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FreeCourseSite com Udemy - Complete Data Science & Machine Learning A-Z with Python

文件列表
  1. 1. Installations/1. Installing Anaconda Distribution for Windows.mp4118.32MB
  2. 1. Installations/3. Installing Anaconda Distribution for MacOs.mp446.31MB
  3. 1. Installations/5. Installing Anaconda Distribution for Linux.mp4114.75MB
  4. 10. Element Selection Operations in DataFrame Structures/1. Element Selection Operations in Pandas DataFrames Lesson 1.mp429.9MB
  5. 10. Element Selection Operations in DataFrame Structures/2. Element Selection Operations in Pandas DataFrames Lesson 2.mp431.84MB
  6. 10. Element Selection Operations in DataFrame Structures/3. Top Level Element Selection in Pandas DataFramesLesson 1.mp438.29MB
  7. 10. Element Selection Operations in DataFrame Structures/4. Top Level Element Selection in Pandas DataFramesLesson 2.mp431.41MB
  8. 10. Element Selection Operations in DataFrame Structures/5. Top Level Element Selection in Pandas DataFramesLesson 3.mp422.11MB
  9. 10. Element Selection Operations in DataFrame Structures/6. Element Selection with Conditional Operations in.mp446.37MB
  10. 11. Structural Operations on Pandas DataFrame/1. Adding Columns to Pandas Data Frames.mp433.58MB
  11. 11. Structural Operations on Pandas DataFrame/2. Removing Rows and Columns from Pandas Data frames.mp415.56MB
  12. 11. Structural Operations on Pandas DataFrame/3. Null Values in Pandas Dataframes.mp466.96MB
  13. 11. Structural Operations on Pandas DataFrame/4. Dropping Null Values Dropna() Function.mp434.54MB
  14. 11. Structural Operations on Pandas DataFrame/5. Filling Null Values Fillna() Function.mp451.62MB
  15. 11. Structural Operations on Pandas DataFrame/6. Setting Index in Pandas DataFrames.mp439.7MB
  16. 12. Multi-Indexed DataFrame Structures/1. Multi-Index and Index Hierarchy in Pandas DataFrames.mp442.66MB
  17. 12. Multi-Indexed DataFrame Structures/2. Element Selection in Multi-Indexed DataFrames.mp424.58MB
  18. 12. Multi-Indexed DataFrame Structures/3. Selecting Elements Using the xs() Function in Multi-Indexed DataFrames.mp431.25MB
  19. 13. Structural Concatenation Operations in Pandas DataFrame/1. Concatenating Pandas Dataframes Concat Function.mp463.84MB
  20. 13. Structural Concatenation Operations in Pandas DataFrame/2. Merge Pandas Dataframes Merge() Function Lesson 1.mp457.29MB
  21. 13. Structural Concatenation Operations in Pandas DataFrame/3. Merge Pandas Dataframes Merge() Function Lesson 2.mp430.55MB
  22. 13. Structural Concatenation Operations in Pandas DataFrame/4. Merge Pandas Dataframes Merge() Function Lesson 3.mp460.17MB
  23. 13. Structural Concatenation Operations in Pandas DataFrame/5. Merge Pandas Dataframes Merge() Function Lesson 4.mp440.68MB
  24. 13. Structural Concatenation Operations in Pandas DataFrame/6. Joining Pandas Dataframes Join() Function.mp456.05MB
  25. 14. Functions That Can Be Applied on a DataFrame/1. Loading a Dataset from the Seaborn Library.mp437.72MB
  26. 14. Functions That Can Be Applied on a DataFrame/2. Examining the Data Set 1.mp442.9MB
  27. 14. Functions That Can Be Applied on a DataFrame/3. Aggregation Functions in Pandas DataFrames.mp490.69MB
  28. 14. Functions That Can Be Applied on a DataFrame/4. Examining the Data Set 2.mp446.58MB
  29. 14. Functions That Can Be Applied on a DataFrame/5. Coordinated Use of Grouping and Aggregation Functions in Pandas Dataframes.mp488.12MB
  30. 14. Functions That Can Be Applied on a DataFrame/6. Advanced Aggregation Functions Aggregate() Function.mp429.22MB
