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[] Udemy - Machine Learning Essentials (2023) - Master core ML concepts

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  • 文件大小:16GB
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文件列表

  1. 5. Logistic Regression/3. Hypothesis Function.mp4 272MB
  2. 3. Linear Regression/7. Gradient Descent Code.mp4 271MB
  3. 19. Ensemble Learning Boosting/5. GBDT Algorithm.mp4 245MB
  4. 4. Linear Regression - Multiple Features/8. Code 04 - Gradient Computation.mp4 222MB
  5. 12. Naive Bayes Algorithm/7. Understanding Golf Dataset.mp4 219MB
  6. 19. Ensemble Learning Boosting/3. Boosting Mathematical Formulation.mp4 212MB
  7. 13. Multinomial Naive Bayes/4. Bernoulli Naive Bayes.mp4 205MB
  8. 15. Decision Trees/5. Information Gain.mp4 200MB
  9. 2. Supervised vs Unsupervised Learning/2. Supervised Learning Example.mp4 198MB
  10. 9. PROJECT - Face Recognition/7. Face Recognition 01 - Data Collection.mp4 198MB
  11. 3. Linear Regression/4. Loss Error Function.mp4 195MB
  12. 12. Naive Bayes Algorithm/6. Computing Likelihood.mp4 193MB
  13. 13. Multinomial Naive Bayes/3. Multinomial Naive Bayes Example.mp4 179MB
  14. 7. Principal Component Analysis (PCA)/3. Maximising Variance.mp4 178MB
  15. 3. Linear Regression/2. Notation.mp4 171MB
  16. 3. Linear Regression/11. Code 02 - Data Normalisation.mp4 171MB
  17. 12. Naive Bayes Algorithm/10. CODE - Likelihood.mp4 166MB
  18. 12. Naive Bayes Algorithm/5. Naive Bayes for Text Classification.mp4 161MB
  19. 14. PROJECT Spam Classifier/2. Data Clearning.mp4 158MB
  20. 19. Ensemble Learning Boosting/4. Concept of Pseudo Residuals.mp4 153MB
  21. 5. Logistic Regression/5. Gradient Update Rule.mp4 147MB
  22. 12. Naive Bayes Algorithm/3. Bayes Theorem Question.mp4 145MB
  23. 18. Ensemble Learning Bagging/3. Why Bagging Helps.mp4 143MB
  24. 13. Multinomial Naive Bayes/1. Multinomial Naive Bayes.mp4 141MB
  25. 7. Principal Component Analysis (PCA)/2. Conceptual Overview of PCA.mp4 141MB
  26. 3. Linear Regression/15. R2 Score.mp4 139MB
  27. 13. Multinomial Naive Bayes/5. Bernoulli Naive Bayes Example.mp4 138MB
  28. 15. Decision Trees/2. Decision Trees Example.mp4 137MB
  29. 15. Decision Trees/6. CODE Split Data.mp4 136MB
  30. 19. Ensemble Learning Boosting/2. Boosting Intuition.mp4 134MB
  31. 19. Ensemble Learning Boosting/7. CODE - Gradient Boosting Decision Trees.mp4 132MB
  32. 18. Ensemble Learning Bagging/2. Bagging Model.mp4 129MB
  33. 18. Ensemble Learning Bagging/5. Bias Variance Tradeoff.mp4 127MB
  34. 20. PROJECT Customer Churn Prediction/1. Project Overview.mp4 122MB
  35. 19. Ensemble Learning Boosting/1. Boosting Introduction.mp4 120MB
  36. 16. Decision Trees Implementation/2. CODE - Train Decision Tree.mp4 120MB
  37. 19. Ensemble Learning Boosting/8. XGBoost.mp4 119MB
  38. 19. Ensemble Learning Boosting/9. Adaptive Boosting (AdaBoost).mp4 119MB
  39. 15. Decision Trees/3. Entropy.mp4 118MB
  40. 3. Linear Regression/13. Code 04 - Modelling.mp4 118MB
