589689.xyz

GetFreeCourses.Co-Udemy-2022 Python for Machine Learning & Data Science Masterclass

  • 收录时间:2022-08-12 00:51:40
  • 文件大小:11GB
  • 下载次数:1
  • 最近下载:2022-08-12 00:51:40
  • 磁力链接:
立即下载 复制链接

文件列表

  1. 23 - Hierarchical Clustering/004 Hierarchical Clustering - Coding Part Two - Scikit-Learn.mp4 209MB
  2. 05 - Pandas/028 Pandas Project Exercise Solutions.mp4 173MB
  3. 13 - Logistic Regression/016 Logistic Regression Project Exercise - Solutions.mp4 161MB
  4. 08 - Data Analysis and Visualization Capstone Project Exercise/004 Capstone Project Solutions - Part Three.mp4 137MB
  5. 17 - Random Forests/007 Coding Classification with Random Forest Classifier - Part Two.mp4 130MB
  6. 19 - Supervised Learning Capstone Project/003 Solution Walkthrough - Supervised Learning Project - Cohort Analysis.mp4 130MB
  7. 05 - Pandas/026 Pandas Pivot Tables.mp4 129MB
  8. 24 - DBSCAN - Density-based spatial clustering of applications with noise/007 DBSCAN - Outlier Project Exercise Solutions.mp4 128MB
  9. 25 - PCA - Principal Component Analysis and Manifold Learning/007 PCA - Project Exercise Solution.mp4 119MB
  10. 11 - Feature Engineering and Data Preparation/005 Dealing with Missing Data _ Part Two - Filling or Dropping data based on Rows.mp4 118MB
  11. 16 - Tree Based Methods_ Decision Tree Learning/008 Coding Decision Trees - Part Two -Creating the Model.mp4 116MB
  12. 23 - Hierarchical Clustering/003 Hierarchical Clustering - Coding Part One - Data and Visualization.mp4 115MB
  13. 19 - Supervised Learning Capstone Project/004 Solution Walkthrough - Supervised Learning Project - Tree Models.mp4 114MB
  14. 07 - Seaborn Data Visualizations/002 Scatterplots with Seaborn.mp4 111MB
  15. 08 - Data Analysis and Visualization Capstone Project Exercise/002 Capstone Project Solutions - Part One.mp4 111MB
  16. 26 - Model Deployment/003 Model Persistence.mp4 110MB
  17. 24 - DBSCAN - Density-based spatial clustering of applications with noise/002 DBSCAN - Theory and Intuition.mp4 109MB
  18. 22 - K-Means Clustering/011 K-Means Clustering Exercise Solution - Part Two.mp4 108MB
  19. 08 - Data Analysis and Visualization Capstone Project Exercise/003 Capstone Project Solutions - Part Two.mp4 106MB
  20. 19 - Supervised Learning Capstone Project/002 Solution Walkthrough - Supervised Learning Project - Data and EDA.mp4 106MB
  21. 06 - Matplotlib/011 Matplotlib Exercise Questions - Solutions.mp4 106MB
  22. 07 - Seaborn Data Visualizations/014 Seaborn Plot Exercises Solutions.mp4 106MB
  23. 11 - Feature Engineering and Data Preparation/006 Dealing with Missing Data _ Part 3 - Fixing data based on Columns.mp4 105MB
  24. 13 - Logistic Regression/014 Multi-Class Classification with Logistic Regression - Part Two - Model.mp4 105MB
  25. 24 - DBSCAN - Density-based spatial clustering of applications with noise/005 DBSCAN - Hyperparameter Tuning Methods.mp4 105MB
  26. 14 - KNN - K Nearest Neighbors/006 KNN Classification Project Exercise Solutions.mp4 105MB
  27. 11 - Feature Engineering and Data Preparation/003 Dealing with Outliers.mp4 103MB
  28. 14 - KNN - K Nearest Neighbors/004 KNN Coding with Python - Part Two - Choosing K.mp4 103MB
  29. 05 - Pandas/023 Pandas Input and Output - HTML Tables.mp4 102MB
  30. 20 - Naive Bayes Classification and Natural Language Processing/010 Text Classification Project Exercise Solutions.mp4 101MB
  31. 04 - NumPy/002 NumPy Arrays.mp4 99MB
  32. 16 - Tree Based Methods_ Decision Tree Learning/007 Coding Decision Trees - Part One - The Data.mp4 99MB
  33. 22 - K-Means Clustering/004 K-Means Clustering - Coding Part One.mp4 98MB
  34. 05 - Pandas/004 DataFrames - Part One - Creating a DataFrame.mp4 97MB
  35. 06 - Matplotlib/006 Matplotlib - Subplots Functionality.mp4 97MB
  36. 05 - Pandas/025 Pandas Input and Output - SQL Databases.mp4 96MB
  37. 25 - PCA - Principal Component Analysis and Manifold Learning/004 PCA - Manual Implementation in Python.mp4 95MB
  38. 10 - Linear Regression/024 L1 Regularization - Lasso Regression - Background and Implementation.mp4 95MB
  39. 15 - Support Vector Machines/010 Support Vector Machine Project Solutions.mp4 93MB
  40. 05 - Pandas/015 GroupBy Operations - Part Two - MultiIndex.mp4 93MB
  41. 12 - Cross Validation , Grid Search, and the Linear Regression Project/008 Linear Regression Project - Solutions.mp4 91MB
  42. 15 - Support Vector Machines/007 SVM with Scikit-Learn and Python - Classification Part Two.mp4 91MB
  43. 10 - Linear Regression/023 L2 Regularization - Ridge Regression - Python Implementation.mp4 89MB
  44. 07 - Seaborn Data Visualizations/011 Seaborn Grid Plots.mp4 87MB
  45. 05 - Pandas/014 GroupBy Operations - Part One.mp4 87MB
  46. 10 - Linear Regression/003 Linear Regression - Understanding Ordinary Least Squares.mp4 86MB
  47. 05 - Pandas/010 Pandas - Useful Methods - Apply on Multiple Columns.mp4 85MB
  48. 17 - Random Forests/009 Coding Regression with Random Forest Regressor - Part Two - Basic Models.mp4 85MB
  49. 07 - Seaborn Data Visualizations/008 Categorical Plots - Distributions within Categories - Coding with Seaborn.mp4 85MB
  50. 01 - Introduction to Course/003 Anaconda Python and Jupyter Install and Setup.mp4 85MB
  51. 05 - Pandas/006 DataFrames - Part Three - Working with Columns.mp4 84MB
  52. 10 - Linear Regression/011 Linear Regression - Model Deployment and Coefficient Interpretation.mp4 81MB
  53. 22 - K-Means Clustering/005 K-Means Clustering Coding Part Two.mp4 81MB
  54. 22 - K-Means Clustering/007 K-Means Color Quantization - Part One.mp4 81MB
  55. 05 - Pandas/021 Pandas - Time Methods for Date and Time Data.mp4 80MB
  56. 22 - K-Means Clustering/010 K-Means Clustering Exercise Solution - Part One.mp4 80MB
  57. 15 - Support Vector Machines/008 SVM with Scikit-Learn and Python - Regression Tasks.mp4 76MB
  58. 05 - Pandas/011 Pandas - Useful Methods - Statistical Information and Sorting.mp4 74MB
  59. 25 - PCA - Principal Component Analysis and Manifold Learning/005 PCA - SciKit-Learn.mp4 74MB
  60. 05 - Pandas/013 Missing Data - Pandas Operations.mp4 74MB
  61. 12 - Cross Validation , Grid Search, and the Linear Regression Project/006 Grid Search.mp4 73MB