  31. 14. Functions That Can Be Applied on a DataFrame/7. Advanced Aggregation Functions Filter() Function.mp424.45MB
  32. 14. Functions That Can Be Applied on a DataFrame/8. Advanced Aggregation Functions Transform() Function.mp447.09MB
  33. 14. Functions That Can Be Applied on a DataFrame/9. Advanced Aggregation Functions Apply() Function.mp441.42MB
  34. 15. Pivot Tables in Pandas Library/1. Examining the Data Set 3.mp439.11MB
  35. 15. Pivot Tables in Pandas Library/2. Pivot Tables in Pandas Library.mp454.23MB
  36. 16. File Operations in Pandas Library/1. Accessing and Making Files Available.mp434.61MB
  37. 16. File Operations in Pandas Library/2. Data Entry with Csv and Txt Files.mp464.34MB
  38. 16. File Operations in Pandas Library/3. Data Entry with Excel Files.mp421.84MB
  39. 16. File Operations in Pandas Library/4. Outputting as an CSV Extension.mp435.7MB
  40. 16. File Operations in Pandas Library/5. Outputting as an Excel File.mp419.74MB
  41. 18. Introduction to Data Visualization with Python/1. Introduction to Data Visualization with Python.mp412.85MB
  42. 19. Fundamentals of Python 3/1. Data Types in Python.mp447.07MB
  43. 19. Fundamentals of Python 3/10. Exercise - Solution in Python.mp451.89MB
  44. 19. Fundamentals of Python 3/2. Operators in Python.mp435.71MB
  45. 19. Fundamentals of Python 3/3. Conditionals in Python.mp441.23MB
  46. 19. Fundamentals of Python 3/4. Loops in Python.mp458.81MB
  47. 19. Fundamentals of Python 3/5. Lists, Tuples, Dictionaries and Sets in pyhton.mp475.33MB
  48. 19. Fundamentals of Python 3/6. Data Type Operators and Methods in Python.mp443.86MB
  49. 19. Fundamentals of Python 3/7. Modules in Python.mp423.95MB
  50. 19. Fundamentals of Python 3/8. Functions in Python.mp428.93MB
  51. 19. Fundamentals of Python 3/9. Exercise - Analyse in Python.mp48.46MB
  52. 2. NumPy Library Introduction/1. Introduction to NumPy Library.mp445.27MB
  53. 2. NumPy Library Introduction/2. The Power of NumPy.mp459.87MB
  54. 20. Object Oriented Programming (OOP)/1. Logic of Object Oriented Programming.mp417.38MB
  55. 20. Object Oriented Programming (OOP)/2. Constructor in Object Oriented Programming (OOP).mp435.84MB
  56. 20. Object Oriented Programming (OOP)/3. Methods in Object Oriented Programming (OOP).mp425.1MB
  57. 20. Object Oriented Programming (OOP)/4. Inheritance in Object Oriented Programming (OOP).mp434.58MB
  58. 20. Object Oriented Programming (OOP)/5. Overriding and Overloading in Object Oriented Programming (OOP).mp462.7MB
  59. 21. Matplotlib/1. What is Matplotlib.mp419.06MB
  60. 21. Matplotlib/2. Using Pyplot.mp428.22MB
  61. 21. Matplotlib/3. Pyplot – Pylab - Matplotlib.mp428.37MB
  62. 21. Matplotlib/4. Figure, Subplot and Axex.mp469.89MB
  63. 21. Matplotlib/5. Figure Customization.mp463.29MB
  64. 21. Matplotlib/6. Plot Customization.mp427.38MB
  65. 21. Matplotlib/7. Grid, Spines, Ticks.mp423.89MB
  66. 21. Matplotlib/8. Basic Plots in Matplotlib I.mp4111.17MB
  67. 21. Matplotlib/9. Basic Plots in Matplotlib II.mp454.82MB
  68. 22. Seaborn/1. What is Seaborn.mp413.59MB
  69. 22. Seaborn/2. Controlling Figure Aesthetics in Seaborn.mp441.82MB
  70. 22. Seaborn/3. Example in Seaborn.mp454.9MB
  71. 22. Seaborn/4. Color Palettes in Seaborn.mp448.32MB
  72. 22. Seaborn/5. Basic Plots in Seaborn.mp498.84MB
  73. 22. Seaborn/6. Multi-Plots in Seaborn.mp442.98MB
  74. 22. Seaborn/7. Regression Plots and Squarify in Seaborn.mp460.1MB
  75. 23. Geoplotlib/1. What is Geoplotlib.mp434.18MB
  76. 23. Geoplotlib/2. Example - 1.mp438.85MB
  77. 23. Geoplotlib/3. Example - 2.mp481.14MB
  78. 23. Geoplotlib/4. Example - 3.mp451.28MB
