53. Preparation for Modelling in Machine Learning/8. Transformation Operations on Unsymmetrical Data.mp4 24MB
  194. 19. Fundamentals of Python 3/7. Modules in Python.mp4 24MB
  195. 21. Matplotlib/7. Grid, Spines, Ticks.mp4 24MB
  196. 40. Recommender System Algorithm in Machine Learning A-Z/1. What is the Recommender System Part 1.mp4 23MB
  197. 34. Random Forest Algorithm in Machine Learning A-Z/1. Random Forest Algorithm Theory.mp4 23MB
  198. 9. DataFrame Structures in Pandas Library/1. Creating Pandas DataFrame with List.mp4 23MB
  199. 5. Indexing, Slicing, and Assigning NumPy Arrays/2. Slicing One-Dimensional Numpy Arrays.mp4 22MB
  200. 10. Element Selection Operations in DataFrame Structures/5. Top Level Element Selection in Pandas DataFramesLesson 3.mp4 22MB
  201. 3. Creating NumPy Array in Python/9. Properties of NumPy Array.mp4 22MB
  202. 35. Support Vector Machine Algorithm in Machine Learning A-Z/1. Support Vector Machine Algorithm Theory.mp4 22MB
  203. 16. File Operations in Pandas Library/3. Data Entry with Excel Files.mp4 22MB
  204. 6. Operations in Numpy Library/1. Operations with Comparison Operators.mp4 21MB
  205. 4. Functions in the NumPy Library/5. Splitting One-Dimensional Numpy Arrays The Split.mp4 21MB
  206. 5. Indexing, Slicing, and Assigning NumPy Arrays/6. Fancy Indexing of One-Dimensional Arrrays.mp4 20MB
  207. 25. Evaluation Metrics in Machine Learning/1. Classification vs Regression in Machine Learning.mp4 20MB
  208. 51. Exploratory Data Analysis (EDA) - Uni-variate Analysis/2. Numeric Variables (Analysis with Distplot) Lesson 2.mp4 20MB
  209. 16. File Operations in Pandas Library/5. Outputting as an Excel File.mp4 20MB
  210. 8. Series Structures in the Pandas Library/4. Object Types in Series.mp4 20MB
  211. 21. Matplotlib/1. What is Matplotlib.mp4 19MB
  212. 8. Series Structures in the Pandas Library/5. Examining the Primary Features of the Pandas Seri.mp4 19MB
  213. 8. Series Structures in the Pandas Library/2. Creating a Pandas Series with a Dictionary.mp4 18MB
  214. 5. Indexing, Slicing, and Assigning NumPy Arrays/4. Assigning Value to One-Dimensional Arrays.mp4 18MB
  215. 40. Recommender System Algorithm in Machine Learning A-Z/2. What is the Recommender System Part 2.mp4 18MB
  216. 30. K-fold Cross-Validation in Machine Learning A-Z/1. K-Fold Cross-Validation Theory.mp4 17MB
  217. 20. Object Oriented Programming (OOP)/1. Logic of Object Oriented Programming.mp4 17MB
  218. 37. K Means Clustering Algorithm in Machine Learning A-Z/1. K Means Clustering Algorithm Theory.mp4 17MB
  219. 4. Functions in the NumPy Library/7. Sorting Numpy Arrays Sort() Function.mp4 17MB
  220. 36. Unsupervised Learning with Machine Learning/1. Unsupervised Learning Overview.mp4 17MB
  221. 5. Indexing, Slicing, and Assigning NumPy Arrays/9. Combining Fancy Index with Normal Slicing.mp4 16MB
  222. 3. Creating NumPy Array in Python/3. Creating NumPy Array with Ones() Function.mp4 16MB
  223. 9. DataFrame Structures in Pandas Library/3. Creating Pandas DataFrame with Dictionary.mp4 16MB
  224. 50. Preparation For Exploratory Data Analysis (EDA) in Data Science/3. Separating variables (Numeric or Categorical).mp4 16MB
  225. 11. Structural Operations on Pandas DataFrame/2. Removing Rows and Columns from Pandas Data frames.mp4 16MB
  226. 4. Functions in the NumPy Library/2. Identifying the Largest Element of a Numpy Array.mp4 15MB
