589689.xyz

[] Udemy - 2021 Python for Machine Learning & Data Science Masterclass

  • 收录时间:2021-07-27 03:51:55
  • 文件大小:10GB
  • 下载次数:1
  • 最近下载:2021-07-27 03:51:54
  • 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 146MB
  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. 05 Pandas/026 Pandas Pivot Tables.mp4 129MB
  7. 24 DBSCAN - Density-based spatial clustering of applications with noise/007 DBSCAN - Outlier Project Exercise Solutions.mp4 128MB
  8. 11 Feature Engineering and Data Preparation/003 Dealing with Outliers.mp4 121MB
  9. 11 Feature Engineering and Data Preparation/005 Dealing with Missing Data _ Part Two - Filling or Dropping data based on Rows.mp4 118MB
  10. 16 Tree Based Methods_ Decision Tree Learning/008 Coding Decision Trees - Part Two -Creating the Model.mp4 116MB
  11. 23 Hierarchical Clustering/003 Hierarchical Clustering - Coding Part One - Data and Visualization.mp4 115MB
  12. 07 Seaborn Data Visualizations/002 Scatterplots with Seaborn.mp4 111MB
  13. 24 DBSCAN - Density-based spatial clustering of applications with noise/002 DBSCAN - Theory and Intuition.mp4 109MB
  14. 20 Naive Bayes Classification and Natural Language Processing (Supervised Learning)/010 Text Classification Project Exercise Solutions.mp4 108MB
  15. 22 K-Means Clustering/011 K-Means Clustering Exercise Solution - Part Two.mp4 108MB
  16. 08 Data Analysis and Visualization Capstone Project Exercise/003 Capstone Project Solutions - Part Two.mp4 106MB
  17. 06 Matplotlib/011 Matplotlib Exercise Questions - Solutions.mp4 106MB
  18. 07 Seaborn Data Visualizations/014 Seaborn Plot Exercises Solutions.mp4 106MB
  19. 11 Feature Engineering and Data Preparation/006 Dealing with Missing Data _ Part 3 - Fixing data based on Columns.mp4 105MB
  20. 24 DBSCAN - Density-based spatial clustering of applications with noise/005 DBSCAN - Hyperparameter Tuning Methods.mp4 105MB
  21. 13 Logistic Regression/014 Multi-Class Classification with Logistic Regression - Part Two - Model.mp4 105MB
  22. 14 KNN - K Nearest Neighbors/006 KNN Classification Project Exercise Solutions.mp4 105MB
  23. 14 KNN - K Nearest Neighbors/004 KNN Coding with Python - Part Two - Choosing K.mp4 103MB
  24. 05 Pandas/023 Pandas Input and Output - HTML Tables.mp4 102MB
  25. 08 Data Analysis and Visualization Capstone Project Exercise/002 Capstone Project Solutions - Part One.mp4 102MB
  26. 04 NumPy/002 NumPy Arrays.mp4 99MB
  27. 16 Tree Based Methods_ Decision Tree Learning/007 Coding Decision Trees - Part One - The Data.mp4 99MB
  28. 22 K-Means Clustering/004 K-Means Clustering - Coding Part One.mp4 98MB
  29. 05 Pandas/004 DataFrames - Part One - Creating a DataFrame.mp4 97MB
  30. 06 Matplotlib/006 Matplotlib - Subplots Functionality.mp4 96MB
  31. 05 Pandas/025 Pandas Input and Output - SQL Databases.mp4 96MB
  32. 25 PCA - Principal Component Analysis and Manifold Learning/004 PCA - Manual Implementation in Python.mp4 95MB
  33. 10 Linear Regression/024 L1 Regularization - Lasso Regression - Background and Implementation.mp4 95MB
  34. 15 Support Vector Machines/010 Support Vector Machine Project Solutions.mp4 93MB
  35. 08 Data Analysis and Visualization Capstone Project Exercise/001 Capstone Project Overview.mp4 93MB
  36. 05 Pandas/015 GroupBy Operations - Part Two - MultiIndex.mp4 93MB
  37. 12 Cross Validation , Grid Search, and the Linear Regression Project/008 Linear Regression Project - Solutions.mp4 91MB
  38. 10 Linear Regression/023 L2 Regularization - Ridge Regression - Python Implementation.mp4 89MB
  39. 07 Seaborn Data Visualizations/011 Seaborn Grid Plots.mp4 87MB
  40. 05 Pandas/014 GroupBy Operations - Part One.mp4 87MB
  41. 05 Pandas/010 Pandas - Useful Methods - Apply on Multiple Columns.mp4 85MB
  42. 17 Random Forests/009 Coding Regression with Random Forest Regressor - Part Two - Basic Models.mp4 85MB
  43. 07 Seaborn Data Visualizations/008 Categorical Plots - Distributions within Categories - Coding with Seaborn.mp4 85MB
  44. 01 Introduction to Course/003 Anaconda Python and Jupyter Install and Setup.mp4 85MB
  45. 05 Pandas/006 DataFrames - Part Three - Working with Columns.mp4 84MB
  46. 10 Linear Regression/006 Python coding Simple Linear Regression.mp4 84MB
  47. 15 Support Vector Machines/007 SVM with Scikit-Learn and Python - Classification Part Two.mp4 83MB
  48. 10 Linear Regression/011 Linear Regression - Model Deployment and Coefficient Interpretation.mp4 81MB
  49. 22 K-Means Clustering/005 K-Means Clustering Coding Part Two.mp4 81MB
  50. 22 K-Means Clustering/007 K-Means Color Quantization - Part One.mp4 80MB
  51. 05 Pandas/021 Pandas - Time Methods for Date and Time Data.mp4 80MB
  52. 22 K-Means Clustering/010 K-Means Clustering Exercise Solution - Part One.mp4 80MB
  53. 15 Support Vector Machines/008 SVM with Scikit-Learn and Python - Regression Tasks.mp4 76MB
  54. 05 Pandas/011 Pandas - Useful Methods - Statistical Information and Sorting.mp4 74MB
  55. 25 PCA - Principal Component Analysis and Manifold Learning/005 PCA - SciKit-Learn.mp4 74MB
  56. 05 Pandas/013 Missing Data - Pandas Operations.mp4 74MB
  57. 19 Supervised Learning Capstone Project - Cohort Analysis and Tree Based Methods/001 Introduction to Supervised Learning Capstone Project.mp4 73MB
  58. 10 Linear Regression/003 Linear Regression - Understanding Ordinary Least Squares.mp4 73MB
  59. 12 Cross Validation , Grid Search, and the Linear Regression Project/006 Grid Search.mp4 73MB