  79. 24. First Contact with Machine Learning/1. What is Machine Learning.mp427.58MB
  80. 24. First Contact with Machine Learning/2. Machine Learning Terminology.mp414.03MB
  81. 25. Evaluation Metrics in Machine Learning/1. Classification vs Regression in Machine Learning.mp419.89MB
  82. 25. Evaluation Metrics in Machine Learning/2. Machine Learning Model Performance Evaluation Classification Error Metrics.mp4100.26MB
  83. 25. Evaluation Metrics in Machine Learning/3. Evaluating Performance Regression Error Metrics in Python.mp445.7MB
  84. 25. Evaluation Metrics in Machine Learning/4. Machine Learning With Python.mp492.24MB
  85. 26. Supervised Learning with Machine Learning/1. What is Supervised Learning in Machine Learning.mp431.69MB
  86. 27. Linear Regression Algorithm in Machine Learning A-Z/1. Linear Regression Algorithm Theory in Machine Learning A-Z.mp434.06MB
  87. 27. Linear Regression Algorithm in Machine Learning A-Z/2. Linear Regression Algorithm With Python Part 1.mp476.17MB
  88. 27. Linear Regression Algorithm in Machine Learning A-Z/3. Linear Regression Algorithm With Python Part 2.mp4106.94MB
  89. 27. Linear Regression Algorithm in Machine Learning A-Z/4. Linear Regression Algorithm With Python Part 3.mp470.28MB
  90. 27. Linear Regression Algorithm in Machine Learning A-Z/5. Linear Regression Algorithm With Python Part 4.mp490MB
  91. 28. Bias Variance Trade-Off in Machine Learning/1. What is Bias Variance Trade-Off.mp455.03MB
  92. 29. Logistic Regression Algorithm in Machine Learning A-Z/1. What is Logistic Regression Algorithm in Machine Learning.mp427.84MB
  93. 29. Logistic Regression Algorithm in Machine Learning A-Z/2. Logistic Regression Algorithm with Python Part 1.mp472.22MB
  94. 29. Logistic Regression Algorithm in Machine Learning A-Z/3. Logistic Regression Algorithm with Python Part 2.mp481.46MB
  95. 29. Logistic Regression Algorithm in Machine Learning A-Z/4. Logistic Regression Algorithm with Python Part 3.mp447.35MB
  96. 29. Logistic Regression Algorithm in Machine Learning A-Z/5. Logistic Regression Algorithm with Python Part 4.mp447.17MB
  97. 29. Logistic Regression Algorithm in Machine Learning A-Z/6. Logistic Regression Algorithm with Python Part 5.mp439.35MB
  98. 3. Creating NumPy Array in Python/1. Creating NumPy Array with The Array() Function.mp429.5MB
  99. 3. Creating NumPy Array in Python/2. Creating NumPy Array with Zeros() Function.mp424.06MB
  100. 3. Creating NumPy Array in Python/3. Creating NumPy Array with Ones() Function.mp415.88MB
  101. 3. Creating NumPy Array in Python/4. Creating NumPy Array with Full() Function.mp411.18MB
  102. 3. Creating NumPy Array in Python/5. Creating NumPy Array with Arange() Function.mp412.1MB
  103. 3. Creating NumPy Array in Python/6. Creating NumPy Array with Eye() Function.mp412.55MB
  104. 3. Creating NumPy Array in Python/7. Creating NumPy Array with Linspace() Function.mp47.34MB
  105. 3. Creating NumPy Array in Python/8. Creating NumPy Array with Random() Function.mp443.3MB
  106. 3. Creating NumPy Array in Python/9. Properties of NumPy Array.mp421.98MB
  107. 30. K-fold Cross-Validation in Machine Learning A-Z/1. K-Fold Cross-Validation Theory.mp417.45MB
  108. 30. K-fold Cross-Validation in Machine Learning A-Z/2. K-Fold Cross-Validation with Python.mp434.67MB
  109. 31. K Nearest Neighbors Algorithm in Machine Learning A-Z/1. K Nearest Neighbors Algorithm Theory.mp428.66MB
  110. 31. K Nearest Neighbors Algorithm in Machine Learning A-Z/2. K Nearest Neighbors Algorithm with Python Part 1.mp435.03MB
  111. 31. K Nearest Neighbors Algorithm in Machine Learning A-Z/3. K Nearest Neighbors Algorithm with Python Part 2.mp459.37MB