  62. 05 - Pandas/007 DataFrames - Part Four - Working with Rows.mp4 73MB
  63. 10 - Linear Regression/006 Python coding Simple Linear Regression.mp4 70MB
  64. 05 - Pandas/008 Pandas - Conditional Filtering.mp4 69MB
  65. 26 - Model Deployment/006 Model API - Creating the Script.mp4 67MB
  66. 01 - Introduction to Course/33985574-UNZIP-FOR-NOTEBOOKS-FINAL.zip 67MB
  67. 01 - Introduction to Course/33985614-UNZIP-FOR-NOTEBOOKS-FINAL.zip 67MB
  68. 24 - DBSCAN - Density-based spatial clustering of applications with noise/003 DBSCAN versus K-Means Clustering.mp4 67MB
  69. 10 - Linear Regression/025 L1 and L2 Regularization - Elastic Net.mp4 66MB
  70. 22 - K-Means Clustering/008 K-Means Color Quantization - Part Two.mp4 65MB
  71. 18 - Boosting Methods/005 AdaBoost Coding Part Two - The Model.mp4 63MB
  72. 22 - K-Means Clustering/012 K-Means Clustering Exercise Solution - Part Three.mp4 63MB
  73. 13 - Logistic Regression/007 Logistic Regression with Scikit-Learn - Part One - EDA.mp4 62MB
  74. 14 - KNN - K Nearest Neighbors/003 KNN Coding with Python - Part One.mp4 62MB
  75. 07 - Seaborn Data Visualizations/012 Seaborn - Matrix Plots.mp4 61MB
  76. 10 - Linear Regression/008 Linear Regression - Scikit-Learn Train Test Split.mp4 61MB
  77. 10 - Linear Regression/022 L2 Regularization - Ridge Regression Theory.mp4 61MB
  78. 22 - K-Means Clustering/006 K-Means Clustering Coding Part Three.mp4 60MB
  79. 22 - K-Means Clustering/009 K-Means Clustering Exercise Overview.mp4 59MB
  80. 12 - Cross Validation , Grid Search, and the Linear Regression Project/003 Cross Validation - Test _ Validation _ Train Split.mp4 59MB
  81. 07 - Seaborn Data Visualizations/004 Distribution Plots - Part Two - Coding with Seaborn.mp4 59MB
  82. 11 - Feature Engineering and Data Preparation/007 Dealing with Categorical Data - Encoding Options.mp4 59MB
  83. 18 - Boosting Methods/007 Gradient Boosting Coding Walkthrough.mp4 58MB
  84. 02 - OPTIONAL_ Python Crash Course/003 Python Crash Course - Part Two.mp4 58MB
  85. 13 - Logistic Regression/012 Logistic Regression with Scikit-Learn - Part Three - Performance Evaluation.mp4 57MB
  86. 10 - Linear Regression/016 Polynomial Regression - Choosing Degree of Polynomial.mp4 56MB
  87. 13 - Logistic Regression/006 Logistic Regression - Theory and Intuition - Best fit with Maximum Likelihood.mp4 55MB
  88. 10 - Linear Regression/002 Linear Regression - Algorithm History.mp4 55MB
  89. 05 - Pandas/009 Pandas - Useful Methods - Apply on Single Column.mp4 54MB
  90. 10 - Linear Regression/009 Linear Regression - Scikit-Learn Performance Evaluation - Regression.mp4 53MB
  91. 25 - PCA - Principal Component Analysis and Manifold Learning/006 PCA - Project Exercise Overview.mp4 53MB
  92. 15 - Support Vector Machines/005 SVM - Theory and Intuition - Kernel Trick and Mathematics.mp4 53MB
  93. 22 - K-Means Clustering/003 K-Means Clustering Theory.mp4 52MB
  94. 16 - Tree Based Methods_ Decision Tree Learning/006 Constructing Decision Trees with Gini Impurity - Part Two.mp4 52MB
  95. 17 - Random Forests/006 Coding Classification with Random Forest Classifier - Part One.mp4 52MB
  96. 23 - Hierarchical Clustering/002 Hierarchical Clustering - Theory and Intuition.mp4 52MB
  97. 07 - Seaborn Data Visualizations/006 Categorical Plots - Statistics within Categories - Coding with Seaborn.mp4 52MB
  98. 07 - Seaborn Data Visualizations/010 Seaborn - Comparison Plots - Coding with Seaborn.mp4 51MB
  99. 17 - Random Forests/011 Coding Regression with Random Forest Regressor - Part Four - Advanced Models.mp4 51MB
  100. 20 - Naive Bayes Classification and Natural Language Processing/006 Feature Extraction from Text - Coding with Scikit-Learn.mp4 50MB
  101. 24 - DBSCAN - Density-based spatial clustering of applications with noise/006 DBSCAN - Outlier Project Exercise Overview.mp4 50MB
  102. 06 - Matplotlib/010 Matplotlib Exercise Questions Overview.mp4 49MB
  103. 02 - OPTIONAL_ Python Crash Course/006 Python Crash Course - Exercise Solutions.mp4 49MB
  104. 20 - Naive Bayes Classification and Natural Language Processing/003 Naive Bayes Algorithm - Part Two - Model Algorithm.mp4 49MB
  105. 07 - Seaborn Data Visualizations/013 Seaborn Plot Exercises Overview.mp4 48MB
  106. 15 - Support Vector Machines/003 SVM - Theory and Intuition - Hyperplanes and Margins.mp4 48MB
  107. 12 - Cross Validation , Grid Search, and the Linear Regression Project/002 Cross Validation - Test _ Train Split.mp4 47MB
  108. 15 - Support Vector Machines/006 SVM with Scikit-Learn and Python - Classification Part One.mp4 46MB
  109. 17 - Random Forests/010 Coding Regression with Random Forest Regressor - Part Three - Polynomials.mp4 46MB
  110. 05 - Pandas/020 Pandas - Text Methods for String Data.mp4 45MB
  111. 12 - Cross Validation , Grid Search, and the Linear Regression Project/005 Cross Validation - cross_validate.mp4 45MB
  112. 07 - Seaborn Data Visualizations/007 Categorical Plots - Distributions within Categories - Understanding Plot Types.mp4 45MB
  113. 12 - Cross Validation , Grid Search, and the Linear Regression Project/004 Cross Validation - cross_val_score.mp4 44MB
  114. 06 - Matplotlib/008 Matplotlib Styling - Colors and Styles.mp4 44MB
  115. 10 - Linear Regression/010 Linear Regression - Residual Plots.mp4 44MB
  116. 18 - Boosting Methods/004 AdaBoost Coding Part One - The Data.mp4 42MB
  117. 18 - Boosting Methods/003 AdaBoost Theory and Intuition.mp4 42MB
  118. 05 - Pandas/005 DataFrames - Part Two - Basic Properties.mp4 40MB
  119. 05 - Pandas/017 Combining DataFrames - Inner Merge.mp4 40MB
  120. 10 - Linear Regression/013 Polynomial Regression - Creating Polynomial Features.mp4 40MB
  121. 04 - NumPy/003 NumPy Indexing and Selection.mp4 40MB
  122. 05 - Pandas/027 Pandas Project Exercise Overview.mp4 39MB
  123. 13 - Logistic Regression/013 Multi-Class Classification with Logistic Regression - Part One - Data and EDA.mp4 37MB
  124. 05 - Pandas/022 Pandas Input and Output - CSV Files.mp4 37MB
  125. 05 - Pandas/016 Combining DataFrames - Concatenation.mp4 37MB
  126. 10 - Linear Regression/014 Polynomial Regression - Training and Evaluation.mp4 36MB
  127. 10 - Linear Regression/015 Bias Variance Trade-Off.mp4 36MB