  41. 18. Ensemble Learning Bagging/4. Random Forest Algorithm.mp4 118MB
  42. 16. Decision Trees Implementation/7. CODE - Prediction.mp4 116MB
  43. 18. Ensemble Learning Bagging/6. CODE Random Forest.mp4 116MB
  44. 12. Naive Bayes Algorithm/12. Implementing Naive Bayes - Sklearn.mp4 112MB
  45. 3. Linear Regression/6. Gradient Descent Optimisation.mp4 110MB
  46. 16. Decision Trees Implementation/8. Handling Numeric Features.mp4 110MB
  47. 13. Multinomial Naive Bayes/7. Gaussian Naive Bayes.mp4 109MB
  48. 12. Naive Bayes Algorithm/9. CODE - Conditional Probability.mp4 108MB
  49. 14. PROJECT Spam Classifier/3. WordCloud.mp4 106MB
  50. 5. Logistic Regression/2. Notation.mp4 105MB
  51. 3. Linear Regression/9. The Math of Training.mp4 105MB
  52. 4. Linear Regression - Multiple Features/5. Code 01 - Data Prep.mp4 104MB
  53. 3. Linear Regression/17. Code 07 - Visualisation.mp4 103MB
  54. 20. PROJECT Customer Churn Prediction/2. Exploratory Data Analysis.mp4 103MB
  55. 16. Decision Trees Implementation/6. CODE - Explore Decision Tree Model.mp4 102MB
  56. 20. PROJECT Customer Churn Prediction/7. Hyperparameter tuning.mp4 101MB
  57. 17. PROJECT Titanic Survival Prediction/1. Project Overview.mp4 101MB
  58. 1. Introduction/7. Automatic Speech Recognition.mp4 101MB
  59. 9. PROJECT - Face Recognition/9. Face Recognition 03 - Predictions using KNN.mp4 100MB
  60. 15. Decision Trees/9. Stopping Conditions.mp4 98MB
  61. 7. Principal Component Analysis (PCA)/4. Minimising Distances.mp4 95MB
  62. 3. Linear Regression/3. Hypothesis.mp4 95MB
  63. 17. PROJECT Titanic Survival Prediction/5. Handling Missing Values.mp4 95MB
  64. 13. Multinomial Naive Bayes/6. Bias Variance Tradeoff.mp4 94MB
  65. 2. Supervised vs Unsupervised Learning/3. Unsupervised Learning.mp4 94MB
  66. 3. Linear Regression/18. Code 08 - Trajectory [Optional].mp4 94MB
  67. 13. Multinomial Naive Bayes/8. CODE - Variants of Naive Bayes.mp4 94MB
  68. 15. Decision Trees/7. CODE Information Gain.mp4 94MB
  69. 17. PROJECT Titanic Survival Prediction/7. Visualize Decision Tree.mp4 93MB
  70. 13. Multinomial Naive Bayes/2. Laplace Smoothing.mp4 92MB
  71. 5. Logistic Regression/4. Binary Cross-Entropy Loss Function.mp4 91MB
  72. 8. K-Nearest Neigbours/4. KNN Algorithm Code.mp4 91MB
  73. 16. Decision Trees Implementation/10. Decision Trees for Regression.mp4 89MB
  74. 3. Linear Regression/12. Code 03 - Train Test Split.mp4 89MB
  75. 21. Deep Learning Introduction - Neural Network/8. Tensorflow Playground.mp4 89MB
  76. 4. Linear Regression - Multiple Features/1. Introduction.mp4 88MB
  77. 14. PROJECT Spam Classifier/1. Project Overview.mp4 87MB
  78. 12. Naive Bayes Algorithm/1. Bayes Theorem.mp4 87MB
  79. 4. Linear Regression - Multiple Features/9. Code 05 - Training Loop.mp4 87MB
  80. 5. Logistic Regression/1. Binary Classification Introduction.mp4 85MB
  81. 21. Deep Learning Introduction - Neural Network/11. CODE - Model Training and Testing.mp4 85MB
  82. 17. PROJECT Titanic Survival Prediction/2. Exploratory Data Analysis.mp4 84MB