  112. 31. K Nearest Neighbors Algorithm in Machine Learning A-Z/4. K Nearest Neighbors Algorithm with Python Part 3.mp431.4MB
  113. 32. Hyperparameter Optimization/1. Hyperparameter Optimization Theory.mp433.14MB
  114. 32. Hyperparameter Optimization/2. Hyperparameter Optimization with Python.mp447.47MB
  115. 33. Decision Tree Algorithm in Machine Learning A-Z/1. Decision Tree Algorithm Theory.mp435.75MB
  116. 33. Decision Tree Algorithm in Machine Learning A-Z/2. Decision Tree Algorithm with Python Part 1.mp431.53MB
  117. 33. Decision Tree Algorithm in Machine Learning A-Z/3. Decision Tree Algorithm with Python Part 2.mp448.92MB
  118. 33. Decision Tree Algorithm in Machine Learning A-Z/4. Decision Tree Algorithm with Python Part 3.mp414.72MB
  119. 33. Decision Tree Algorithm in Machine Learning A-Z/5. Decision Tree Algorithm with Python Part 4.mp442.49MB
  120. 33. Decision Tree Algorithm in Machine Learning A-Z/6. Decision Tree Algorithm with Python Part 5.mp432.67MB
  121. 34. Random Forest Algorithm in Machine Learning A-Z/1. Random Forest Algorithm Theory.mp422.89MB
  122. 34. Random Forest Algorithm in Machine Learning A-Z/2. Random Forest Algorithm with Pyhon Part 1.mp438.61MB
  123. 34. Random Forest Algorithm in Machine Learning A-Z/3. Random Forest Algorithm with Pyhon Part 2.mp438.72MB
  124. 35. Support Vector Machine Algorithm in Machine Learning A-Z/1. Support Vector Machine Algorithm Theory.mp421.84MB
  125. 35. Support Vector Machine Algorithm in Machine Learning A-Z/2. Support Vector Machine Algorithm with Python Part 1.mp435.59MB
  126. 35. Support Vector Machine Algorithm in Machine Learning A-Z/3. Support Vector Machine Algorithm with Python Part 2.mp441.72MB
  127. 35. Support Vector Machine Algorithm in Machine Learning A-Z/4. Support Vector Machine Algorithm with Python Part 3.mp434.77MB
  128. 35. Support Vector Machine Algorithm in Machine Learning A-Z/5. Support Vector Machine Algorithm with Python Part 4.mp437.55MB
  129. 36. Unsupervised Learning with Machine Learning/1. Unsupervised Learning Overview.mp416.91MB
  130. 37. K Means Clustering Algorithm in Machine Learning A-Z/1. K Means Clustering Algorithm Theory.mp417.13MB
  131. 37. K Means Clustering Algorithm in Machine Learning A-Z/2. K Means Clustering Algorithm with Python Part 1.mp429.96MB
  132. 37. K Means Clustering Algorithm in Machine Learning A-Z/3. K Means Clustering Algorithm with Python Part 2.mp429.64MB
  133. 37. K Means Clustering Algorithm in Machine Learning A-Z/4. K Means Clustering Algorithm with Python Part 3.mp427.75MB
  134. 37. K Means Clustering Algorithm in Machine Learning A-Z/5. K Means Clustering Algorithm with Python Part 4.mp429.03MB
  135. 38. Hierarchical Clustering Algorithm in machine learning data science/1. Hierarchical Clustering Algorithm Theory.mp428.55MB
  136. 38. Hierarchical Clustering Algorithm in machine learning data science/2. Hierarchical Clustering Algorithm with Python Part 2.mp435.52MB
  137. 38. Hierarchical Clustering Algorithm in machine learning data science/3. Hierarchical Clustering Algorithm with Python Part 2.mp428.89MB
  138. 39. Principal Component Analysis (PCA) in Machine Learning A-Z/1. Principal Component Analysis (PCA) Theory.mp437.96MB
  139. 39. Principal Component Analysis (PCA) in Machine Learning A-Z/2. Principal Component Analysis (PCA) with Python Part 1.mp426.02MB
  140. 39. Principal Component Analysis (PCA) in Machine Learning A-Z/3. Principal Component Analysis (PCA) with Python Part 2.mp48.42MB