  60. 05 Pandas/007 DataFrames - Part Four - Working with Rows.mp4 73MB
  61. 05 Pandas/008 Pandas - Conditional Filtering.mp4 69MB
  62. 24 DBSCAN - Density-based spatial clustering of applications with noise/003 DBSCAN versus K-Means Clustering.mp4 67MB
  63. 10 Linear Regression/025 L1 and L2 Regularization - Elastic Net.mp4 66MB
  64. 22 K-Means Clustering/008 K-Means Color Quantization - Part Two.mp4 65MB
  65. 13 Logistic Regression/012 Logistic Regression with Scikit-Learn - Part Three - Performance Evaluation.mp4 64MB
  66. 18 Boosting Methods/005 AdaBoost Coding Part Two - The Model.mp4 63MB
  67. 13 Logistic Regression/007 Logistic Regression with Scikit-Learn - Part One - EDA.mp4 63MB
  68. 22 K-Means Clustering/012 K-Means Clustering Exercise Solution - Part Three.mp4 62MB
  69. 10 Linear Regression/009 Linear Regression - Scikit-Learn Performance Evaluation - Regression.mp4 62MB
  70. 14 KNN - K Nearest Neighbors/003 KNN Coding with Python - Part One.mp4 62MB
  71. 10 Linear Regression/008 Linear Regression - Scikit-Learn Train Test Split.mp4 61MB
  72. 10 Linear Regression/022 L2 Regularization - Ridge Regression Theory.mp4 61MB
  73. 22 K-Means Clustering/006 K-Means Clustering Coding Part Three.mp4 60MB
  74. 12 Cross Validation , Grid Search, and the Linear Regression Project/003 Cross Validation - Test _ Validation _ Train Split.mp4 59MB
  75. 22 K-Means Clustering/009 K-Means Clustering Exercise Overview.mp4 59MB
  76. 11 Feature Engineering and Data Preparation/007 Dealing with Categorical Data - Encoding Options.mp4 59MB
  77. 18 Boosting Methods/007 Gradient Boosting Coding Walkthrough.mp4 58MB
  78. 01 Introduction to Course/UNZIP-FOR-NOTEBOOKS-Ver7.zip 57MB
  79. 10 Linear Regression/016 Polynomial Regression - Choosing Degree of Polynomial.mp4 56MB
  80. 13 Logistic Regression/006 Logistic Regression - Theory and Intuition - Best fit with Maximum Likelihood.mp4 55MB
  81. 10 Linear Regression/002 Linear Regression - Algorithm History.mp4 55MB
  82. 05 Pandas/009 Pandas - Useful Methods - Apply on Single Column.mp4 54MB
  83. 15 Support Vector Machines/005 SVM - Theory and Intuition - Kernel Trick and Mathematics.mp4 53MB
  84. 22 K-Means Clustering/003 K-Means Clustering Theory.mp4 52MB
  85. 17 Random Forests/006 Coding Classification with Random Forest Classifier - Part One.mp4 52MB
  86. 23 Hierarchical Clustering/002 Hierarchical Clustering - Theory and Intuition.mp4 52MB
  87. 07 Seaborn Data Visualizations/006 Categorical Plots - Statistics within Categories - Coding with Seaborn.mp4 52MB
  88. 07 Seaborn Data Visualizations/010 Seaborn - Comparison Plots - Coding with Seaborn.mp4 51MB
  89. 17 Random Forests/011 Coding Regression with Random Forest Regressor - Part Four - Advanced Models.mp4 51MB
  90. 24 DBSCAN - Density-based spatial clustering of applications with noise/006 DBSCAN - Outlier Project Exercise Overview.mp4 50MB
  91. 06 Matplotlib/010 Matplotlib Exercise Questions Overview.mp4 49MB
  92. 20 Naive Bayes Classification and Natural Language Processing (Supervised Learning)/003 Naive Bayes Algorithm - Part Two - Model Algorithm.mp4 49MB
  93. 12 Cross Validation , Grid Search, and the Linear Regression Project/002 Cross Validation - Test _ Train Split.mp4 47MB
  94. 15 Support Vector Machines/006 SVM with Scikit-Learn and Python - Classification Part One.mp4 46MB
  95. 17 Random Forests/010 Coding Regression with Random Forest Regressor - Part Three - Polynomials.mp4 46MB
  96. 05 Pandas/020 Pandas - Text Methods for String Data.mp4 45MB
  97. 12 Cross Validation , Grid Search, and the Linear Regression Project/005 Cross Validation - cross_validate.mp4 45MB
  98. 07 Seaborn Data Visualizations/007 Categorical Plots - Distributions within Categories - Understanding Plot Types.mp4 45MB
  99. 12 Cross Validation , Grid Search, and the Linear Regression Project/004 Cross Validation - cross_val_score.mp4 45MB
  100. 07 Seaborn Data Visualizations/004 Distribution Plots - Part Two - Coding with Seaborn.mp4 44MB
  101. 06 Matplotlib/008 Matplotlib Styling - Colors and Styles.mp4 44MB
  102. 17 Random Forests/005 Random Forests - Bootstrapping and Out-of-Bag Error.mp4 43MB
  103. 18 Boosting Methods/003 AdaBoost Theory and Intuition.mp4 42MB
  104. 11 Feature Engineering and Data Preparation/002 Introduction to Feature Engineering and Data Preparation.mp4 41MB
  105. 03 Machine Learning Pathway Overview/001 Machine Learning Pathway.mp4 41MB
  106. 05 Pandas/017 Combining DataFrames - Inner Merge.mp4 40MB
  107. 05 Pandas/005 DataFrames - Part Two - Basic Properties.mp4 40MB
  108. 10 Linear Regression/013 Polynomial Regression - Creating Polynomial Features.mp4 40MB
  109. 04 NumPy/003 NumPy Indexing and Selection.mp4 40MB
  110. 05 Pandas/027 Pandas Project Exercise Overview.mp4 39MB
  111. 13 Logistic Regression/013 Multi-Class Classification with Logistic Regression - Part One - Data and EDA.mp4 37MB
  112. 05 Pandas/022 Pandas Input and Output - CSV Files.mp4 37MB
  113. 05 Pandas/016 Combining DataFrames - Concatenation.mp4 37MB
  114. 10 Linear Regression/014 Polynomial Regression - Training and Evaluation.mp4 36MB
  115. 10 Linear Regression/015 Bias Variance Trade-Off.mp4 36MB
  116. 13 Logistic Regression/005 Logistic Regression - Theory and Intuition - Linear to Logistic Math.mp4 36MB
  117. 04 NumPy/004 NumPy Operations.mp4 36MB
