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[GigaCourse.Com] Udemy - 2022 Python for Machine Learning & Data Science Masterclass

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GigaCourse Com Udemy - 2022 Python for Machine Learning & Data Science Masterclass

文件列表
  1. 01 - Introduction to Course/002 COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP_.mp47.22MB
  2. 01 - Introduction to Course/003 Anaconda Python and Jupyter Install and Setup.mp484.53MB
  3. 01 - Introduction to Course/005 Environment Setup.mp435.71MB
  4. 02 - OPTIONAL_ Python Crash Course/002 Python Crash Course - Part One.mp429.74MB
  5. 02 - OPTIONAL_ Python Crash Course/003 Python Crash Course - Part Two.mp457.63MB
  6. 02 - OPTIONAL_ Python Crash Course/004 Python Crash Course - Part Three.mp432.01MB
  7. 02 - OPTIONAL_ Python Crash Course/005 Python Crash Course - Exercise Questions.mp43.41MB
  8. 02 - OPTIONAL_ Python Crash Course/006 Python Crash Course - Exercise Solutions.mp448.7MB
  9. 03 - Machine Learning Pathway Overview/001 Machine Learning Pathway.mp414.1MB
  10. 04 - NumPy/001 Introduction to NumPy.mp43.37MB
  11. 04 - NumPy/002 NumPy Arrays.mp499.45MB
  12. 04 - NumPy/003 NumPy Indexing and Selection.mp439.63MB
  13. 04 - NumPy/004 NumPy Operations.mp436.06MB
  14. 04 - NumPy/005 NumPy Exercises.mp49.64MB
  15. 04 - NumPy/006 Numpy Exercises - Solutions.mp434.88MB
  16. 05 - Pandas/001 Introduction to Pandas.mp46.7MB
  17. 05 - Pandas/002 Series - Part One.mp428.62MB
  18. 05 - Pandas/003 Series - Part Two.mp426.12MB
  19. 05 - Pandas/004 DataFrames - Part One - Creating a DataFrame.mp497.48MB
  20. 05 - Pandas/005 DataFrames - Part Two - Basic Properties.mp440.28MB
  21. 05 - Pandas/006 DataFrames - Part Three - Working with Columns.mp484.08MB
  22. 05 - Pandas/007 DataFrames - Part Four - Working with Rows.mp472.59MB
  23. 05 - Pandas/008 Pandas - Conditional Filtering.mp469.21MB
  24. 05 - Pandas/009 Pandas - Useful Methods - Apply on Single Column.mp453.72MB
  25. 05 - Pandas/010 Pandas - Useful Methods - Apply on Multiple Columns.mp485.32MB
  26. 05 - Pandas/011 Pandas - Useful Methods - Statistical Information and Sorting.mp474.37MB
  27. 05 - Pandas/012 Missing Data - Overview.mp427.24MB
  28. 05 - Pandas/013 Missing Data - Pandas Operations.mp473.6MB
  29. 05 - Pandas/014 GroupBy Operations - Part One.mp486.96MB
  30. 05 - Pandas/015 GroupBy Operations - Part Two - MultiIndex.mp492.86MB
  31. 05 - Pandas/016 Combining DataFrames - Concatenation.mp436.84MB
  32. 05 - Pandas/017 Combining DataFrames - Inner Merge.mp440.27MB
  33. 05 - Pandas/018 Combining DataFrames - Left and Right Merge.mp416.4MB
  34. 05 - Pandas/019 Combining DataFrames - Outer Merge.mp422.17MB
  35. 05 - Pandas/020 Pandas - Text Methods for String Data.mp445.12MB
  36. 05 - Pandas/021 Pandas - Time Methods for Date and Time Data.mp480.19MB
  37. 05 - Pandas/022 Pandas Input and Output - CSV Files.mp437.15MB
  38. 05 - Pandas/023 Pandas Input and Output - HTML Tables.mp4102.34MB
  39. 05 - Pandas/024 Pandas Input and Output - Excel Files.mp425.87MB
  40. 05 - Pandas/025 Pandas Input and Output - SQL Databases.mp495.98MB
  41. 05 - Pandas/026 Pandas Pivot Tables.mp4129.09MB
  42. 05 - Pandas/027 Pandas Project Exercise Overview.mp439.43MB
  43. 05 - Pandas/028 Pandas Project Exercise Solutions.mp4172.55MB
  44. 06 - Matplotlib/001 Introduction to Matplotlib.mp46.55MB
  45. 06 - Matplotlib/002 Matplotlib Basics.mp431.07MB
  46. 06 - Matplotlib/003 Matplotlib - Understanding the Figure Object.mp411.7MB
  47. 06 - Matplotlib/004 Matplotlib - Implementing Figures and Axes.mp434.86MB
  48. 06 - Matplotlib/005 Matplotlib - Figure Parameters.mp413.06MB