  141. 39. Principal Component Analysis (PCA) in Machine Learning A-Z/4. Principal Component Analysis (PCA) with Python Part 3.mp437.25MB
  142. 4. Functions in the NumPy Library/1. Reshaping a NumPy Array Reshape() Function.mp426.16MB
  143. 4. Functions in the NumPy Library/2. Identifying the Largest Element of a Numpy Array.mp415.12MB
  144. 4. Functions in the NumPy Library/3. Detecting Least Element of Numpy Array Min(), Ar.mp410.17MB
  145. 4. Functions in the NumPy Library/4. Concatenating Numpy Arrays Concatenate() Functio.mp438.36MB
  146. 4. Functions in the NumPy Library/5. Splitting One-Dimensional Numpy Arrays The Split.mp420.9MB
  147. 4. Functions in the NumPy Library/6. Splitting Two-Dimensional Numpy Arrays Split(),.mp435.73MB
  148. 4. Functions in the NumPy Library/7. Sorting Numpy Arrays Sort() Function.mp417.02MB
  149. 40. Recommender System Algorithm in Machine Learning A-Z/1. What is the Recommender System Part 1.mp423.04MB
  150. 40. Recommender System Algorithm in Machine Learning A-Z/2. What is the Recommender System Part 2.mp417.96MB
  151. 41. First Contact with Kaggle/1. What is Kaggle.mp4129.67MB
  152. 41. First Contact with Kaggle/3. Registering on Kaggle and Member Login Procedures.mp443.48MB
  153. 41. First Contact with Kaggle/5. Getting to Know the Kaggle Homepage.mp4122.93MB
  154. 42. Competition Section on Kaggle/1. Competitions on Kaggle Lesson 1.mp4188.17MB
  155. 42. Competition Section on Kaggle/2. Competitions on Kaggle Lesson 2.mp4191.68MB
  156. 43. Dataset Section on Kaggle/1. Datasets on Kaggle.mp4133.23MB
  157. 44. Code Section on Kaggle/1. Examining the Code Section in Kaggle Lesson 1.mp479.53MB
  158. 44. Code Section on Kaggle/2. Examining the Code Section in Kaggle Lesson 2.mp4105.81MB
  159. 44. Code Section on Kaggle/3. Examining the Code Section in Kaggle Lesson 3.mp4159.89MB
  160. 45. Discussion Section on Kaggle/1. What is Discussion on Kaggle.mp440.63MB
  161. 46. Other Most Used Options on Kaggle/1. Courses in Kaggle.mp452.14MB
  162. 46. Other Most Used Options on Kaggle/2. Ranking Among Users on Kaggle.mp4107.04MB
  163. 46. Other Most Used Options on Kaggle/3. Blog and Documentation Sections.mp440.85MB
  164. 47. Details on Kaggle/1. User Page Review on Kaggle.mp481.5MB
  165. 47. Details on Kaggle/2. Treasure in The Kaggle.mp474.64MB
  166. 47. Details on Kaggle/3. Publishing Notebooks on Kaggle.mp438.2MB
  167. 47. Details on Kaggle/4. What Should Be Done to Achieve Success in Kaggle.mp458.48MB
  168. 48. Introduction to Machine Learning with Real Hearth Attack Prediction Project/1. First Step to the Hearth Attack Prediction Project.mp4117.14MB
  169. 48. Introduction to Machine Learning with Real Hearth Attack Prediction Project/3. Notebook Design to be Used in the Project.mp4104.93MB
  170. 48. Introduction to Machine Learning with Real Hearth Attack Prediction Project/5. Examining the Project Topic.mp476.51MB
  171. 48. Introduction to Machine Learning with Real Hearth Attack Prediction Project/6. Recognizing Variables In Dataset.mp4126.87MB
  172. 49. First Organization/1. Required Python Libraries.mp463.55MB
  173. 49. First Organization/2. Loading the Statistics Dataset in Data Science.mp410MB
  174. 49. First Organization/3. Initial analysis on the dataset.mp463.96MB
  175. 5. Indexing, Slicing, and Assigning NumPy Arrays/1. Indexing Numpy Arrays,.mp426.56MB
  176. 5. Indexing, Slicing, and Assigning NumPy Arrays/2. Slicing One-Dimensional Numpy Arrays.mp422.27MB
  177. 5. Indexing, Slicing, and Assigning NumPy Arrays/3. Slicing Two-Dimensional Numpy Arrays.mp434.27MB
  178. 5. Indexing, Slicing, and Assigning NumPy Arrays/4. Assigning Value to One-Dimensional Arrays.mp418.2MB