  118. 16 Tree Based Methods_ Decision Tree Learning/002 Decision Tree - History.mp4 36MB
  119. 15 Support Vector Machines/003 SVM - Theory and Intuition - Hyperplanes and Margins.mp4 35MB
  120. 04 NumPy/006 Numpy Exercises - Solutions.mp4 35MB
  121. 06 Matplotlib/004 Matplotlib - Implementing Figures and Axes.mp4 35MB
  122. 15 Support Vector Machines/009 Support Vector Machine Project Overview.mp4 35MB
  123. 07 Seaborn Data Visualizations/012 Seaborn - Matrix Plots.mp4 34MB
  124. 09 Machine Learning Concepts Overview/004 Supervised Machine Learning Process.mp4 34MB
  125. 02 OPTIONAL_ Python Crash Course/006 Python Crash Course - Exercise Solutions.mp4 33MB
  126. 13 Logistic Regression/008 Logistic Regression with Scikit-Learn - Part Two - Model Training.mp4 33MB
  127. 02 OPTIONAL_ Python Crash Course/004 Python Crash Course - Part Three.mp4 32MB
  128. 21 Unsupervised Learning/001 Unsupervised Learning Overview.mp4 32MB
  129. 11 Feature Engineering and Data Preparation/004 Dealing with Missing Data _ Part One - Evaluation of Missing Data.mp4 31MB
  130. 06 Matplotlib/002 Matplotlib Basics.mp4 31MB
  131. 20 Naive Bayes Classification and Natural Language Processing (Supervised Learning)/009 Text Classification Project Exercise Overview.mp4 31MB
  132. 02 OPTIONAL_ Python Crash Course/002 Python Crash Course - Part One.mp4 30MB
  133. 25 PCA - Principal Component Analysis and Manifold Learning/002 PCA Theory and Intuition - Part One.mp4 30MB
  134. 10 Linear Regression/010 Linear Regression - Residual Plots.mp4 30MB
  135. 10 Linear Regression/020 Introduction to Cross Validation.mp4 29MB
  136. 10 Linear Regression/005 Linear Regression - Gradient Descent.mp4 29MB
  137. 05 Pandas/002 Series - Part One.mp4 29MB
  138. 16 Tree Based Methods_ Decision Tree Learning/006 Constructing Decision Trees with Gini Impurity - Part Two.mp4 28MB
  139. 17 Random Forests/004 Random Forests - Number of Estimators and Features in Subsets.mp4 27MB
  140. 05 Pandas/012 Missing Data - Overview.mp4 27MB
  141. 05 Pandas/003 Series - Part Two.mp4 26MB
  142. 05 Pandas/024 Pandas Input and Output - Excel Files.mp4 26MB
  143. 02 OPTIONAL_ Python Crash Course/003 Python Crash Course - Part Two.mp4 26MB
  144. 06 Matplotlib/009 Advanced Matplotlib Commands (Optional).mp4 25MB
  145. 22 K-Means Clustering/002 Clustering General Overview.mp4 25MB
  146. 01 Introduction to Course/002 COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!.mp4 25MB
  147. 10 Linear Regression/019 Feature Scaling.mp4 24MB
  148. 13 Logistic Regression/015 Logistic Regression Exercise Project Overview.mp4 24MB
  149. 17 Random Forests/002 Random Forests - History and Motivation.mp4 24MB
  150. 14 KNN - K Nearest Neighbors/002 KNN Classification - Theory and Intuition.mp4 24MB
  151. 12 Cross Validation , Grid Search, and the Linear Regression Project/007 Linear Regression Project Overview.mp4 24MB
  152. 13 Logistic Regression/010 Classification Metrics - Precison, Recall, F1-Score.mp4 23MB
  153. 10 Linear Regression/017 Polynomial Regression - Model Deployment.mp4 23MB
  154. 01 Introduction to Course/005 Environment Setup.mp4 23MB
  155. 10 Linear Regression/007 Overview of Scikit-Learn and Python.mp4 23MB
  156. 18 Boosting Methods/006 Gradient Boosting Theory.mp4 23MB
  157. 18 Boosting Methods/004 AdaBoost Coding Part One - The Data.mp4 23MB
  158. 10 Linear Regression/012 Polynomial Regression - Theory and Motivation.mp4 22MB
  159. 05 Pandas/019 Combining DataFrames - Outer Merge.mp4 22MB
  160. 20 Naive Bayes Classification and Natural Language Processing (Supervised Learning)/002 Naive Bayes Algorithm - Part One - Bayes Theorem.mp4 22MB
  161. 18 Boosting Methods/002 Boosting Methods - Motivation and History.mp4 22MB
  162. 13 Logistic Regression/009 Classification Metrics - Confusion Matrix and Accuracy.mp4 22MB
  163. 14 KNN - K Nearest Neighbors/005 KNN Classification Project Exercise Overview.mp4 21MB
  164. 09 Machine Learning Concepts Overview/002 Why Machine Learning_.mp4 21MB
  165. 16 Tree Based Methods_ Decision Tree Learning/004 Decision Tree - Understanding Gini Impurity.mp4 19MB
  166. 25 PCA - Principal Component Analysis and Manifold Learning/003 PCA Theory and Intuition - Part Two.mp4 19MB
  167. 09 Machine Learning Concepts Overview/003 Types of Machine Learning Algorithms.mp4 18MB
  168. 16 Tree Based Methods_ Decision Tree Learning/005 Constructing Decision Trees with Gini Impurity - Part One.mp4 18MB
  169. 13 Logistic Regression/003 Logistic Regression - Theory and Intuition - Part One_ The Logistic Function.mp4 17MB
  170. 10 Linear Regression/026 Linear Regression Project - Data Overview.mp4 17MB
  171. 10 Linear Regression/004 Linear Regression - Cost Functions.mp4 17MB
  172. 24 DBSCAN - Density-based spatial clustering of applications with noise/004 DBSCAN - Hyperparameter Theory.mp4 16MB
  173. 05 Pandas/018 Combining DataFrames - Left and Right Merge.mp4 16MB
  174. 06 Matplotlib/007 Matplotlib Styling - Legends.mp4 16MB
  175. 13 Logistic Regression/011 Classification Metrics - ROC Curves.mp4 16MB
  176. 07 Seaborn Data Visualizations/005 Categorical Plots - Statistics within Categories - Understanding Plot Types.mp4 16MB
  177. 07 Seaborn Data Visualizations/013 Seaborn Plot Exercises Overview.mp4 16MB
  178. 15 Support Vector Machines/002 History of Support Vector Machines.mp4 16MB
  179. 10 Linear Regression/021 Regularization Data Setup.mp4 15MB