  128. 11 - Feature Engineering and Data Preparation/002 Introduction to Feature Engineering and Data Preparation.mp4 36MB
  129. 04 - NumPy/004 NumPy Operations.mp4 36MB
  130. 13 - Logistic Regression/005 Logistic Regression - Theory and Intuition - Linear to Logistic Math.mp4 36MB
  131. 01 - Introduction to Course/005 Environment Setup.mp4 36MB
  132. 16 - Tree Based Methods_ Decision Tree Learning/002 Decision Tree - History.mp4 36MB
  133. 04 - NumPy/006 Numpy Exercises - Solutions.mp4 35MB
  134. 06 - Matplotlib/004 Matplotlib - Implementing Figures and Axes.mp4 35MB
  135. 15 - Support Vector Machines/009 Support Vector Machine Project Overview.mp4 35MB
  136. 20 - Naive Bayes Classification and Natural Language Processing/008 Natural Language Processing - Classification of Text - Part Two.mp4 35MB
  137. 09 - Machine Learning Concepts Overview/004 Supervised Machine Learning Process.mp4 34MB
  138. 26 - Model Deployment/007 Testing the API.mp4 33MB
  139. 13 - Logistic Regression/010 Classification Metrics - Precison, Recall, F1-Score.mp4 33MB
  140. 10 - Linear Regression/020 Introduction to Cross Validation.mp4 33MB
  141. 17 - Random Forests/005 Random Forests - Bootstrapping and Out-of-Bag Error.mp4 33MB
  142. 13 - Logistic Regression/008 Logistic Regression with Scikit-Learn - Part Two - Model Training.mp4 33MB
  143. 02 - OPTIONAL_ Python Crash Course/004 Python Crash Course - Part Three.mp4 32MB
  144. 10 - Linear Regression/007 Overview of Scikit-Learn and Python.mp4 31MB
  145. 08 - Data Analysis and Visualization Capstone Project Exercise/001 Capstone Project Overview.mp4 31MB
  146. 06 - Matplotlib/002 Matplotlib Basics.mp4 31MB
  147. 20 - Naive Bayes Classification and Natural Language Processing/009 Text Classification Project Exercise Overview.mp4 31MB
  148. 19 - Supervised Learning Capstone Project/001 Introduction to Supervised Learning Capstone Project.mp4 30MB
  149. 02 - OPTIONAL_ Python Crash Course/002 Python Crash Course - Part One.mp4 30MB
  150. 25 - PCA - Principal Component Analysis and Manifold Learning/002 PCA Theory and Intuition - Part One.mp4 30MB
  151. 20 - Naive Bayes Classification and Natural Language Processing/004 Feature Extraction from Text - Part One - Theory and Intuition.mp4 29MB
  152. 10 - Linear Regression/005 Linear Regression - Gradient Descent.mp4 29MB
  153. 05 - Pandas/002 Series - Part One.mp4 29MB
  154. 20 - Naive Bayes Classification and Natural Language Processing/007 Natural Language Processing - Classification of Text - Part One.mp4 28MB
  155. 17 - Random Forests/004 Random Forests - Number of Estimators and Features in Subsets.mp4 27MB
  156. 05 - Pandas/012 Missing Data - Overview.mp4 27MB
  157. 05 - Pandas/003 Series - Part Two.mp4 26MB
  158. 05 - Pandas/024 Pandas Input and Output - Excel Files.mp4 26MB
  159. 06 - Matplotlib/009 Advanced Matplotlib Commands (Optional).mp4 25MB
  160. 22 - K-Means Clustering/002 Clustering General Overview.mp4 25MB
  161. 10 - Linear Regression/019 Feature Scaling.mp4 24MB
  162. 13 - Logistic Regression/015 Logistic Regression Exercise Project Overview.mp4 24MB
  163. 17 - Random Forests/002 Random Forests - History and Motivation.mp4 24MB
  164. 12 - Cross Validation , Grid Search, and the Linear Regression Project/007 Linear Regression Project Overview.mp4 24MB
  165. 14 - KNN - K Nearest Neighbors/002 KNN Classification - Theory and Intuition.mp4 24MB
  166. 10 - Linear Regression/017 Polynomial Regression - Model Deployment.mp4 23MB
  167. 18 - Boosting Methods/006 Gradient Boosting Theory.mp4 23MB
  168. 10 - Linear Regression/012 Polynomial Regression - Theory and Motivation.mp4 22MB
  169. 05 - Pandas/019 Combining DataFrames - Outer Merge.mp4 22MB
  170. 20 - Naive Bayes Classification and Natural Language Processing/002 Naive Bayes Algorithm - Part One - Bayes Theorem.mp4 22MB
  171. 18 - Boosting Methods/002 Boosting Methods - Motivation and History.mp4 22MB
  172. 13 - Logistic Regression/009 Classification Metrics - Confusion Matrix and Accuracy.mp4 22MB
  173. 14 - KNN - K Nearest Neighbors/005 KNN Classification Project Exercise Overview.mp4 21MB
  174. 09 - Machine Learning Concepts Overview/002 Why Machine Learning_.mp4 21MB
  175. 10 - Linear Regression/021 Regularization Data Setup.mp4 20MB
  176. 16 - Tree Based Methods_ Decision Tree Learning/004 Decision Tree - Understanding Gini Impurity.mp4 19MB
  177. 11 - Feature Engineering and Data Preparation/004 Dealing with Missing Data _ Part One - Evaluation of Missing Data.mp4 19MB
  178. 25 - PCA - Principal Component Analysis and Manifold Learning/003 PCA Theory and Intuition - Part Two.mp4 19MB
  179. 26 - Model Deployment/002 Model Deployment Considerations.mp4 18MB
  180. 09 - Machine Learning Concepts Overview/003 Types of Machine Learning Algorithms.mp4 18MB
  181. 16 - Tree Based Methods_ Decision Tree Learning/005 Constructing Decision Trees with Gini Impurity - Part One.mp4 18MB
  182. 26 - Model Deployment/004 Model Deployment as an API - General Overview.mp4 17MB
  183. 13 - Logistic Regression/003 Logistic Regression - Theory and Intuition - Part One_ The Logistic Function.mp4 17MB
  184. 10 - Linear Regression/026 Linear Regression Project - Data Overview.mp4 17MB
  185. 10 - Linear Regression/004 Linear Regression - Cost Functions.mp4 17MB
  186. 05 - Pandas/018 Combining DataFrames - Left and Right Merge.mp4 16MB
  187. 06 - Matplotlib/007 Matplotlib Styling - Legends.mp4 16MB
  188. 13 - Logistic Regression/011 Classification Metrics - ROC Curves.mp4 16MB
  189. 07 - Seaborn Data Visualizations/005 Categorical Plots - Statistics within Categories - Understanding Plot Types.mp4 16MB
  190. 15 - Support Vector Machines/002 History of Support Vector Machines.mp4 16MB
  191. 10 - Linear Regression/018 Regularization Overview.mp4 16MB
  192. 07 - Seaborn Data Visualizations/003 Distribution Plots - Part One - Understanding Plot Types.mp4 15MB
  193. 03 - Machine Learning Pathway Overview/001 Machine Learning Pathway.mp4 14MB
  194. 13 - Logistic Regression/002 Introduction to Logistic Regression Section.mp4 14MB
  195. 24 - DBSCAN - Density-based spatial clustering of applications with noise/004 DBSCAN - Hyperparameter Theory.mp4 14MB