  179. 5. Indexing, Slicing, and Assigning NumPy Arrays/5. Assigning Value to Two-Dimensional Array.mp435.4MB
  180. 5. Indexing, Slicing, and Assigning NumPy Arrays/6. Fancy Indexing of One-Dimensional Arrrays.mp420.49MB
  181. 5. Indexing, Slicing, and Assigning NumPy Arrays/7. Fancy Indexing of Two-Dimensional Arrrays.mp445.75MB
  182. 5. Indexing, Slicing, and Assigning NumPy Arrays/8. Combining Fancy Index with Normal Indexing.mp412.65MB
  183. 5. Indexing, Slicing, and Assigning NumPy Arrays/9. Combining Fancy Index with Normal Slicing.mp416.46MB
  184. 50. Preparation For Exploratory Data Analysis (EDA) in Data Science/1. Examining Missing Values.mp445.79MB
  185. 50. Preparation For Exploratory Data Analysis (EDA) in Data Science/2. Examining Unique Values.mp444.54MB
  186. 50. Preparation For Exploratory Data Analysis (EDA) in Data Science/3. Separating variables (Numeric or Categorical).mp415.81MB
  187. 50. Preparation For Exploratory Data Analysis (EDA) in Data Science/4. Examining Statistics of Variables.mp491.37MB
  188. 51. Exploratory Data Analysis (EDA) - Uni-variate Analysis/1. Numeric Variables (Analysis with Distplot) Lesson 1.mp480.35MB
  189. 51. Exploratory Data Analysis (EDA) - Uni-variate Analysis/2. Numeric Variables (Analysis with Distplot) Lesson 2.mp419.75MB
  190. 51. Exploratory Data Analysis (EDA) - Uni-variate Analysis/3. Categoric Variables (Analysis with Pie Chart) Lesson 1.mp474.74MB
  191. 51. Exploratory Data Analysis (EDA) - Uni-variate Analysis/4. Categoric Variables (Analysis with Pie Chart) Lesson 2.mp484.06MB
  192. 51. Exploratory Data Analysis (EDA) - Uni-variate Analysis/5. Examining the Missing Data According to the Analysis Result.mp453.78MB
  193. 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/1. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 1.mp449.37MB
  194. 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/10. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 2.mp468.08MB
  195. 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/11. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 1.mp438.07MB
  196. 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/12. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 2.mp435.47MB
  197. 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/13. Relationships between variables (Analysis with Heatmap) Lesson 1.mp436.36MB
  198. 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/14. Relationships between variables (Analysis with Heatmap) Lesson 2.mp490.67MB
  199. 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/2. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 2.mp435.64MB
  200. 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/3. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 1.mp424.15MB
  201. 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/4. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 2.mp456.27MB
  202. 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/5. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 1.mp428.31MB
  203. 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/6. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 2.mp447.14MB
  204. 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/7. Feature Scaling with the Robust Scaler Method.mp435.2MB
  205. 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/8. Creating a New DataFrame with the Melt() Function.mp452.89MB
  206. 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/9. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 1.mp441.72MB