  180. 07 Seaborn Data Visualizations/003 Distribution Plots - Part One - Understanding Plot Types.mp4 15MB
  181. 13 Logistic Regression/002 Introduction to Logistic Regression Section.mp4 14MB
  182. 17 Random Forests/008 Coding Regression with Random Forest Regressor - Part One - Data.mp4 14MB
  183. 15 Support Vector Machines/004 SVM - Theory and Intuition - Kernel Intuition.mp4 13MB
  184. 09 Machine Learning Concepts Overview/001 Introduction to Machine Learning Overview Section.mp4 13MB
  185. 10 Linear Regression/018 Regularization Overview.mp4 13MB
  186. 06 Matplotlib/003 Matplotlib - Understanding the Figure Object.mp4 12MB
  187. 06 Matplotlib/005 Matplotlib - Figure Parameters.mp4 11MB
  188. 06 Matplotlib/001 Introduction to Matplotlib.mp4 11MB
  189. 13 Logistic Regression/004 Logistic Regression - Theory and Intuition - Part Two_ Linear to Logistic.mp4 11MB
  190. 07 Seaborn Data Visualizations/009 Seaborn - Comparison Plots - Understanding the Plot Types.mp4 11MB
  191. 07 Seaborn Data Visualizations/001 Introduction to Seaborn.mp4 11MB
  192. 12 Cross Validation , Grid Search, and the Linear Regression Project/001 Section Overview and Introduction.mp4 10MB
  193. 09 Machine Learning Concepts Overview/005 Companion Book - Introduction to Statistical Learning.mp4 10MB
  194. 04 NumPy/005 NumPy Exercises.mp4 10MB
  195. 17 Random Forests/003 Random Forests - Key Hyperparameters.mp4 10MB
  196. 05 Pandas/001 Introduction to Pandas.mp4 9MB
  197. 04 NumPy/001 Introduction to NumPy.mp4 8MB
  198. 02 OPTIONAL_ Python Crash Course/005 Python Crash Course - Exercise Questions.mp4 8MB
  199. 20 Naive Bayes Classification and Natural Language Processing (Supervised Learning)/moviereviews.csv 7MB
  200. 19 Supervised Learning Capstone Project - Cohort Analysis and Tree Based Methods/17-Supervised-Learning-Capstone-Project.zip 7MB
  201. 20 Naive Bayes Classification and Natural Language Processing (Supervised Learning)/001 Introduction to NLP and Naive Bayes Section.mp4 7MB
  202. 16 Tree Based Methods_ Decision Tree Learning/003 Decision Tree - Terminology.mp4 6MB
  203. 25 PCA - Principal Component Analysis and Manifold Learning/001 Introduction to Principal Component Analysis.mp4 6MB
  204. 24 DBSCAN - Density-based spatial clustering of applications with noise/001 Introduction to DBSCAN Section.mp4 6MB
  205. 22 K-Means Clustering/20-Kmeans-Clustering.zip 6MB
  206. 23 Hierarchical Clustering/001 Introduction to Hierarchical Clustering.mp4 6MB
  207. 14 KNN - K Nearest Neighbors/001 Introduction to KNN Section.mp4 5MB
  208. 22 K-Means Clustering/bank-full.csv 5MB
  209. 22 K-Means Clustering/001 Introduction to K-Means Clustering Section.mp4 5MB
  210. 15 Support Vector Machines/001 Introduction to Support Vector Machines.mp4 4MB
  211. 18 Boosting Methods/001 Introduction to Boosting Section.mp4 4MB
  212. 17 Random Forests/001 Introduction to Random Forests Section.mp4 4MB
  213. 17 Random Forests/15-Random-Forests.zip 4MB
  214. 24 DBSCAN - Density-based spatial clustering of applications with noise/22-DBSCAN.zip 4MB
  215. 10 Linear Regression/001 Introduction to Linear Regression Section.mp4 3MB
  216. 20 Naive Bayes Classification and Natural Language Processing (Supervised Learning)/airline-tweets.csv 3MB
  217. 16 Tree Based Methods_ Decision Tree Learning/001 Introduction to Tree Based Methods.mp4 3MB
  218. 13 Logistic Regression/11-Logistic-Regression-Models.zip 2MB
  219. 16 Tree Based Methods_ Decision Tree Learning/14-Decision-Trees.zip 2MB
  220. 15 Support Vector Machines/13-Support-Vector-Machines.zip 2MB
  221. 14 KNN - K Nearest Neighbors/12-K-Nearest-Neighbors.zip 1MB
  222. 19 Supervised Learning Capstone Project - Cohort Analysis and Tree Based Methods/Telco-Customer-Churn.csv 954KB
  223. 18 Boosting Methods/16-Boosted-Trees.zip 918KB
  224. 23 Hierarchical Clustering/21-Hierarchical-Clustering.zip 622KB
  225. 18 Boosting Methods/mushrooms.csv 365KB
  226. 20 Naive Bayes Classification and Natural Language Processing (Supervised Learning)/18-Naive-Bayes-and-NLP.zip 192KB
  227. 22 K-Means Clustering/palm-trees.jpg 173KB
  228. 24 DBSCAN - Density-based spatial clustering of applications with noise/cluster-circles.csv 60KB
  229. 24 DBSCAN - Density-based spatial clustering of applications with noise/cluster-moons.csv 59KB
  230. 24 DBSCAN - Density-based spatial clustering of applications with noise/cluster-blobs.csv 56KB
  231. 17 Random Forests/data-banknote-authentication.csv 45KB
  232. 23 Hierarchical Clustering/004 Hierarchical Clustering - Coding Part Two - Scikit-Learn_en.srt 42KB
  233. 11 Feature Engineering and Data Preparation/003 Dealing with Outliers_en.srt 41KB
  234. 05 Pandas/028 Pandas Project Exercise Solutions_en.srt 39KB
  235. 24 DBSCAN - Density-based spatial clustering of applications with noise/cluster-two-blobs-outliers.csv 38KB
  236. 24 DBSCAN - Density-based spatial clustering of applications with noise/cluster-two-blobs.csv 38KB
  237. 24 DBSCAN - Density-based spatial clustering of applications with noise/007 DBSCAN - Outlier Project Exercise Solutions_en.srt 38KB
  238. 11 Feature Engineering and Data Preparation/006 Dealing with Missing Data _ Part 3 - Fixing data based on Columns_en.srt 37KB
  239. 22 K-Means Clustering/CIA-Country-Facts.csv 33KB
  240. 16 Tree Based Methods_ Decision Tree Learning/008 Coding Decision Trees - Part Two -Creating the Model_en.srt 33KB