  196. 21 - Unsupervised Learning/001 Unsupervised Learning Overview.mp4 14MB
  197. 17 - Random Forests/008 Coding Regression with Random Forest Regressor - Part One - Data.mp4 14MB
  198. 09 - Machine Learning Concepts Overview/001 Introduction to Machine Learning Overview Section.mp4 13MB
  199. 06 - Matplotlib/005 Matplotlib - Figure Parameters.mp4 13MB
  200. 06 - Matplotlib/003 Matplotlib - Understanding the Figure Object.mp4 12MB
  201. 07 - Seaborn Data Visualizations/009 Seaborn - Comparison Plots - Understanding the Plot Types.mp4 11MB
  202. 15 - Support Vector Machines/004 SVM - Theory and Intuition - Kernel Intuition.mp4 10MB
  203. 04 - NumPy/005 NumPy Exercises.mp4 10MB
  204. 17 - Random Forests/003 Random Forests - Key Hyperparameters.mp4 8MB
  205. 13 - Logistic Regression/004 Logistic Regression - Theory and Intuition - Part Two_ Linear to Logistic.mp4 8MB
  206. 16 - Tree Based Methods_ Decision Tree Learning/003 Decision Tree - Terminology.mp4 7MB
  207. 20 - Naive Bayes Classification and Natural Language Processing/31640132-moviereviews.csv 7MB
  208. 01 - Introduction to Course/002 COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP_.mp4 7MB
  209. 19 - Supervised Learning Capstone Project/31389398-17-Supervised-Learning-Capstone-Project.zip 7MB
  210. 05 - Pandas/001 Introduction to Pandas.mp4 7MB
  211. 06 - Matplotlib/001 Introduction to Matplotlib.mp4 7MB
  212. 22 - K-Means Clustering/32407448-20-Kmeans-Clustering.zip 6MB
  213. 07 - Seaborn Data Visualizations/001 Introduction to Seaborn.mp4 6MB
  214. 12 - Cross Validation , Grid Search, and the Linear Regression Project/001 Section Overview and Introduction.mp4 6MB
  215. 09 - Machine Learning Concepts Overview/005 Companion Book - Introduction to Statistical Learning.mp4 5MB
  216. 25 - PCA - Principal Component Analysis and Manifold Learning/001 Introduction to Principal Component Analysis.mp4 5MB
  217. 22 - K-Means Clustering/32407452-bank-full.csv 5MB
  218. 20 - Naive Bayes Classification and Natural Language Processing/001 Introduction to NLP and Naive Bayes Section.mp4 4MB
  219. 26 - Model Deployment/001 Model Deployment Section Overview.mp4 4MB
  220. 25 - PCA - Principal Component Analysis and Manifold Learning/33912220-23-PCA-Principal-Component-Analysis.zip 4MB
  221. 17 - Random Forests/30930956-15-Random-Forests.zip 4MB
  222. 14 - KNN - K Nearest Neighbors/001 Introduction to KNN Section.mp4 4MB
  223. 22 - K-Means Clustering/001 Introduction to K-Means Clustering Section.mp4 4MB
  224. 24 - DBSCAN - Density-based spatial clustering of applications with noise/33643014-22-DBSCAN.zip 4MB
  225. 02 - OPTIONAL_ Python Crash Course/005 Python Crash Course - Exercise Questions.mp4 3MB
  226. 04 - NumPy/001 Introduction to NumPy.mp4 3MB
  227. 20 - Naive Bayes Classification and Natural Language Processing/31640102-airline-tweets.csv 3MB
  228. 18 - Boosting Methods/001 Introduction to Boosting Section.mp4 3MB
  229. 17 - Random Forests/001 Introduction to Random Forests Section.mp4 3MB
  230. 15 - Support Vector Machines/001 Introduction to Support Vector Machines.mp4 3MB
  231. 10 - Linear Regression/001 Introduction to Linear Regression Section.mp4 3MB
  232. 16 - Tree Based Methods_ Decision Tree Learning/001 Introduction to Tree Based Methods.mp4 2MB
  233. 13 - Logistic Regression/29304858-11-Logistic-Regression-Models.zip 2MB
  234. 24 - DBSCAN - Density-based spatial clustering of applications with noise/001 Introduction to DBSCAN Section.mp4 2MB
  235. 16 - Tree Based Methods_ Decision Tree Learning/30205020-14-Decision-Trees.zip 2MB
  236. 23 - Hierarchical Clustering/001 Introduction to Hierarchical Clustering.mp4 2MB
  237. 15 - Support Vector Machines/29902052-13-Support-Vector-Machines.zip 2MB
  238. 14 - KNN - K Nearest Neighbors/29434428-12-K-Nearest-Neighbors.zip 1MB
  239. 19 - Supervised Learning Capstone Project/31389400-Telco-Customer-Churn.csv 954KB
  240. 18 - Boosting Methods/31286608-16-Boosted-Trees.zip 918KB
  241. 23 - Hierarchical Clustering/33028500-21-Hierarchical-Clustering.zip 622KB
  242. 25 - PCA - Principal Component Analysis and Manifold Learning/33912190-digits.csv 486KB
  243. 18 - Boosting Methods/31286610-mushrooms.csv 365KB
  244. 20 - Naive Bayes Classification and Natural Language Processing/31640094-18-Naive-Bayes-and-NLP.zip 192KB
  245. 22 - K-Means Clustering/33555798-palm-trees.jpg 173KB
  246. 25 - PCA - Principal Component Analysis and Manifold Learning/33912194-cancer-tumor-data-features.csv 118KB
  247. 24 - DBSCAN - Density-based spatial clustering of applications with noise/33643060-cluster-circles.csv 60KB
  248. 24 - DBSCAN - Density-based spatial clustering of applications with noise/33643082-cluster-moons.csv 59KB
  249. 24 - DBSCAN - Density-based spatial clustering of applications with noise/33643080-cluster-blobs.csv 56KB
  250. 17 - Random Forests/30930966-data-banknote-authentication.csv 45KB
  251. 23 - Hierarchical Clustering/004 Hierarchical Clustering - Coding Part Two - Scikit-Learn__en.srt 42KB
  252. 11 - Feature Engineering and Data Preparation/003 Dealing with Outliers__en.srt 41KB
  253. 05 - Pandas/028 Pandas Project Exercise Solutions__en.srt 39KB
  254. 19 - Supervised Learning Capstone Project/003 Solution Walkthrough - Supervised Learning Project - Cohort Analysis__en.srt 39KB
  255. 24 - DBSCAN - Density-based spatial clustering of applications with noise/33643070-cluster-two-blobs-outliers.csv 38KB
  256. 24 - DBSCAN - Density-based spatial clustering of applications with noise/33643072-cluster-two-blobs.csv 38KB
  257. 24 - DBSCAN - Density-based spatial clustering of applications with noise/007 DBSCAN - Outlier Project Exercise Solutions__en.srt 38KB
  258. 11 - Feature Engineering and Data Preparation/006 Dealing with Missing Data _ Part 3 - Fixing data based on Columns__en.srt 37KB
  259. 22 - K-Means Clustering/32407456-CIA-Country-Facts.csv 33KB
  260. 16 - Tree Based Methods_ Decision Tree Learning/008 Coding Decision Trees - Part Two -Creating the Model__en.srt 33KB
  261. 24 - DBSCAN - Density-based spatial clustering of applications with noise/005 DBSCAN - Hyperparameter Tuning Methods__en.srt 33KB