  207. 53. Preparation for Modelling in Machine Learning/1. Dropping Columns with Low Correlation.mp426.83MB
  208. 53. Preparation for Modelling in Machine Learning/10. Feature Scaling with the Robust Scaler Method for Machine Learning Algorithms.mp411.45MB
  209. 53. Preparation for Modelling in Machine Learning/11. Separating Data into Test and Training Set.mp429.75MB
  210. 53. Preparation for Modelling in Machine Learning/2. Visualizing Outliers.mp434.89MB
  211. 53. Preparation for Modelling in Machine Learning/3. Dealing with Outliers – Trtbps Variable Lesson 1.mp442.82MB
  212. 53. Preparation for Modelling in Machine Learning/4. Dealing with Outliers – Trtbps Variable Lesson 2.mp443.91MB
  213. 53. Preparation for Modelling in Machine Learning/5. Dealing with Outliers – Thalach Variable.mp436.24MB
  214. 53. Preparation for Modelling in Machine Learning/6. Dealing with Outliers – Oldpeak Variable.mp436.06MB
  215. 53. Preparation for Modelling in Machine Learning/7. Determining Distributions of Numeric Variables.mp425.17MB
  216. 53. Preparation for Modelling in Machine Learning/8. Transformation Operations on Unsymmetrical Data.mp424.01MB
  217. 53. Preparation for Modelling in Machine Learning/9. Applying One Hot Encoding Method to Categorical Variables.mp424.09MB
  218. 54. Modelling for Machine Learning/1. Logistic Regression.mp429.34MB
  219. 54. Modelling for Machine Learning/2. Cross Validation.mp430.21MB
  220. 54. Modelling for Machine Learning/3. Roc Curve and Area Under Curve (AUC).mp441.71MB
  221. 54. Modelling for Machine Learning/4. Hyperparameter Optimization (with GridSearchCV).mp458.77MB
  222. 54. Modelling for Machine Learning/5. Decision Tree Algorithm.mp425.7MB
  223. 54. Modelling for Machine Learning/6. Support Vector Machine Algorithm.mp424.52MB
  224. 54. Modelling for Machine Learning/7. Random Forest Algorithm.mp429.78MB
  225. 54. Modelling for Machine Learning/8. Hyperparameter Optimization (with GridSearchCV).mp452.65MB
  226. 55. Conclusion/1. Project Conclusion and Sharing.mp428.66MB
  227. 6. Operations in Numpy Library/1. Operations with Comparison Operators.mp421.14MB
  228. 6. Operations in Numpy Library/2. Arithmetic Operations in Numpy.mp471.82MB
  229. 6. Operations in Numpy Library/3. Statistical Operations in Numpy.mp432.02MB
  230. 6. Operations in Numpy Library/4. Solving Second-Degree Equations with NumPy.mp424.2MB
  231. 7. Pandas Library Introduction/1. Introduction to Pandas Library.mp433.93MB
  232. 8. Series Structures in the Pandas Library/1. Creating a Pandas Series with a List.mp439.19MB
  233. 8. Series Structures in the Pandas Library/2. Creating a Pandas Series with a Dictionary.mp418.29MB
  234. 8. Series Structures in the Pandas Library/3. Creating Pandas Series with NumPy Array.mp411.97MB
  235. 8. Series Structures in the Pandas Library/4. Object Types in Series.mp419.58MB
  236. 8. Series Structures in the Pandas Library/5. Examining the Primary Features of the Pandas Seri.mp418.94MB
  237. 8. Series Structures in the Pandas Library/6. Most Applied Methods on Pandas Series.mp448.21MB
  238. 8. Series Structures in the Pandas Library/7. Indexing and Slicing Pandas Series.mp429.89MB
  239. 9. DataFrame Structures in Pandas Library/1. Creating Pandas DataFrame with List.mp422.57MB
  240. 9. DataFrame Structures in Pandas Library/2. Creating Pandas DataFrame with NumPy Array.mp412.1MB
  241. 9. DataFrame Structures in Pandas Library/3. Creating Pandas DataFrame with Dictionary.mp415.84MB
  242. 9. DataFrame Structures in Pandas Library/4. Examining the Properties of Pandas DataFrames.mp425.94MB
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