  241. 24 DBSCAN - Density-based spatial clustering of applications with noise/005 DBSCAN - Hyperparameter Tuning Methods_en.srt 33KB
  242. 05 Pandas/026 Pandas Pivot Tables_en.srt 32KB
  243. 04 NumPy/002 NumPy Arrays_en.srt 32KB
  244. 05 Pandas/021 Pandas - Time Methods for Date and Time Data_en.srt 32KB
  245. 11 Feature Engineering and Data Preparation/005 Dealing with Missing Data _ Part Two - Filling or Dropping data based on Rows_en.srt 31KB
  246. 13 Logistic Regression/016 Logistic Regression Project Exercise - Solutions_en.vtt 31KB
  247. 08 Data Analysis and Visualization Capstone Project Exercise/004 Capstone Project Solutions - Part Three_en.srt 31KB
  248. 14 KNN - K Nearest Neighbors/004 KNN Coding with Python - Part Two - Choosing K_en.vtt 31KB
  249. 22 K-Means Clustering/004 K-Means Clustering - Coding Part One_en.srt 30KB
  250. 07 Seaborn Data Visualizations/002 Scatterplots with Seaborn_en.srt 30KB
  251. 05 Pandas/025 Pandas Input and Output - SQL Databases_en.srt 29KB
  252. 15 Support Vector Machines/005 SVM - Theory and Intuition - Kernel Trick and Mathematics_en.srt 29KB
  253. 16 Tree Based Methods_ Decision Tree Learning/007 Coding Decision Trees - Part One - The Data_en.srt 29KB
  254. 05 Pandas/004 DataFrames - Part One - Creating a DataFrame_en.srt 29KB
  255. 18 Boosting Methods/003 AdaBoost Theory and Intuition_en.srt 29KB
  256. 06 Matplotlib/006 Matplotlib - Subplots Functionality_en.srt 29KB
  257. 07 Seaborn Data Visualizations/008 Categorical Plots - Distributions within Categories - Coding with Seaborn_en.srt 28KB
  258. 10 Linear Regression/006 Python coding Simple Linear Regression_en.srt 28KB
  259. 17 Random Forests/007 Coding Classification with Random Forest Classifier - Part Two_en.vtt 28KB
  260. 05 Pandas/013 Missing Data - Pandas Operations_en.srt 27KB
  261. 05 Pandas/008 Pandas - Conditional Filtering_en.srt 27KB
  262. 08 Data Analysis and Visualization Capstone Project Exercise/002 Capstone Project Solutions - Part One_en.srt 27KB
  263. 18 Boosting Methods/005 AdaBoost Coding Part Two - The Model_en.srt 27KB
  264. 22 K-Means Clustering/005 K-Means Clustering Coding Part Two_en.srt 27KB
  265. 24 DBSCAN - Density-based spatial clustering of applications with noise/002 DBSCAN - Theory and Intuition_en.srt 27KB
  266. 25 PCA - Principal Component Analysis and Manifold Learning/004 PCA - Manual Implementation in Python_en.srt 26KB
  267. 15 Support Vector Machines/008 SVM with Scikit-Learn and Python - Regression Tasks_en.vtt 26KB
  268. 05 Pandas/010 Pandas - Useful Methods - Apply on Multiple Columns_en.srt 26KB
  269. 19 Supervised Learning Capstone Project - Cohort Analysis and Tree Based Methods/001 Introduction to Supervised Learning Capstone Project_en.srt 26KB
  270. 15 Support Vector Machines/008 SVM with Scikit-Learn and Python - Regression Tasks_en.srt 26KB
  271. 10 Linear Regression/011 Linear Regression - Model Deployment and Coefficient Interpretation_en.srt 26KB
  272. 23 Hierarchical Clustering/003 Hierarchical Clustering - Coding Part One - Data and Visualization_en.srt 25KB
  273. 13 Logistic Regression/005 Logistic Regression - Theory and Intuition - Linear to Logistic Math_en.srt 25KB
  274. 07 Seaborn Data Visualizations/004 Distribution Plots - Part Two - Coding with Seaborn_en.srt 25KB
  275. 02 OPTIONAL_ Python Crash Course/002 Python Crash Course - Part One_en.srt 25KB
  276. 06 Matplotlib/011 Matplotlib Exercise Questions - Solutions_en.srt 25KB
  277. 11 Feature Engineering and Data Preparation/002 Introduction to Feature Engineering and Data Preparation_en.srt 24KB
  278. 05 Pandas/020 Pandas - Text Methods for String Data_en.srt 24KB
  279. 13 Logistic Regression/014 Multi-Class Classification with Logistic Regression - Part Two - Model_en.srt 24KB
  280. 10 Linear Regression/008 Linear Regression - Scikit-Learn Train Test Split_en.srt 24KB
  281. 22 K-Means Clustering/011 K-Means Clustering Exercise Solution - Part Two_en.srt 24KB
  282. 08 Data Analysis and Visualization Capstone Project Exercise/003 Capstone Project Solutions - Part Two_en.srt 23KB
  283. 13 Logistic Regression/012 Logistic Regression with Scikit-Learn - Part Three - Performance Evaluation_en.srt 23KB
  284. 05 Pandas/011 Pandas - Useful Methods - Statistical Information and Sorting_en.srt 23KB
  285. 10 Linear Regression/009 Linear Regression - Scikit-Learn Performance Evaluation - Regression_en.srt 23KB
  286. 10 Linear Regression/023 L2 Regularization - Ridge Regression - Python Implementation_en.vtt 23KB
  287. 13 Logistic Regression/006 Logistic Regression - Theory and Intuition - Best fit with Maximum Likelihood_en.srt 23KB
  288. 10 Linear Regression/025 L1 and L2 Regularization - Elastic Net_en.vtt 23KB
  289. 10 Linear Regression/003 Linear Regression - Understanding Ordinary Least Squares_en.srt 23KB
  290. 15 Support Vector Machines/010 Support Vector Machine Project Solutions_en.vtt 23KB
  291. 07 Seaborn Data Visualizations/014 Seaborn Plot Exercises Solutions_en.srt 22KB
  292. 05 Pandas/023 Pandas Input and Output - HTML Tables_en.srt 22KB
  293. 13 Logistic Regression/007 Logistic Regression with Scikit-Learn - Part One - EDA_en.srt 22KB
  294. 12 Cross Validation , Grid Search, and the Linear Regression Project/003 Cross Validation - Test _ Validation _ Train Split_en.srt 22KB
  295. 01 Introduction to Course/003 Anaconda Python and Jupyter Install and Setup_en.srt 22KB