  262. 05 - Pandas/026 Pandas Pivot Tables__en.srt 32KB
  263. 04 - NumPy/002 NumPy Arrays__en.srt 32KB
  264. 05 - Pandas/021 Pandas - Time Methods for Date and Time Data__en.srt 32KB
  265. 11 - Feature Engineering and Data Preparation/005 Dealing with Missing Data _ Part Two - Filling or Dropping data based on Rows__en.srt 31KB
  266. 13 - Logistic Regression/016 Logistic Regression Project Exercise - Solutions_en.vtt 31KB
  267. 08 - Data Analysis and Visualization Capstone Project Exercise/004 Capstone Project Solutions - Part Three__en.srt 31KB
  268. 14 - KNN - K Nearest Neighbors/004 KNN Coding with Python - Part Two - Choosing K_en.vtt 31KB
  269. 22 - K-Means Clustering/004 K-Means Clustering - Coding Part One__en.srt 30KB
  270. 07 - Seaborn Data Visualizations/002 Scatterplots with Seaborn__en.srt 30KB
  271. 19 - Supervised Learning Capstone Project/002 Solution Walkthrough - Supervised Learning Project - Data and EDA__en.srt 30KB
  272. 05 - Pandas/025 Pandas Input and Output - SQL Databases__en.srt 29KB
  273. 19 - Supervised Learning Capstone Project/004 Solution Walkthrough - Supervised Learning Project - Tree Models_en.vtt 29KB
  274. 15 - Support Vector Machines/005 SVM - Theory and Intuition - Kernel Trick and Mathematics__en.srt 29KB
  275. 16 - Tree Based Methods_ Decision Tree Learning/007 Coding Decision Trees - Part One - The Data__en.srt 29KB
  276. 05 - Pandas/004 DataFrames - Part One - Creating a DataFrame__en.srt 29KB
  277. 18 - Boosting Methods/003 AdaBoost Theory and Intuition__en.srt 29KB
  278. 06 - Matplotlib/006 Matplotlib - Subplots Functionality__en.srt 29KB
  279. 07 - Seaborn Data Visualizations/008 Categorical Plots - Distributions within Categories - Coding with Seaborn__en.srt 28KB
  280. 10 - Linear Regression/006 Python coding Simple Linear Regression__en.srt 28KB
  281. 26 - Model Deployment/003 Model Persistence_en.vtt 28KB
  282. 17 - Random Forests/007 Coding Classification with Random Forest Classifier - Part Two_en.vtt 28KB
  283. 05 - Pandas/013 Missing Data - Pandas Operations__en.srt 27KB
  284. 05 - Pandas/008 Pandas - Conditional Filtering__en.srt 27KB
  285. 08 - Data Analysis and Visualization Capstone Project Exercise/002 Capstone Project Solutions - Part One__en.srt 27KB
  286. 18 - Boosting Methods/005 AdaBoost Coding Part Two - The Model__en.srt 27KB
  287. 22 - K-Means Clustering/005 K-Means Clustering Coding Part Two__en.srt 27KB
  288. 24 - DBSCAN - Density-based spatial clustering of applications with noise/002 DBSCAN - Theory and Intuition__en.srt 27KB
  289. 20 - Naive Bayes Classification and Natural Language Processing/003 Naive Bayes Algorithm - Part Two - Model Algorithm__en.srt 26KB
  290. 25 - PCA - Principal Component Analysis and Manifold Learning/004 PCA - Manual Implementation in Python__en.srt 26KB
  291. 15 - Support Vector Machines/008 SVM with Scikit-Learn and Python - Regression Tasks_en.vtt 26KB
  292. 26 - Model Deployment/006 Model API - Creating the Script__en.srt 26KB
  293. 05 - Pandas/010 Pandas - Useful Methods - Apply on Multiple Columns__en.srt 26KB
  294. 25 - PCA - Principal Component Analysis and Manifold Learning/007 PCA - Project Exercise Solution__en.srt 26KB
  295. 19 - Supervised Learning Capstone Project/001 Introduction to Supervised Learning Capstone Project__en.srt 26KB
  296. 15 - Support Vector Machines/008 SVM with Scikit-Learn and Python - Regression Tasks__en.srt 26KB
  297. 10 - Linear Regression/011 Linear Regression - Model Deployment and Coefficient Interpretation__en.srt 26KB
  298. 23 - Hierarchical Clustering/003 Hierarchical Clustering - Coding Part One - Data and Visualization__en.srt 25KB
  299. 13 - Logistic Regression/005 Logistic Regression - Theory and Intuition - Linear to Logistic Math__en.srt 25KB
  300. 07 - Seaborn Data Visualizations/004 Distribution Plots - Part Two - Coding with Seaborn__en.srt 25KB
  301. 02 - OPTIONAL_ Python Crash Course/002 Python Crash Course - Part One__en.srt 25KB
  302. 06 - Matplotlib/011 Matplotlib Exercise Questions - Solutions__en.srt 25KB
  303. 11 - Feature Engineering and Data Preparation/002 Introduction to Feature Engineering and Data Preparation__en.srt 24KB
  304. 05 - Pandas/020 Pandas - Text Methods for String Data__en.srt 24KB
  305. 13 - Logistic Regression/014 Multi-Class Classification with Logistic Regression - Part Two - Model__en.srt 24KB
  306. 10 - Linear Regression/008 Linear Regression - Scikit-Learn Train Test Split__en.srt 24KB
  307. 22 - K-Means Clustering/011 K-Means Clustering Exercise Solution - Part Two__en.srt 24KB
  308. 08 - Data Analysis and Visualization Capstone Project Exercise/003 Capstone Project Solutions - Part Two__en.srt 23KB
  309. 13 - Logistic Regression/012 Logistic Regression with Scikit-Learn - Part Three - Performance Evaluation__en.srt 23KB
  310. 05 - Pandas/011 Pandas - Useful Methods - Statistical Information and Sorting__en.srt 23KB
  311. 10 - Linear Regression/009 Linear Regression - Scikit-Learn Performance Evaluation - Regression__en.srt 23KB
  312. 10 - Linear Regression/023 L2 Regularization - Ridge Regression - Python Implementation_en.vtt 23KB
  313. 13 - Logistic Regression/006 Logistic Regression - Theory and Intuition - Best fit with Maximum Likelihood__en.srt 23KB
  314. 10 - Linear Regression/025 L1 and L2 Regularization - Elastic Net_en.vtt 23KB
  315. 10 - Linear Regression/003 Linear Regression - Understanding Ordinary Least Squares__en.srt 23KB
  316. 15 - Support Vector Machines/010 Support Vector Machine Project Solutions_en.vtt 23KB
  317. 07 - Seaborn Data Visualizations/014 Seaborn Plot Exercises Solutions__en.srt 22KB
  318. 05 - Pandas/023 Pandas Input and Output - HTML Tables__en.srt 22KB
  319. 13 - Logistic Regression/007 Logistic Regression with Scikit-Learn - Part One - EDA__en.srt 22KB
  320. 12 - Cross Validation , Grid Search, and the Linear Regression Project/003 Cross Validation - Test _ Validation _ Train Split__en.srt 22KB
  321. 01 - Introduction to Course/003 Anaconda Python and Jupyter Install and Setup__en.srt 22KB
  322. 05 - Pandas/014 GroupBy Operations - Part One__en.srt 21KB
  323. 22 - K-Means Clustering/006 K-Means Clustering Coding Part Three__en.srt 21KB