  83. 16. Decision Trees Implementation/5. CODE - Train Child Nodes.mp4 83MB
  84. 19. Ensemble Learning Boosting/6. Bias Variance Tradeoff.mp4 83MB
  85. 17. PROJECT Titanic Survival Prediction/4. Data Preparation for ML Model.mp4 83MB
  86. 10. K-Means/6. Code 05 - Visualizing K-Means & Results.mp4 82MB
  87. 12. Naive Bayes Algorithm/4. Naive Bayes Algorithm.mp4 81MB
  88. 5. Logistic Regression/6. Code 01 - Data Prep.mp4 80MB
  89. 9. PROJECT - Face Recognition/3. Object Detection using Haarcascades.mp4 80MB
  90. 17. PROJECT Titanic Survival Prediction/3. Exploratory Data Analysis - II.mp4 79MB
  91. 9. PROJECT - Face Recognition/4. Face Detection in Images.mp4 79MB
  92. 4. Linear Regression - Multiple Features/6. Code 02 - Hypothesis.mp4 79MB
  93. 2. Supervised vs Unsupervised Learning/1. Supervised Learning Introduction.mp4 78MB
  94. 15. Decision Trees/1. Decision Trees Introduction.mp4 78MB
  95. 17. PROJECT Titanic Survival Prediction/6. Decision Tree Model Building.mp4 78MB
  96. 10. K-Means/4. Code 03 - Assigning Points.mp4 76MB
  97. 12. Naive Bayes Algorithm/2. Derivation of Bayes Theorem.mp4 75MB
  98. 20. PROJECT Customer Churn Prediction/6. Model Building.mp4 75MB
  99. 5. Logistic Regression/14. Multiclass Classification One Vs Rest.mp4 72MB
  100. 16. Decision Trees Implementation/4. CODE - Stopping Conditions.mp4 72MB
  101. 9. PROJECT - Face Recognition/8. Face Recognition 02 - Loading Data.mp4 72MB
  102. 12. Naive Bayes Algorithm/11. CODE - Prediction.mp4 71MB
  103. 11. Project - Dominant Color Extraction/5. Image in K-Colors.mp4 71MB
  104. 15. Decision Trees/4. CODE Entropy.mp4 70MB
  105. 18. Ensemble Learning Bagging/1. Ensemble Learning.mp4 69MB
  106. 3. Linear Regression/10. Code 01 - Data Generation.mp4 68MB
  107. 14. PROJECT Spam Classifier/6. Model Evaluation.mp4 68MB
  108. 20. PROJECT Customer Churn Prediction/4. Finding relations.mp4 67MB
  109. 1. Introduction/3. Machine Learning.mp4 67MB
  110. 15. Decision Trees/8. Construction of Decision Trees.mp4 66MB
  111. 10. K-Means/3. Code 02 - Init Centers.mp4 66MB
  112. 1. Introduction/6. Natural Language Processing.mp4 64MB
  113. 6. Dimensionality Reduction Feature Selection/6. Feature Selection - Code.mp4 64MB
  114. 7. Principal Component Analysis (PCA)/1. Introduction to PCA.mp4 63MB
  115. 5. Logistic Regression/10. Code 05 - Training Loop.mp4 62MB
  116. 20. PROJECT Customer Churn Prediction/5. Data Preparation.mp4 61MB
  117. 16. Decision Trees Implementation/1. CODE - Decision Tree Node.mp4 61MB
  118. 12. Naive Bayes Algorithm/8. CODE - Prior Probability.mp4 61MB
  119. 10. K-Means/1. K-Means Algorithm.mp4 60MB
  120. 16. Decision Trees Implementation/3. CODE - Assign Target Variable to Each Node.mp4 60MB
  121. 10. K-Means/5. Code 04 - Updating Centroids.mp4 59MB
  122. 16. Decision Trees Implementation/9. Bias Variance Tradeoff.mp4 59MB
  123. 21. Deep Learning Introduction - Neural Network/5. Neural Networks.mp4 58MB