  296. 05 Pandas/014 GroupBy Operations - Part One_en.srt 21KB
  297. 22 K-Means Clustering/006 K-Means Clustering Coding Part Three_en.srt 21KB
  298. 20 Naive Bayes Classification and Natural Language Processing (Supervised Learning)/010 Text Classification Project Exercise Solutions_en.vtt 21KB
  299. 22 K-Means Clustering/008 K-Means Color Quantization - Part Two_en.srt 21KB
  300. 22 K-Means Clustering/010 K-Means Clustering Exercise Solution - Part One_en.srt 21KB
  301. 07 Seaborn Data Visualizations/012 Seaborn - Matrix Plots_en.srt 21KB
  302. 05 Pandas/007 DataFrames - Part Four - Working with Rows_en.srt 21KB
  303. 06 Matplotlib/008 Matplotlib Styling - Colors and Styles_en.srt 21KB
  304. 15 Support Vector Machines/007 SVM with Scikit-Learn and Python - Classification Part Two_en.vtt 21KB
  305. 06 Matplotlib/004 Matplotlib - Implementing Figures and Axes_en.srt 21KB
  306. 05 Pandas/015 GroupBy Operations - Part Two - MultiIndex_en.srt 21KB
  307. 23 Hierarchical Clustering/cluster-mpg.csv 21KB
  308. 15 Support Vector Machines/007 SVM with Scikit-Learn and Python - Classification Part Two_en.srt 21KB
  309. 10 Linear Regression/022 L2 Regularization - Ridge Regression Theory_en.srt 21KB
  310. 05 Pandas/006 DataFrames - Part Three - Working with Columns_en.srt 21KB
  311. 08 Data Analysis and Visualization Capstone Project Exercise/001 Capstone Project Overview_en.srt 21KB
  312. 07 Seaborn Data Visualizations/011 Seaborn Grid Plots_en.srt 20KB
  313. 17 Random Forests/009 Coding Regression with Random Forest Regressor - Part Two - Basic Models_en.srt 20KB
  314. 22 K-Means Clustering/007 K-Means Color Quantization - Part One_en.srt 20KB
  315. 05 Pandas/009 Pandas - Useful Methods - Apply on Single Column_en.srt 20KB
  316. 10 Linear Regression/010 Linear Regression - Residual Plots_en.srt 20KB
  317. 07 Seaborn Data Visualizations/007 Categorical Plots - Distributions within Categories - Understanding Plot Types_en.srt 20KB
  318. 11 Feature Engineering and Data Preparation/007 Dealing with Categorical Data - Encoding Options_en.srt 20KB
  319. 17 Random Forests/007 Coding Classification with Random Forest Classifier - Part Two_en.srt 20KB
  320. 10 Linear Regression/016 Polynomial Regression - Choosing Degree of Polynomial_en.srt 20KB
  321. 10 Linear Regression/020 Introduction to Cross Validation_en.srt 20KB
  322. 09 Machine Learning Concepts Overview/004 Supervised Machine Learning Process_en.srt 20KB
  323. 06 Matplotlib/002 Matplotlib Basics_en.srt 20KB
  324. 10 Linear Regression/024 L1 Regularization - Lasso Regression - Background and Implementation_en.vtt 20KB
  325. 20 Naive Bayes Classification and Natural Language Processing (Supervised Learning)/010 Text Classification Project Exercise Solutions_en.srt 19KB
  326. 14 KNN - K Nearest Neighbors/003 KNN Coding with Python - Part One_en.vtt 19KB
  327. 12 Cross Validation , Grid Search, and the Linear Regression Project/006 Grid Search_en.srt 19KB
  328. 15 Support Vector Machines/003 SVM - Theory and Intuition - Hyperplanes and Margins_en.srt 19KB
  329. 14 KNN - K Nearest Neighbors/006 KNN Classification Project Exercise Solutions_en.vtt 19KB
  330. 05 Pandas/017 Combining DataFrames - Inner Merge_en.srt 19KB
  331. 05 Pandas/012 Missing Data - Overview_en.srt 18KB
  332. 02 OPTIONAL_ Python Crash Course/003 Python Crash Course - Part Two_en.srt 18KB
  333. 17 Random Forests/005 Random Forests - Bootstrapping and Out-of-Bag Error_en.srt 18KB
  334. 18 Boosting Methods/007 Gradient Boosting Coding Walkthrough_en.vtt 18KB
  335. 12 Cross Validation , Grid Search, and the Linear Regression Project/002 Cross Validation - Test _ Train Split_en.srt 17KB
  336. 24 DBSCAN - Density-based spatial clustering of applications with noise/003 DBSCAN versus K-Means Clustering_en.srt 17KB
  337. 25 PCA - Principal Component Analysis and Manifold Learning/005 PCA - SciKit-Learn_en.srt 17KB
  338. 23 Hierarchical Clustering/002 Hierarchical Clustering - Theory and Intuition_en.srt 17KB
  339. 22 K-Means Clustering/003 K-Means Clustering Theory_en.srt 17KB
  340. 17 Random Forests/002 Random Forests - History and Motivation_en.srt 17KB
  341. 11 Feature Engineering and Data Preparation/004 Dealing with Missing Data _ Part One - Evaluation of Missing Data_en.srt 17KB
  342. 10 Linear Regression/025 L1 and L2 Regularization - Elastic Net_en.srt 17KB
  343. 14 KNN - K Nearest Neighbors/002 KNN Classification - Theory and Intuition_en.srt 17KB
  344. 10 Linear Regression/005 Linear Regression - Gradient Descent_en.srt 17KB
  345. 18 Boosting Methods/004 AdaBoost Coding Part One - The Data_en.srt 17KB
  346. 05 Pandas/022 Pandas Input and Output - CSV Files_en.srt 17KB
  347. 02 OPTIONAL_ Python Crash Course/004 Python Crash Course - Part Three_en.srt 17KB
  348. 22 K-Means Clustering/002 Clustering General Overview_en.srt 16KB
  349. 16 Tree Based Methods_ Decision Tree Learning/006 Constructing Decision Trees with Gini Impurity - Part Two_en.srt 16KB
  350. 10 Linear Regression/013 Polynomial Regression - Creating Polynomial Features_en.srt 16KB
  351. 15 Support Vector Machines/006 SVM with Scikit-Learn and Python - Classification Part One_en.srt 16KB
  352. 25 PCA - Principal Component Analysis and Manifold Learning/003 PCA Theory and Intuition - Part Two_en.srt 16KB
  353. 04 NumPy/003 NumPy Indexing and Selection_en.srt 16KB
  354. 17 Random Forests/004 Random Forests - Number of Estimators and Features in Subsets_en.srt 16KB