  49. 06 - Matplotlib/006 Matplotlib - Subplots Functionality.mp496.57MB
  50. 06 - Matplotlib/007 Matplotlib Styling - Legends.mp416.19MB
  51. 06 - Matplotlib/008 Matplotlib Styling - Colors and Styles.mp444.27MB
  52. 06 - Matplotlib/009 Advanced Matplotlib Commands (Optional).mp425.19MB
  53. 06 - Matplotlib/010 Matplotlib Exercise Questions Overview.mp448.99MB
  54. 06 - Matplotlib/011 Matplotlib Exercise Questions - Solutions.mp4105.86MB
  55. 07 - Seaborn Data Visualizations/001 Introduction to Seaborn.mp45.74MB
  56. 07 - Seaborn Data Visualizations/002 Scatterplots with Seaborn.mp4111.3MB
  57. 07 - Seaborn Data Visualizations/003 Distribution Plots - Part One - Understanding Plot Types.mp415.03MB
  58. 07 - Seaborn Data Visualizations/004 Distribution Plots - Part Two - Coding with Seaborn.mp459.21MB
  59. 07 - Seaborn Data Visualizations/005 Categorical Plots - Statistics within Categories - Understanding Plot Types.mp415.98MB
  60. 07 - Seaborn Data Visualizations/006 Categorical Plots - Statistics within Categories - Coding with Seaborn.mp451.65MB
  61. 07 - Seaborn Data Visualizations/007 Categorical Plots - Distributions within Categories - Understanding Plot Types.mp444.96MB
  62. 07 - Seaborn Data Visualizations/008 Categorical Plots - Distributions within Categories - Coding with Seaborn.mp484.57MB
  63. 07 - Seaborn Data Visualizations/009 Seaborn - Comparison Plots - Understanding the Plot Types.mp410.57MB
  64. 07 - Seaborn Data Visualizations/010 Seaborn - Comparison Plots - Coding with Seaborn.mp451.16MB
  65. 07 - Seaborn Data Visualizations/011 Seaborn Grid Plots.mp487.01MB
  66. 07 - Seaborn Data Visualizations/012 Seaborn - Matrix Plots.mp461.47MB
  67. 07 - Seaborn Data Visualizations/013 Seaborn Plot Exercises Overview.mp447.88MB
  68. 07 - Seaborn Data Visualizations/014 Seaborn Plot Exercises Solutions.mp4105.72MB
  69. 08 - Data Analysis and Visualization Capstone Project Exercise/001 Capstone Project Overview.mp431.11MB
  70. 08 - Data Analysis and Visualization Capstone Project Exercise/002 Capstone Project Solutions - Part One.mp4110.61MB
  71. 08 - Data Analysis and Visualization Capstone Project Exercise/003 Capstone Project Solutions - Part Two.mp4106.18MB
  72. 08 - Data Analysis and Visualization Capstone Project Exercise/004 Capstone Project Solutions - Part Three.mp4137.39MB
  73. 09 - Machine Learning Concepts Overview/001 Introduction to Machine Learning Overview Section.mp413.17MB
  74. 09 - Machine Learning Concepts Overview/002 Why Machine Learning_.mp421.04MB
  75. 09 - Machine Learning Concepts Overview/003 Types of Machine Learning Algorithms.mp418.08MB
  76. 09 - Machine Learning Concepts Overview/004 Supervised Machine Learning Process.mp433.53MB
  77. 09 - Machine Learning Concepts Overview/005 Companion Book - Introduction to Statistical Learning.mp45.11MB
  78. 10 - Linear Regression/001 Introduction to Linear Regression Section.mp42.58MB
  79. 10 - Linear Regression/002 Linear Regression - Algorithm History.mp454.82MB
  80. 10 - Linear Regression/003 Linear Regression - Understanding Ordinary Least Squares.mp486.37MB
  81. 10 - Linear Regression/004 Linear Regression - Cost Functions.mp416.63MB
  82. 10 - Linear Regression/005 Linear Regression - Gradient Descent.mp429.21MB
  83. 10 - Linear Regression/006 Python coding Simple Linear Regression.mp470.14MB
  84. 10 - Linear Regression/007 Overview of Scikit-Learn and Python.mp431.44MB
  85. 10 - Linear Regression/008 Linear Regression - Scikit-Learn Train Test Split.mp461.42MB
  86. 10 - Linear Regression/009 Linear Regression - Scikit-Learn Performance Evaluation - Regression.mp453.4MB