  227. 33. Decision Tree Algorithm in Machine Learning A-Z/4. Decision Tree Algorithm with Python Part 3.mp4 15MB
  228. 24. First Contact with Machine Learning/2. Machine Learning Terminology.mp4 14MB
  229. 22. Seaborn/1. What is Seaborn.mp4 14MB
  230. 18. Introduction to Data Visualization with Python/1. Introduction to Data Visualization with Python.mp4 13MB
  231. 5. Indexing, Slicing, and Assigning NumPy Arrays/8. Combining Fancy Index with Normal Indexing.mp4 13MB
  232. 3. Creating NumPy Array in Python/6. Creating NumPy Array with Eye() Function.mp4 13MB
  233. 3. Creating NumPy Array in Python/5. Creating NumPy Array with Arange() Function.mp4 12MB
  234. 9. DataFrame Structures in Pandas Library/2. Creating Pandas DataFrame with NumPy Array.mp4 12MB
  235. 8. Series Structures in the Pandas Library/3. Creating Pandas Series with NumPy Array.mp4 12MB
  236. 53. Preparation for Modelling in Machine Learning/10. Feature Scaling with the Robust Scaler Method for Machine Learning Algorithms.mp4 11MB
  237. 3. Creating NumPy Array in Python/4. Creating NumPy Array with Full() Function.mp4 11MB
  238. 4. Functions in the NumPy Library/3. Detecting Least Element of Numpy Array Min(), Ar.mp4 10MB
  239. 49. First Organization/2. Loading the Statistics Dataset in Data Science.mp4 10MB
  240. 19. Fundamentals of Python 3/9. Exercise - Analyse in Python.mp4 8MB
  241. 39. Principal Component Analysis (PCA) in Machine Learning A-Z/3. Principal Component Analysis (PCA) with Python Part 2.mp4 8MB
  242. 3. Creating NumPy Array in Python/7. Creating NumPy Array with Linspace() Function.mp4 7MB
  243. 48. Introduction to Machine Learning with Real Hearth Attack Prediction Project/2. FAQ about Machine Learning, Data Science.html 15KB
  244. 41. First Contact with Kaggle/2. FAQ about Kaggle.html 11KB
  245. 18. Introduction to Data Visualization with Python/2. FAQ regarding Data Visualization, Python.html 9KB
  246. 24. First Contact with Machine Learning/5. FAQ regarding Machine Learning.html 7KB
  247. 24. First Contact with Machine Learning/4. FAQ regarding Python.html 6KB
  248. 1. Installations/4. 6 Article Advice And Links about Numpy, Numpy Pyhon.html 4KB
  249. 56. Extra/1. Complete Data Science & Machine Learning A-Z with Python.html 266B
  250. 24. First Contact with Machine Learning/3. Machine Learning Project Files.html 254B
  251. 10. Element Selection Operations in DataFrame Structures/7. Quiz.html 205B
  252. 11. Structural Operations on Pandas DataFrame/7. Quiz.html 205B
  253. 12. Multi-Indexed DataFrame Structures/4. Quiz.html 205B
  254. 13. Structural Concatenation Operations in Pandas DataFrame/7. Quiz.html 205B
  255. 14. Functions That Can Be Applied on a DataFrame/10. Quiz.html 205B
  256. 15. Pivot Tables in Pandas Library/3. Quiz.html 205B
  257. 16. File Operations in Pandas Library/6. Quiz.html 205B
  258. 19. Fundamentals of Python 3/11. Quiz.html 205B
  259. 2. NumPy Library Introduction/3. Quiz.html 205B
  260. 20. Object Oriented Programming (OOP)/6. Quiz.html 205B
  261. 21. Matplotlib/10. Quiz.html 205B
  262. 22. Seaborn/8. Quiz.html 205B
  263. 23. Geoplotlib/5. Quiz.html 205B
  264. 24. First Contact with Machine Learning/6. Quiz.html 205B
  265. 25. Evaluation Metrics in Machine Learning/5. Quiz.html 205B
  266. 26. Supervised Learning with Machine Learning/2. Quiz.html 205B