  324. 20 - Naive Bayes Classification and Natural Language Processing/010 Text Classification Project Exercise Solutions_en.vtt 21KB
  325. 22 - K-Means Clustering/008 K-Means Color Quantization - Part Two__en.srt 21KB
  326. 22 - K-Means Clustering/010 K-Means Clustering Exercise Solution - Part One__en.srt 21KB
  327. 07 - Seaborn Data Visualizations/012 Seaborn - Matrix Plots__en.srt 21KB
  328. 05 - Pandas/007 DataFrames - Part Four - Working with Rows__en.srt 21KB
  329. 06 - Matplotlib/008 Matplotlib Styling - Colors and Styles__en.srt 21KB
  330. 15 - Support Vector Machines/007 SVM with Scikit-Learn and Python - Classification Part Two_en.vtt 21KB
  331. 06 - Matplotlib/004 Matplotlib - Implementing Figures and Axes__en.srt 21KB
  332. 05 - Pandas/015 GroupBy Operations - Part Two - MultiIndex__en.srt 21KB
  333. 23 - Hierarchical Clustering/33028506-cluster-mpg.csv 21KB
  334. 15 - Support Vector Machines/007 SVM with Scikit-Learn and Python - Classification Part Two__en.srt 21KB
  335. 10 - Linear Regression/022 L2 Regularization - Ridge Regression Theory__en.srt 21KB
  336. 05 - Pandas/006 DataFrames - Part Three - Working with Columns__en.srt 21KB
  337. 08 - Data Analysis and Visualization Capstone Project Exercise/001 Capstone Project Overview__en.srt 21KB
  338. 07 - Seaborn Data Visualizations/011 Seaborn Grid Plots__en.srt 20KB
  339. 17 - Random Forests/009 Coding Regression with Random Forest Regressor - Part Two - Basic Models__en.srt 20KB
  340. 22 - K-Means Clustering/007 K-Means Color Quantization - Part One__en.srt 20KB
  341. 05 - Pandas/009 Pandas - Useful Methods - Apply on Single Column__en.srt 20KB
  342. 10 - Linear Regression/010 Linear Regression - Residual Plots__en.srt 20KB
  343. 07 - Seaborn Data Visualizations/007 Categorical Plots - Distributions within Categories - Understanding Plot Types__en.srt 20KB
  344. 11 - Feature Engineering and Data Preparation/007 Dealing with Categorical Data - Encoding Options__en.srt 20KB
  345. 17 - Random Forests/007 Coding Classification with Random Forest Classifier - Part Two__en.srt 20KB
  346. 10 - Linear Regression/016 Polynomial Regression - Choosing Degree of Polynomial__en.srt 20KB
  347. 10 - Linear Regression/020 Introduction to Cross Validation__en.srt 20KB
  348. 09 - Machine Learning Concepts Overview/004 Supervised Machine Learning Process__en.srt 20KB
  349. 06 - Matplotlib/002 Matplotlib Basics__en.srt 20KB
  350. 10 - Linear Regression/024 L1 Regularization - Lasso Regression - Background and Implementation_en.vtt 20KB
  351. 20 - Naive Bayes Classification and Natural Language Processing/010 Text Classification Project Exercise Solutions__en.srt 19KB
  352. 14 - KNN - K Nearest Neighbors/003 KNN Coding with Python - Part One_en.vtt 19KB
  353. 12 - Cross Validation , Grid Search, and the Linear Regression Project/006 Grid Search__en.srt 19KB
  354. 15 - Support Vector Machines/003 SVM - Theory and Intuition - Hyperplanes and Margins__en.srt 19KB
  355. 14 - KNN - K Nearest Neighbors/006 KNN Classification Project Exercise Solutions_en.vtt 19KB
  356. 05 - Pandas/017 Combining DataFrames - Inner Merge__en.srt 19KB
  357. 05 - Pandas/012 Missing Data - Overview__en.srt 18KB
  358. 02 - OPTIONAL_ Python Crash Course/003 Python Crash Course - Part Two__en.srt 18KB
  359. 17 - Random Forests/005 Random Forests - Bootstrapping and Out-of-Bag Error__en.srt 18KB
  360. 18 - Boosting Methods/007 Gradient Boosting Coding Walkthrough_en.vtt 18KB
  361. 12 - Cross Validation , Grid Search, and the Linear Regression Project/002 Cross Validation - Test _ Train Split__en.srt 17KB
  362. 24 - DBSCAN - Density-based spatial clustering of applications with noise/003 DBSCAN versus K-Means Clustering__en.srt 17KB
  363. 25 - PCA - Principal Component Analysis and Manifold Learning/005 PCA - SciKit-Learn__en.srt 17KB
  364. 23 - Hierarchical Clustering/002 Hierarchical Clustering - Theory and Intuition__en.srt 17KB
  365. 22 - K-Means Clustering/003 K-Means Clustering Theory__en.srt 17KB
  366. 17 - Random Forests/002 Random Forests - History and Motivation__en.srt 17KB
  367. 11 - Feature Engineering and Data Preparation/004 Dealing with Missing Data _ Part One - Evaluation of Missing Data__en.srt 17KB
  368. 10 - Linear Regression/025 L1 and L2 Regularization - Elastic Net__en.srt 17KB
  369. 14 - KNN - K Nearest Neighbors/002 KNN Classification - Theory and Intuition__en.srt 17KB
  370. 10 - Linear Regression/005 Linear Regression - Gradient Descent__en.srt 17KB
  371. 20 - Naive Bayes Classification and Natural Language Processing/006 Feature Extraction from Text - Coding with Scikit-Learn__en.srt 17KB
  372. 18 - Boosting Methods/004 AdaBoost Coding Part One - The Data__en.srt 17KB
  373. 05 - Pandas/022 Pandas Input and Output - CSV Files__en.srt 17KB
  374. 02 - OPTIONAL_ Python Crash Course/004 Python Crash Course - Part Three__en.srt 17KB
  375. 22 - K-Means Clustering/002 Clustering General Overview__en.srt 16KB
  376. 16 - Tree Based Methods_ Decision Tree Learning/006 Constructing Decision Trees with Gini Impurity - Part Two__en.srt 16KB
  377. 10 - Linear Regression/013 Polynomial Regression - Creating Polynomial Features__en.srt 16KB
  378. 15 - Support Vector Machines/006 SVM with Scikit-Learn and Python - Classification Part One__en.srt 16KB
  379. 25 - PCA - Principal Component Analysis and Manifold Learning/003 PCA Theory and Intuition - Part Two__en.srt 16KB
  380. 04 - NumPy/003 NumPy Indexing and Selection__en.srt 16KB
  381. 17 - Random Forests/004 Random Forests - Number of Estimators and Features in Subsets__en.srt 16KB
  382. 18 - Boosting Methods/006 Gradient Boosting Theory__en.srt 16KB
  383. 10 - Linear Regression/015 Bias Variance Trade-Off__en.srt 16KB
  384. 12 - Cross Validation , Grid Search, and the Linear Regression Project/008 Linear Regression Project - Solutions_en.vtt 16KB
  385. 03 - Machine Learning Pathway Overview/001 Machine Learning Pathway__en.srt 16KB
  386. 17 - Random Forests/006 Coding Classification with Random Forest Classifier - Part One_en.vtt 16KB
  387. 07 - Seaborn Data Visualizations/010 Seaborn - Comparison Plots - Coding with Seaborn__en.srt 16KB