  87. 10 - Linear Regression/010 Linear Regression - Residual Plots.mp444.02MB
  88. 10 - Linear Regression/011 Linear Regression - Model Deployment and Coefficient Interpretation.mp481.14MB
  89. 10 - Linear Regression/012 Polynomial Regression - Theory and Motivation.mp422.25MB
  90. 10 - Linear Regression/013 Polynomial Regression - Creating Polynomial Features.mp440.09MB
  91. 10 - Linear Regression/014 Polynomial Regression - Training and Evaluation.mp436.3MB
  92. 10 - Linear Regression/015 Bias Variance Trade-Off.mp436.18MB
  93. 10 - Linear Regression/016 Polynomial Regression - Choosing Degree of Polynomial.mp455.68MB
  94. 10 - Linear Regression/017 Polynomial Regression - Model Deployment.mp423.22MB
  95. 10 - Linear Regression/018 Regularization Overview.mp415.52MB
  96. 10 - Linear Regression/019 Feature Scaling.mp424.34MB
  97. 10 - Linear Regression/020 Introduction to Cross Validation.mp432.97MB
  98. 10 - Linear Regression/021 Regularization Data Setup.mp420.16MB
  99. 10 - Linear Regression/022 L2 Regularization - Ridge Regression Theory.mp461.3MB
  100. 10 - Linear Regression/023 L2 Regularization - Ridge Regression - Python Implementation.mp489.37MB
  101. 10 - Linear Regression/024 L1 Regularization - Lasso Regression - Background and Implementation.mp494.65MB
  102. 10 - Linear Regression/025 L1 and L2 Regularization - Elastic Net.mp466.4MB
  103. 10 - Linear Regression/026 Linear Regression Project - Data Overview.mp416.94MB
  104. 11 - Feature Engineering and Data Preparation/002 Introduction to Feature Engineering and Data Preparation.mp436.11MB
  105. 11 - Feature Engineering and Data Preparation/003 Dealing with Outliers.mp4103.32MB
  106. 11 - Feature Engineering and Data Preparation/004 Dealing with Missing Data _ Part One - Evaluation of Missing Data.mp419.05MB
  107. 11 - Feature Engineering and Data Preparation/005 Dealing with Missing Data _ Part Two - Filling or Dropping data based on Rows.mp4117.56MB
  108. 11 - Feature Engineering and Data Preparation/006 Dealing with Missing Data _ Part 3 - Fixing data based on Columns.mp4105.22MB
  109. 11 - Feature Engineering and Data Preparation/007 Dealing with Categorical Data - Encoding Options.mp458.87MB
  110. 12 - Cross Validation , Grid Search, and the Linear Regression Project/001 Section Overview and Introduction.mp45.61MB
  111. 12 - Cross Validation , Grid Search, and the Linear Regression Project/002 Cross Validation - Test _ Train Split.mp446.86MB
  112. 12 - Cross Validation , Grid Search, and the Linear Regression Project/003 Cross Validation - Test _ Validation _ Train Split.mp459.41MB
  113. 12 - Cross Validation , Grid Search, and the Linear Regression Project/004 Cross Validation - cross_val_score.mp444.46MB
  114. 12 - Cross Validation , Grid Search, and the Linear Regression Project/005 Cross Validation - cross_validate.mp445.01MB
  115. 12 - Cross Validation , Grid Search, and the Linear Regression Project/006 Grid Search.mp473.19MB
  116. 12 - Cross Validation , Grid Search, and the Linear Regression Project/007 Linear Regression Project Overview.mp423.63MB
  117. 12 - Cross Validation , Grid Search, and the Linear Regression Project/008 Linear Regression Project - Solutions.mp491.23MB
  118. 13 - Logistic Regression/002 Introduction to Logistic Regression Section.mp413.93MB
  119. 13 - Logistic Regression/003 Logistic Regression - Theory and Intuition - Part One_ The Logistic Function.mp417.31MB
  120. 13 - Logistic Regression/004 Logistic Regression - Theory and Intuition - Part Two_ Linear to Logistic.mp48.03MB
  121. 13 - Logistic Regression/005 Logistic Regression - Theory and Intuition - Linear to Logistic Math.mp436.04MB