  355. 18 Boosting Methods/006 Gradient Boosting Theory_en.srt 16KB
  356. 10 Linear Regression/015 Bias Variance Trade-Off_en.srt 16KB
  357. 12 Cross Validation , Grid Search, and the Linear Regression Project/008 Linear Regression Project - Solutions_en.vtt 16KB
  358. 03 Machine Learning Pathway Overview/001 Machine Learning Pathway_en.srt 16KB
  359. 17 Random Forests/006 Coding Classification with Random Forest Classifier - Part One_en.vtt 16KB
  360. 07 Seaborn Data Visualizations/010 Seaborn - Comparison Plots - Coding with Seaborn_en.srt 16KB
  361. 25 PCA - Principal Component Analysis and Manifold Learning/002 PCA Theory and Intuition - Part One_en.srt 16KB
  362. 17 Random Forests/011 Coding Regression with Random Forest Regressor - Part Four - Advanced Models_en.srt 15KB
  363. 05 Pandas/003 Series - Part Two_en.srt 15KB
  364. 17 Random Forests/010 Coding Regression with Random Forest Regressor - Part Three - Polynomials_en.srt 15KB
  365. 12 Cross Validation , Grid Search, and the Linear Regression Project/004 Cross Validation - cross_val_score_en.vtt 15KB
  366. 05 Pandas/016 Combining DataFrames - Concatenation_en.srt 15KB
  367. 07 Seaborn Data Visualizations/003 Distribution Plots - Part One - Understanding Plot Types_en.srt 15KB
  368. 10 Linear Regression/019 Feature Scaling_en.srt 15KB
  369. 24 DBSCAN - Density-based spatial clustering of applications with noise/wholesome-customers-data.csv 15KB
  370. 09 Machine Learning Concepts Overview/002 Why Machine Learning__en.srt 15KB
  371. 07 Seaborn Data Visualizations/006 Categorical Plots - Statistics within Categories - Coding with Seaborn_en.srt 15KB
  372. 05 Pandas/019 Combining DataFrames - Outer Merge_en.srt 15KB
  373. 01 Introduction to Course/005 Environment Setup_en.srt 14KB
  374. 13 Logistic Regression/016 Logistic Regression Project Exercise - Solutions_en.srt 14KB
  375. 10 Linear Regression/014 Polynomial Regression - Training and Evaluation_en.srt 14KB
  376. 13 Logistic Regression/009 Classification Metrics - Confusion Matrix and Accuracy_en.srt 14KB
  377. 02 OPTIONAL_ Python Crash Course/006 Python Crash Course - Exercise Solutions_en.srt 13KB
  378. 22 K-Means Clustering/009 K-Means Clustering Exercise Overview_en.srt 13KB
  379. 05 Pandas/002 Series - Part One_en.srt 13KB
  380. 05 Pandas/005 DataFrames - Part Two - Basic Properties_en.srt 13KB
  381. 16 Tree Based Methods_ Decision Tree Learning/002 Decision Tree - History_en.srt 13KB
  382. 10 Linear Regression/002 Linear Regression - Algorithm History_en.srt 13KB
  383. 21 Unsupervised Learning/001 Unsupervised Learning Overview_en.srt 13KB
  384. 15 Support Vector Machines/010 Support Vector Machine Project Solutions_en.srt 13KB
  385. 10 Linear Regression/021 Regularization Data Setup_en.srt 12KB
  386. 22 K-Means Clustering/012 K-Means Clustering Exercise Solution - Part Three_en.srt 12KB
  387. 04 NumPy/004 NumPy Operations_en.srt 12KB
  388. 13 Logistic Regression/013 Multi-Class Classification with Logistic Regression - Part One - Data and EDA_en.srt 12KB
  389. 20 Naive Bayes Classification and Natural Language Processing (Supervised Learning)/002 Naive Bayes Algorithm - Part One - Bayes Theorem_en.srt 12KB
  390. 09 Machine Learning Concepts Overview/003 Types of Machine Learning Algorithms_en.srt 12KB
  391. 06 Matplotlib/003 Matplotlib - Understanding the Figure Object_en.srt 12KB
  392. 16 Tree Based Methods_ Decision Tree Learning/005 Constructing Decision Trees with Gini Impurity - Part One_en.srt 11KB
  393. 10 Linear Regression/004 Linear Regression - Cost Functions_en.srt 11KB
  394. 07 Seaborn Data Visualizations/013 Seaborn Plot Exercises Overview_en.srt 11KB
  395. 12 Cross Validation , Grid Search, and the Linear Regression Project/005 Cross Validation - cross_validate_en.srt 11KB
  396. 10 Linear Regression/012 Polynomial Regression - Theory and Motivation_en.srt 11KB
  397. 16 Tree Based Methods_ Decision Tree Learning/004 Decision Tree - Understanding Gini Impurity_en.srt 11KB
  398. 13 Logistic Regression/011 Classification Metrics - ROC Curves_en.srt 11KB
  399. 14 KNN - K Nearest Neighbors/003 KNN Coding with Python - Part One_en.srt 11KB
  400. 10 Linear Regression/007 Overview of Scikit-Learn and Python_en.vtt 11KB
  401. 10 Linear Regression/023 L2 Regularization - Ridge Regression - Python Implementation_en.srt 11KB
  402. 05 Pandas/024 Pandas Input and Output - Excel Files_en.srt 11KB
  403. 04 NumPy/006 Numpy Exercises - Solutions_en.srt 11KB
  404. 24 DBSCAN - Density-based spatial clustering of applications with noise/004 DBSCAN - Hyperparameter Theory_en.srt 11KB
  405. 06 Matplotlib/007 Matplotlib Styling - Legends_en.srt 10KB
  406. 10 Linear Regression/018 Regularization Overview_en.srt 10KB
  407. 10 Linear Regression/007 Overview of Scikit-Learn and Python_en.srt 10KB
  408. 24 DBSCAN - Density-based spatial clustering of applications with noise/006 DBSCAN - Outlier Project Exercise Overview_en.srt 10KB
  409. 17 Random Forests/006 Coding Classification with Random Forest Classifier - Part One_en.srt 10KB
  410. 05 Pandas/027 Pandas Project Exercise Overview_en.srt 10KB
  411. 13 Logistic Regression/008 Logistic Regression with Scikit-Learn - Part Two - Model Training_en.srt 10KB
  412. 06 Matplotlib/010 Matplotlib Exercise Questions Overview_en.srt 9KB
  413. 05 Pandas/018 Combining DataFrames - Left and Right Merge_en.srt 9KB