  388. 25 - PCA - Principal Component Analysis and Manifold Learning/002 PCA Theory and Intuition - Part One__en.srt 16KB
  389. 17 - Random Forests/011 Coding Regression with Random Forest Regressor - Part Four - Advanced Models__en.srt 15KB
  390. 05 - Pandas/003 Series - Part Two__en.srt 15KB
  391. 17 - Random Forests/010 Coding Regression with Random Forest Regressor - Part Three - Polynomials__en.srt 15KB
  392. 12 - Cross Validation , Grid Search, and the Linear Regression Project/004 Cross Validation - cross_val_score_en.vtt 15KB
  393. 05 - Pandas/016 Combining DataFrames - Concatenation__en.srt 15KB
  394. 07 - Seaborn Data Visualizations/003 Distribution Plots - Part One - Understanding Plot Types__en.srt 15KB
  395. 10 - Linear Regression/019 Feature Scaling__en.srt 15KB
  396. 24 - DBSCAN - Density-based spatial clustering of applications with noise/33643066-wholesome-customers-data.csv 15KB
  397. 09 - Machine Learning Concepts Overview/002 Why Machine Learning___en.srt 15KB
  398. 07 - Seaborn Data Visualizations/006 Categorical Plots - Statistics within Categories - Coding with Seaborn__en.srt 15KB
  399. 05 - Pandas/019 Combining DataFrames - Outer Merge__en.srt 15KB
  400. 01 - Introduction to Course/005 Environment Setup__en.srt 14KB
  401. 13 - Logistic Regression/016 Logistic Regression Project Exercise - Solutions__en.srt 14KB
  402. 10 - Linear Regression/014 Polynomial Regression - Training and Evaluation__en.srt 14KB
  403. 13 - Logistic Regression/009 Classification Metrics - Confusion Matrix and Accuracy__en.srt 14KB
  404. 02 - OPTIONAL_ Python Crash Course/006 Python Crash Course - Exercise Solutions__en.srt 13KB
  405. 22 - K-Means Clustering/009 K-Means Clustering Exercise Overview__en.srt 13KB
  406. 05 - Pandas/002 Series - Part One__en.srt 13KB
  407. 05 - Pandas/005 DataFrames - Part Two - Basic Properties__en.srt 13KB
  408. 16 - Tree Based Methods_ Decision Tree Learning/002 Decision Tree - History__en.srt 13KB
  409. 10 - Linear Regression/002 Linear Regression - Algorithm History__en.srt 13KB
  410. 21 - Unsupervised Learning/001 Unsupervised Learning Overview__en.srt 13KB
  411. 15 - Support Vector Machines/010 Support Vector Machine Project Solutions__en.srt 13KB
  412. 10 - Linear Regression/021 Regularization Data Setup__en.srt 12KB
  413. 26 - Model Deployment/007 Testing the API__en.srt 12KB
  414. 22 - K-Means Clustering/012 K-Means Clustering Exercise Solution - Part Three__en.srt 12KB
  415. 04 - NumPy/004 NumPy Operations__en.srt 12KB
  416. 13 - Logistic Regression/013 Multi-Class Classification with Logistic Regression - Part One - Data and EDA__en.srt 12KB
  417. 25 - PCA - Principal Component Analysis and Manifold Learning/006 PCA - Project Exercise Overview__en.srt 12KB
  418. 20 - Naive Bayes Classification and Natural Language Processing/002 Naive Bayes Algorithm - Part One - Bayes Theorem__en.srt 12KB
  419. 09 - Machine Learning Concepts Overview/003 Types of Machine Learning Algorithms__en.srt 12KB
  420. 26 - Model Deployment/004 Model Deployment as an API - General Overview__en.srt 12KB
  421. 06 - Matplotlib/003 Matplotlib - Understanding the Figure Object__en.srt 12KB
  422. 16 - Tree Based Methods_ Decision Tree Learning/005 Constructing Decision Trees with Gini Impurity - Part One__en.srt 11KB
  423. 10 - Linear Regression/004 Linear Regression - Cost Functions__en.srt 11KB
  424. 07 - Seaborn Data Visualizations/013 Seaborn Plot Exercises Overview__en.srt 11KB
  425. 12 - Cross Validation , Grid Search, and the Linear Regression Project/005 Cross Validation - cross_validate__en.srt 11KB
  426. 10 - Linear Regression/012 Polynomial Regression - Theory and Motivation__en.srt 11KB
  427. 16 - Tree Based Methods_ Decision Tree Learning/004 Decision Tree - Understanding Gini Impurity__en.srt 11KB
  428. 13 - Logistic Regression/011 Classification Metrics - ROC Curves__en.srt 11KB
  429. 14 - KNN - K Nearest Neighbors/003 KNN Coding with Python - Part One__en.srt 11KB
  430. 10 - Linear Regression/007 Overview of Scikit-Learn and Python_en.vtt 11KB
  431. 10 - Linear Regression/023 L2 Regularization - Ridge Regression - Python Implementation__en.srt 11KB
  432. 05 - Pandas/024 Pandas Input and Output - Excel Files__en.srt 11KB
  433. 04 - NumPy/006 Numpy Exercises - Solutions__en.srt 11KB
  434. 24 - DBSCAN - Density-based spatial clustering of applications with noise/004 DBSCAN - Hyperparameter Theory__en.srt 11KB
  435. 26 - Model Deployment/002 Model Deployment Considerations__en.srt 11KB
  436. 06 - Matplotlib/007 Matplotlib Styling - Legends__en.srt 10KB
  437. 10 - Linear Regression/018 Regularization Overview__en.srt 10KB
  438. 10 - Linear Regression/007 Overview of Scikit-Learn and Python__en.srt 10KB
  439. 24 - DBSCAN - Density-based spatial clustering of applications with noise/006 DBSCAN - Outlier Project Exercise Overview__en.srt 10KB
  440. 17 - Random Forests/006 Coding Classification with Random Forest Classifier - Part One__en.srt 10KB
  441. 05 - Pandas/027 Pandas Project Exercise Overview__en.srt 10KB
  442. 13 - Logistic Regression/008 Logistic Regression with Scikit-Learn - Part Two - Model Training__en.srt 10KB
  443. 06 - Matplotlib/010 Matplotlib Exercise Questions Overview__en.srt 9KB
  444. 05 - Pandas/018 Combining DataFrames - Left and Right Merge__en.srt 9KB
  445. 18 - Boosting Methods/002 Boosting Methods - Motivation and History__en.srt 9KB
  446. 18 - Boosting Methods/007 Gradient Boosting Coding Walkthrough__en.srt 9KB
  447. 07 - Seaborn Data Visualizations/005 Categorical Plots - Statistics within Categories - Understanding Plot Types__en.srt 9KB
  448. 12 - Cross Validation , Grid Search, and the Linear Regression Project/008 Linear Regression Project - Solutions__en.srt 9KB
  449. 07 - Seaborn Data Visualizations/009 Seaborn - Comparison Plots - Understanding the Plot Types__en.srt 9KB
  450. 14 - KNN - K Nearest Neighbors/006 KNN Classification Project Exercise Solutions__en.srt 9KB
  451. 09 - Machine Learning Concepts Overview/001 Introduction to Machine Learning Overview Section__en.srt 9KB
  452. 13 - Logistic Regression/002 Introduction to Logistic Regression Section__en.srt 8KB
  453. 10 - Linear Regression/017 Polynomial Regression - Model Deployment__en.srt 8KB