  122. 13 - Logistic Regression/006 Logistic Regression - Theory and Intuition - Best fit with Maximum Likelihood.mp454.91MB
  123. 13 - Logistic Regression/007 Logistic Regression with Scikit-Learn - Part One - EDA.mp462.45MB
  124. 13 - Logistic Regression/008 Logistic Regression with Scikit-Learn - Part Two - Model Training.mp432.57MB
  125. 13 - Logistic Regression/009 Classification Metrics - Confusion Matrix and Accuracy.mp421.72MB
  126. 13 - Logistic Regression/010 Classification Metrics - Precison, Recall, F1-Score.mp433.14MB
  127. 13 - Logistic Regression/011 Classification Metrics - ROC Curves.mp416.07MB
  128. 13 - Logistic Regression/012 Logistic Regression with Scikit-Learn - Part Three - Performance Evaluation.mp457.03MB
  129. 13 - Logistic Regression/013 Multi-Class Classification with Logistic Regression - Part One - Data and EDA.mp437.38MB
  130. 13 - Logistic Regression/014 Multi-Class Classification with Logistic Regression - Part Two - Model.mp4105.09MB
  131. 13 - Logistic Regression/015 Logistic Regression Exercise Project Overview.mp424.29MB
  132. 13 - Logistic Regression/016 Logistic Regression Project Exercise - Solutions.mp4161.29MB
  133. 14 - KNN - K Nearest Neighbors/001 Introduction to KNN Section.mp43.65MB
  134. 14 - KNN - K Nearest Neighbors/002 KNN Classification - Theory and Intuition.mp423.55MB
  135. 14 - KNN - K Nearest Neighbors/003 KNN Coding with Python - Part One.mp461.55MB
  136. 14 - KNN - K Nearest Neighbors/004 KNN Coding with Python - Part Two - Choosing K.mp4102.86MB
  137. 14 - KNN - K Nearest Neighbors/005 KNN Classification Project Exercise Overview.mp421.12MB
  138. 14 - KNN - K Nearest Neighbors/006 KNN Classification Project Exercise Solutions.mp4105.03MB
  139. 15 - Support Vector Machines/001 Introduction to Support Vector Machines.mp42.79MB
  140. 15 - Support Vector Machines/002 History of Support Vector Machines.mp415.54MB
  141. 15 - Support Vector Machines/003 SVM - Theory and Intuition - Hyperplanes and Margins.mp447.74MB
  142. 15 - Support Vector Machines/004 SVM - Theory and Intuition - Kernel Intuition.mp49.83MB
  143. 15 - Support Vector Machines/005 SVM - Theory and Intuition - Kernel Trick and Mathematics.mp452.62MB
  144. 15 - Support Vector Machines/006 SVM with Scikit-Learn and Python - Classification Part One.mp446.28MB
  145. 15 - Support Vector Machines/007 SVM with Scikit-Learn and Python - Classification Part Two.mp490.63MB
  146. 15 - Support Vector Machines/008 SVM with Scikit-Learn and Python - Regression Tasks.mp476.27MB
  147. 15 - Support Vector Machines/009 Support Vector Machine Project Overview.mp434.84MB
  148. 15 - Support Vector Machines/010 Support Vector Machine Project Solutions.mp493.36MB
  149. 16 - Tree Based Methods_ Decision Tree Learning/001 Introduction to Tree Based Methods.mp42.33MB
  150. 16 - Tree Based Methods_ Decision Tree Learning/002 Decision Tree - History.mp435.58MB
  151. 16 - Tree Based Methods_ Decision Tree Learning/003 Decision Tree - Terminology.mp47.29MB
  152. 16 - Tree Based Methods_ Decision Tree Learning/004 Decision Tree - Understanding Gini Impurity.mp419.45MB
  153. 16 - Tree Based Methods_ Decision Tree Learning/005 Constructing Decision Trees with Gini Impurity - Part One.mp417.69MB
  154. 16 - Tree Based Methods_ Decision Tree Learning/006 Constructing Decision Trees with Gini Impurity - Part Two.mp452.35MB
  155. 16 - Tree Based Methods_ Decision Tree Learning/007 Coding Decision Trees - Part One - The Data.mp498.72MB
  156. 16 - Tree Based Methods_ Decision Tree Learning/008 Coding Decision Trees - Part Two -Creating the Model.mp4115.8MB