  414. 18 Boosting Methods/002 Boosting Methods - Motivation and History_en.srt 9KB
  415. 18 Boosting Methods/007 Gradient Boosting Coding Walkthrough_en.srt 9KB
  416. 07 Seaborn Data Visualizations/005 Categorical Plots - Statistics within Categories - Understanding Plot Types_en.srt 9KB
  417. 12 Cross Validation , Grid Search, and the Linear Regression Project/008 Linear Regression Project - Solutions_en.srt 9KB
  418. 07 Seaborn Data Visualizations/009 Seaborn - Comparison Plots - Understanding the Plot Types_en.srt 9KB
  419. 14 KNN - K Nearest Neighbors/006 KNN Classification Project Exercise Solutions_en.srt 9KB
  420. 09 Machine Learning Concepts Overview/001 Introduction to Machine Learning Overview Section_en.srt 9KB
  421. 13 Logistic Regression/002 Introduction to Logistic Regression Section_en.srt 8KB
  422. 10 Linear Regression/017 Polynomial Regression - Model Deployment_en.srt 8KB
  423. 13 Logistic Regression/010 Classification Metrics - Precison, Recall, F1-Score_en.srt 8KB
  424. 12 Cross Validation , Grid Search, and the Linear Regression Project/004 Cross Validation - cross_val_score_en.srt 8KB
  425. 13 Logistic Regression/003 Logistic Regression - Theory and Intuition - Part One_ The Logistic Function_en.srt 8KB
  426. 22 K-Means Clustering/country-iso-codes.csv 8KB
  427. 20 Naive Bayes Classification and Natural Language Processing (Supervised Learning)/009 Text Classification Project Exercise Overview_en.srt 8KB
  428. 10 Linear Regression/026 Linear Regression Project - Data Overview_en.srt 8KB
  429. 06 Matplotlib/005 Matplotlib - Figure Parameters_en.srt 8KB
  430. 13 Logistic Regression/004 Logistic Regression - Theory and Intuition - Part Two_ Linear to Logistic_en.srt 7KB
  431. 05 Pandas/001 Introduction to Pandas_en.srt 7KB
  432. 01 Introduction to Course/002 COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!_en.srt 7KB
  433. 15 Support Vector Machines/004 SVM - Theory and Intuition - Kernel Intuition_en.srt 7KB
  434. 15 Support Vector Machines/009 Support Vector Machine Project Overview_en.srt 7KB
  435. 17 Random Forests/008 Coding Regression with Random Forest Regressor - Part One - Data_en.srt 7KB
  436. 06 Matplotlib/001 Introduction to Matplotlib_en.srt 7KB
  437. 15 Support Vector Machines/002 History of Support Vector Machines_en.srt 7KB
  438. 07 Seaborn Data Visualizations/001 Introduction to Seaborn_en.srt 7KB
  439. 06 Matplotlib/009 Advanced Matplotlib Commands (Optional)_en.srt 6KB
  440. 13 Logistic Regression/015 Logistic Regression Exercise Project Overview_en.srt 6KB
  441. 16 Tree Based Methods_ Decision Tree Learning/003 Decision Tree - Terminology_en.srt 6KB
  442. 12 Cross Validation , Grid Search, and the Linear Regression Project/007 Linear Regression Project Overview_en.srt 6KB
  443. 10 Linear Regression/024 L1 Regularization - Lasso Regression - Background and Implementation_en.srt 5KB
  444. 14 KNN - K Nearest Neighbors/005 KNN Classification Project Exercise Overview_en.srt 5KB
  445. 12 Cross Validation , Grid Search, and the Linear Regression Project/001 Section Overview and Introduction_en.srt 5KB
  446. 09 Machine Learning Concepts Overview/005 Companion Book - Introduction to Statistical Learning_en.srt 5KB
  447. 17 Random Forests/003 Random Forests - Key Hyperparameters_en.srt 4KB
  448. 25 PCA - Principal Component Analysis and Manifold Learning/001 Introduction to Principal Component Analysis_en.srt 4KB
  449. 14 KNN - K Nearest Neighbors/004 KNN Coding with Python - Part Two - Choosing K_en.srt 4KB
  450. 20 Naive Bayes Classification and Natural Language Processing (Supervised Learning)/001 Introduction to NLP and Naive Bayes Section_en.srt 4KB
  451. 14 KNN - K Nearest Neighbors/001 Introduction to KNN Section_en.srt 4KB
  452. 22 K-Means Clustering/001 Introduction to K-Means Clustering Section_en.srt 4KB
  453. 04 NumPy/001 Introduction to NumPy_en.srt 3KB
  454. 17 Random Forests/001 Introduction to Random Forests Section_en.srt 3KB
  455. 10 Linear Regression/001 Introduction to Linear Regression Section_en.srt 3KB
  456. 18 Boosting Methods/001 Introduction to Boosting Section_en.srt 3KB
  457. 02 OPTIONAL_ Python Crash Course/005 Python Crash Course - Exercise Questions_en.srt 3KB
  458. 15 Support Vector Machines/001 Introduction to Support Vector Machines_en.srt 2KB
  459. 16 Tree Based Methods_ Decision Tree Learning/001 Introduction to Tree Based Methods_en.srt 2KB
  460. 04 NumPy/005 NumPy Exercises_en.srt 2KB
  461. 24 DBSCAN - Density-based spatial clustering of applications with noise/001 Introduction to DBSCAN Section_en.srt 1KB
  462. 23 Hierarchical Clustering/001 Introduction to Hierarchical Clustering_en.srt 1KB
  463. 11 Feature Engineering and Data Preparation/001 A note from Jose on Feature Engineering and Data Preparation.html 990B
  464. 01 Introduction to Course/004 Note on Environment Setup - Please read me!.html 857B
  465. 01 Introduction to Course/001 Welcome to the Course!.html 598B
  466. 13 Logistic Regression/001 Early Bird Note on Downloading .zip for Logistic Regression Notes.html 523B
  467. 02 OPTIONAL_ Python Crash Course/001 OPTIONAL_ Python Crash Course.html 472B
  468. 01 Introduction to Course/requirements.txt 221B
  469. 01 Introduction to Course/external-assets-links.txt 132B
  470. [FreeCourseLab.com].url 126B
  471. 24 DBSCAN - Density-based spatial clustering of applications with noise/external-assets-links.txt 103B