  454. 13 - Logistic Regression/010 Classification Metrics - Precison, Recall, F1-Score__en.srt 8KB
  455. 12 - Cross Validation , Grid Search, and the Linear Regression Project/004 Cross Validation - cross_val_score__en.srt 8KB
  456. 13 - Logistic Regression/003 Logistic Regression - Theory and Intuition - Part One_ The Logistic Function__en.srt 8KB
  457. 22 - K-Means Clustering/32407460-country-iso-codes.csv 8KB
  458. 20 - Naive Bayes Classification and Natural Language Processing/009 Text Classification Project Exercise Overview__en.srt 8KB
  459. 10 - Linear Regression/026 Linear Regression Project - Data Overview__en.srt 8KB
  460. 06 - Matplotlib/005 Matplotlib - Figure Parameters__en.srt 8KB
  461. 13 - Logistic Regression/004 Logistic Regression - Theory and Intuition - Part Two_ Linear to Logistic__en.srt 7KB
  462. 05 - Pandas/001 Introduction to Pandas__en.srt 7KB
  463. 01 - Introduction to Course/002 COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP___en.srt 7KB
  464. 15 - Support Vector Machines/004 SVM - Theory and Intuition - Kernel Intuition__en.srt 7KB
  465. 15 - Support Vector Machines/009 Support Vector Machine Project Overview__en.srt 7KB
  466. 17 - Random Forests/008 Coding Regression with Random Forest Regressor - Part One - Data__en.srt 7KB
  467. 06 - Matplotlib/001 Introduction to Matplotlib__en.srt 7KB
  468. 15 - Support Vector Machines/002 History of Support Vector Machines__en.srt 7KB
  469. 07 - Seaborn Data Visualizations/001 Introduction to Seaborn__en.srt 7KB
  470. 06 - Matplotlib/009 Advanced Matplotlib Commands (Optional)__en.srt 6KB
  471. 13 - Logistic Regression/015 Logistic Regression Exercise Project Overview__en.srt 6KB
  472. 16 - Tree Based Methods_ Decision Tree Learning/003 Decision Tree - Terminology__en.srt 6KB
  473. 12 - Cross Validation , Grid Search, and the Linear Regression Project/007 Linear Regression Project Overview__en.srt 6KB
  474. 10 - Linear Regression/024 L1 Regularization - Lasso Regression - Background and Implementation__en.srt 5KB
  475. 14 - KNN - K Nearest Neighbors/005 KNN Classification Project Exercise Overview__en.srt 5KB
  476. 12 - Cross Validation , Grid Search, and the Linear Regression Project/001 Section Overview and Introduction__en.srt 5KB
  477. 09 - Machine Learning Concepts Overview/005 Companion Book - Introduction to Statistical Learning__en.srt 5KB
  478. 17 - Random Forests/003 Random Forests - Key Hyperparameters__en.srt 4KB
  479. 19 - Supervised Learning Capstone Project/004 Solution Walkthrough - Supervised Learning Project - Tree Models__en.srt 4KB
  480. 25 - PCA - Principal Component Analysis and Manifold Learning/001 Introduction to Principal Component Analysis__en.srt 4KB
  481. 14 - KNN - K Nearest Neighbors/004 KNN Coding with Python - Part Two - Choosing K__en.srt 4KB
  482. 20 - Naive Bayes Classification and Natural Language Processing/001 Introduction to NLP and Naive Bayes Section__en.srt 4KB
  483. 14 - KNN - K Nearest Neighbors/001 Introduction to KNN Section__en.srt 4KB
  484. 22 - K-Means Clustering/001 Introduction to K-Means Clustering Section__en.srt 4KB
  485. 26 - Model Deployment/001 Model Deployment Section Overview__en.srt 3KB
  486. 26 - Model Deployment/003 Model Persistence__en.srt 3KB
  487. 04 - NumPy/001 Introduction to NumPy__en.srt 3KB
  488. 17 - Random Forests/001 Introduction to Random Forests Section__en.srt 3KB
  489. 10 - Linear Regression/001 Introduction to Linear Regression Section__en.srt 3KB
  490. 18 - Boosting Methods/001 Introduction to Boosting Section__en.srt 3KB
  491. 02 - OPTIONAL_ Python Crash Course/005 Python Crash Course - Exercise Questions__en.srt 3KB
  492. 15 - Support Vector Machines/001 Introduction to Support Vector Machines__en.srt 2KB
  493. 16 - Tree Based Methods_ Decision Tree Learning/001 Introduction to Tree Based Methods__en.srt 2KB
  494. 04 - NumPy/005 NumPy Exercises__en.srt 2KB
  495. 01 - Introduction to Course/001 Welcome to the Course_.html 2KB
  496. 24 - DBSCAN - Density-based spatial clustering of applications with noise/001 Introduction to DBSCAN Section__en.srt 1KB
  497. 23 - Hierarchical Clustering/001 Introduction to Hierarchical Clustering__en.srt 1KB
  498. 11 - Feature Engineering and Data Preparation/001 A note from Jose on Feature Engineering and Data Preparation.html 990B
  499. 01 - Introduction to Course/004 Note on Environment Setup - Please read me_.html 857B
  500. 13 - Logistic Regression/001 Early Bird Note on Downloading .zip for Logistic Regression Notes.html 523B
  501. 02 - OPTIONAL_ Python Crash Course/001 OPTIONAL_ Python Crash Course.html 472B
  502. 26 - Model Deployment/005 Note on Upcoming Video.html 249B
  503. 01 - Introduction to Course/28813464-requirements.txt 221B
  504. 08 - Data Analysis and Visualization Capstone Project Exercise/How you can help GetFreeCourses.Co.txt 182B
  505. 22 - K-Means Clustering/How you can help GetFreeCourses.Co.txt 182B
  506. How you can help GetFreeCourses.Co.txt 182B
  507. 01 - Introduction to Course/external-assets-links.txt 132B
  508. 08 - Data Analysis and Visualization Capstone Project Exercise/GetFreeCourses.Co.url 116B
  509. 22 - K-Means Clustering/GetFreeCourses.Co.url 116B
  510. Download Paid Udemy Courses For Free.url 116B
  511. GetFreeCourses.Co.url 116B
  512. 24 - DBSCAN - Density-based spatial clustering of applications with noise/external-assets-links.txt 103B
  513. 20 - Naive Bayes Classification and Natural Language Processing/004 Feature Extraction from Text - Part One - Theory and Intuition__en.srt 0B
  514. 20 - Naive Bayes Classification and Natural Language Processing/005 Feature Extraction from Text - Coding Count Vectorization Manually.mp4 0B
  515. 20 - Naive Bayes Classification and Natural Language Processing/005 Feature Extraction from Text - Coding Count Vectorization Manually__en.srt 0B
  516. 20 - Naive Bayes Classification and Natural Language Processing/007 Natural Language Processing - Classification of Text - Part One__en.srt 0B
  517. 20 - Naive Bayes Classification and Natural Language Processing/008 Natural Language Processing - Classification of Text - Part Two__en.srt 0B