  157. 17 - Random Forests/001 Introduction to Random Forests Section.mp42.87MB
  158. 17 - Random Forests/002 Random Forests - History and Motivation.mp424MB
  159. 17 - Random Forests/003 Random Forests - Key Hyperparameters.mp48.27MB
  160. 17 - Random Forests/004 Random Forests - Number of Estimators and Features in Subsets.mp427.31MB
  161. 17 - Random Forests/005 Random Forests - Bootstrapping and Out-of-Bag Error.mp432.72MB
  162. 17 - Random Forests/006 Coding Classification with Random Forest Classifier - Part One.mp452.1MB
  163. 17 - Random Forests/007 Coding Classification with Random Forest Classifier - Part Two.mp4130.37MB
  164. 17 - Random Forests/008 Coding Regression with Random Forest Regressor - Part One - Data.mp413.68MB
  165. 17 - Random Forests/009 Coding Regression with Random Forest Regressor - Part Two - Basic Models.mp485.01MB
  166. 17 - Random Forests/010 Coding Regression with Random Forest Regressor - Part Three - Polynomials.mp445.54MB
  167. 17 - Random Forests/011 Coding Regression with Random Forest Regressor - Part Four - Advanced Models.mp450.67MB
  168. 18 - Boosting Methods/001 Introduction to Boosting Section.mp42.99MB
  169. 18 - Boosting Methods/002 Boosting Methods - Motivation and History.mp421.98MB
  170. 18 - Boosting Methods/003 AdaBoost Theory and Intuition.mp441.53MB
  171. 18 - Boosting Methods/004 AdaBoost Coding Part One - The Data.mp442.25MB
  172. 18 - Boosting Methods/005 AdaBoost Coding Part Two - The Model.mp463.11MB
  173. 18 - Boosting Methods/006 Gradient Boosting Theory.mp422.96MB
  174. 18 - Boosting Methods/007 Gradient Boosting Coding Walkthrough.mp457.91MB
  175. 19 - Supervised Learning Capstone Project/001 Introduction to Supervised Learning Capstone Project.mp429.84MB
  176. 19 - Supervised Learning Capstone Project/002 Solution Walkthrough - Supervised Learning Project - Data and EDA.mp4106.1MB
  177. 19 - Supervised Learning Capstone Project/003 Solution Walkthrough - Supervised Learning Project - Cohort Analysis.mp4130.14MB
  178. 19 - Supervised Learning Capstone Project/004 Solution Walkthrough - Supervised Learning Project - Tree Models.mp4114.21MB
  179. 20 - Naive Bayes Classification and Natural Language Processing/001 Introduction to NLP and Naive Bayes Section.mp44.22MB
  180. 20 - Naive Bayes Classification and Natural Language Processing/002 Naive Bayes Algorithm - Part One - Bayes Theorem.mp422.04MB
  181. 20 - Naive Bayes Classification and Natural Language Processing/003 Naive Bayes Algorithm - Part Two - Model Algorithm.mp448.61MB
  182. 20 - Naive Bayes Classification and Natural Language Processing/004 Feature Extraction from Text - Part One - Theory and Intuition.mp429.4MB
  183. 20 - Naive Bayes Classification and Natural Language Processing/005 Feature Extraction from Text - Coding Count Vectorization Manually.mp462.89MB
  184. 20 - Naive Bayes Classification and Natural Language Processing/006 Feature Extraction from Text - Coding with Scikit-Learn.mp450.39MB
  185. 20 - Naive Bayes Classification and Natural Language Processing/007 Natural Language Processing - Classification of Text - Part One.mp428.26MB
  186. 20 - Naive Bayes Classification and Natural Language Processing/008 Natural Language Processing - Classification of Text - Part Two.mp434.77MB
  187. 20 - Naive Bayes Classification and Natural Language Processing/009 Text Classification Project Exercise Overview.mp430.54MB
  188. 20 - Naive Bayes Classification and Natural Language Processing/010 Text Classification Project Exercise Solutions.mp4100.59MB
  189. 21 - Unsupervised Learning/001 Unsupervised Learning Overview.mp413.75MB