  267. 28. Bias Variance Trade-Off in Machine Learning/2. Quiz.html 205B
  268. 29. Logistic Regression Algorithm in Machine Learning A-Z/7. Quiz.html 205B
  269. 3. Creating NumPy Array in Python/10. Quiz.html 205B
  270. 31. K Nearest Neighbors Algorithm in Machine Learning A-Z/5. Quiz.html 205B
  271. 33. Decision Tree Algorithm in Machine Learning A-Z/7. Quiz.html 205B
  272. 35. Support Vector Machine Algorithm in Machine Learning A-Z/6. Quiz.html 205B
  273. 37. K Means Clustering Algorithm in Machine Learning A-Z/6. Quiz.html 205B
  274. 4. Functions in the NumPy Library/8. Quiz.html 205B
  275. 41. First Contact with Kaggle/6. quiz.html 205B
  276. 43. Dataset Section on Kaggle/2. Quiz.html 205B
  277. 44. Code Section on Kaggle/4. Quiz.html 205B
  278. 45. Discussion Section on Kaggle/2. Quiz.html 205B
  279. 46. Other Most Used Options on Kaggle/4. Quiz.html 205B
  280. 47. Details on Kaggle/5. Quiz.html 205B
  281. 48. Introduction to Machine Learning with Real Hearth Attack Prediction Project/7. Quiz.html 205B
  282. 49. First Organization/4. Quiz.html 205B
  283. 50. Preparation For Exploratory Data Analysis (EDA) in Data Science/5. Quiz.html 205B
  284. 51. Exploratory Data Analysis (EDA) - Uni-variate Analysis/6. Quiz.html 205B
  285. 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/15. Quiz.html 205B
  286. 53. Preparation for Modelling in Machine Learning/12. Quiz.html 205B
  287. 54. Modelling for Machine Learning/9. Quiz.html 205B
  288. 55. Conclusion/2. Quiz.html 205B
  289. 7. Pandas Library Introduction/3. Quiz.html 205B
  290. 8. Series Structures in the Pandas Library/8. Quiz.html 205B
  291. 9. DataFrame Structures in Pandas Library/5. Quiz.html 205B
  292. 7. Pandas Library Introduction/2. Pandas Project Files Link.html 180B
  293. 17. Code Files And Resources Python data analysis and visualization/1. Data Visualisation - Matplotlib Files.html 170B
  294. 17. Code Files And Resources Python data analysis and visualization/2. Data Visualisation - Seaborn Files.html 170B
  295. 17. Code Files And Resources Python data analysis and visualization/3. Data Visualisation - Geoplotlib.html 168B
  296. 1. Installations/2. Notebook Project Files Link regarding NumPy Python Programming Language Library.html 155B
  297. 0. Websites you may like/[FreeCourseSite.com].url 127B
  298. 11. Structural Operations on Pandas DataFrame/0. Websites you may like/[FreeCourseSite.com].url 127B
  299. 26. Supervised Learning with Machine Learning/0. Websites you may like/[FreeCourseSite.com].url 127B
  300. 0. Websites you may like/[CourseClub.Me].url 122B
  301. 11. Structural Operations on Pandas DataFrame/0. Websites you may like/[CourseClub.Me].url 122B
  302. 26. Supervised Learning with Machine Learning/0. Websites you may like/[CourseClub.Me].url 122B
  303. 48. Introduction to Machine Learning with Real Hearth Attack Prediction Project/4. Project Link File - Hearth Attack Prediction Project, Machine Learning.html 108B
  304. 41. First Contact with Kaggle/4. Project Link File - Hearth Attack Prediction Project, Machine Learning.html 97B
  305. 0. Websites you may like/[GigaCourse.Com].url 49B
  306. 11. Structural Operations on Pandas DataFrame/0. Websites you may like/[GigaCourse.Com].url 49B
  307. 26. Supervised Learning with Machine Learning/0. Websites you may like/[GigaCourse.Com].url 49B