  190. 22 - K-Means Clustering/001 Introduction to K-Means Clustering Section.mp43.55MB
  191. 22 - K-Means Clustering/002 Clustering General Overview.mp424.86MB
  192. 22 - K-Means Clustering/003 K-Means Clustering Theory.mp452.49MB
  193. 22 - K-Means Clustering/004 K-Means Clustering - Coding Part One.mp497.9MB
  194. 22 - K-Means Clustering/005 K-Means Clustering Coding Part Two.mp480.85MB
  195. 22 - K-Means Clustering/006 K-Means Clustering Coding Part Three.mp459.77MB
  196. 22 - K-Means Clustering/007 K-Means Color Quantization - Part One.mp480.57MB
  197. 22 - K-Means Clustering/008 K-Means Color Quantization - Part Two.mp465.03MB
  198. 22 - K-Means Clustering/009 K-Means Clustering Exercise Overview.mp459.48MB
  199. 22 - K-Means Clustering/010 K-Means Clustering Exercise Solution - Part One.mp479.92MB
  200. 22 - K-Means Clustering/011 K-Means Clustering Exercise Solution - Part Two.mp4108.19MB
  201. 22 - K-Means Clustering/012 K-Means Clustering Exercise Solution - Part Three.mp462.5MB
  202. 23 - Hierarchical Clustering/001 Introduction to Hierarchical Clustering.mp41.67MB
  203. 23 - Hierarchical Clustering/002 Hierarchical Clustering - Theory and Intuition.mp452.07MB
  204. 23 - Hierarchical Clustering/003 Hierarchical Clustering - Coding Part One - Data and Visualization.mp4114.98MB
  205. 23 - Hierarchical Clustering/004 Hierarchical Clustering - Coding Part Two - Scikit-Learn.mp4209.23MB
  206. 24 - DBSCAN - Density-based spatial clustering of applications with noise/001 Introduction to DBSCAN Section.mp41.8MB
  207. 24 - DBSCAN - Density-based spatial clustering of applications with noise/002 DBSCAN - Theory and Intuition.mp4109.09MB
  208. 24 - DBSCAN - Density-based spatial clustering of applications with noise/003 DBSCAN versus K-Means Clustering.mp466.64MB
  209. 24 - DBSCAN - Density-based spatial clustering of applications with noise/004 DBSCAN - Hyperparameter Theory.mp413.86MB
  210. 24 - DBSCAN - Density-based spatial clustering of applications with noise/005 DBSCAN - Hyperparameter Tuning Methods.mp4105.08MB
  211. 24 - DBSCAN - Density-based spatial clustering of applications with noise/006 DBSCAN - Outlier Project Exercise Overview.mp450.27MB
  212. 24 - DBSCAN - Density-based spatial clustering of applications with noise/007 DBSCAN - Outlier Project Exercise Solutions.mp4127.93MB
  213. 25 - PCA - Principal Component Analysis and Manifold Learning/001 Introduction to Principal Component Analysis.mp45.08MB
  214. 25 - PCA - Principal Component Analysis and Manifold Learning/002 PCA Theory and Intuition - Part One.mp429.72MB
  215. 25 - PCA - Principal Component Analysis and Manifold Learning/003 PCA Theory and Intuition - Part Two.mp419.04MB
  216. 25 - PCA - Principal Component Analysis and Manifold Learning/004 PCA - Manual Implementation in Python.mp495.04MB
  217. 25 - PCA - Principal Component Analysis and Manifold Learning/005 PCA - SciKit-Learn.mp474.09MB
  218. 25 - PCA - Principal Component Analysis and Manifold Learning/006 PCA - Project Exercise Overview.mp452.77MB
  219. 25 - PCA - Principal Component Analysis and Manifold Learning/007 PCA - Project Exercise Solution.mp4119.45MB
  220. 26 - Model Deployment/001 Model Deployment Section Overview.mp44.16MB
  221. 26 - Model Deployment/002 Model Deployment Considerations.mp418.31MB
  222. 26 - Model Deployment/003 Model Persistence.mp4109.76MB
  223. 26 - Model Deployment/004 Model Deployment as an API - General Overview.mp417.48MB
  224. 26 - Model Deployment/006 Model API - Creating the Script.mp467.27MB
  225. 26 - Model Deployment/007 Testing the API.mp433.15MB
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