  124. 5. Logistic Regression/12. Code 07 - Predictions & Accuracy.mp4 56MB
  125. 1. Introduction/4. Deep Learning.mp4 54MB
  126. 3. Linear Regression/14. Code 05 - Predictions.mp4 54MB
  127. 11. Project - Dominant Color Extraction/3. Finding Clusters.mp4 54MB
  128. 8. K-Nearest Neigbours/8. KNN Pros and Cons.mp4 54MB
  129. 21. Deep Learning Introduction - Neural Network/4. Gradient Descent Updates.mp4 53MB
  130. 20. PROJECT Customer Churn Prediction/3. Data Visualisation.mp4 53MB
  131. 14. PROJECT Spam Classifier/5. Model Building.mp4 52MB
  132. 3. Linear Regression/8. Gradient Descent - for Linear Regression.mp4 52MB
  133. 4. Linear Regression - Multiple Features/11. Code 06 - Evaluation.mp4 51MB
  134. 7. Principal Component Analysis (PCA)/8. PCA Code.mp4 51MB
  135. 22. PROJECT Pokemon Image Classification/5. Data Preprocessing.mp4 50MB
  136. 22. PROJECT Pokemon Image Classification/9. Model evaluation.mp4 50MB
  137. 21. Deep Learning Introduction - Neural Network/7. Why Neural Nets.mp4 50MB
  138. 1. Introduction/1. Course Overview.mp4 50MB
  139. 9. PROJECT - Face Recognition/5. Face Detection in Live Video.mp4 49MB
  140. 22. PROJECT Pokemon Image Classification/2. The Data.mp4 49MB
  141. 1. Introduction/2. Artificial Intelligence.mp4 49MB
  142. 7. Principal Component Analysis (PCA)/5. Eigen Values & Eigen Vectors.mp4 48MB
  143. 3. Linear Regression/5. Training Idea.mp4 48MB
  144. 21. Deep Learning Introduction - Neural Network/10. CODE - Model Building.mp4 46MB
  145. 7. Principal Component Analysis (PCA)/9. Choosing the right dimensions.mp4 45MB
  146. 5. Logistic Regression/9. Code 04 - Gradient Computation.mp4 45MB
  147. 8. K-Nearest Neigbours/1. Introduction.mp4 45MB
  148. 7. Principal Component Analysis (PCA)/7. Understanding Eigen Values.mp4 45MB
  149. 14. PROJECT Spam Classifier/4. Text Featurization.mp4 44MB
  150. 1. Introduction/8. Reinforcement Learning.mp4 44MB
  151. 21. Deep Learning Introduction - Neural Network/9. CODE -Data Preparation.mp4 44MB
  152. 4. Linear Regression - Multiple Features/4. Training & Gradient Updates.mp4 43MB
  153. 5. Logistic Regression/11. Code 06 - Visualise Decision Boundary.mp4 43MB
  154. 1. Introduction/5. Computer Vision.mp4 43MB
  155. 22. PROJECT Pokemon Image Classification/4. Data Loading.mp4 43MB
  156. 21. Deep Learning Introduction - Neural Network/3. How does a perceptron Learns.mp4 43MB
  157. 11. Project - Dominant Color Extraction/4. Dominant Color Swatches.mp4 40MB
  158. 16. Decision Trees Implementation/11. Decision Tree Code - Sklearn.mp4 37MB
  159. 22. PROJECT Pokemon Image Classification/1. Introduction.mp4 36MB
  160. 4. Linear Regression - Multiple Features/12. Linear Regression using Sk-Learn.mp4 35MB
  161. 8. K-Nearest Neigbours/2. KNN Idea.mp4 35MB
  162. 9. PROJECT - Face Recognition/2. OpenCV - Video Input from WebCam.mp4 34MB
  163. 5. Logistic Regression/7. Code 02 - Hypothesis Logit Model.mp4 34MB
  164. 21. Deep Learning Introduction - Neural Network/2. A Neuron.mp4 34MB
  165. 9. PROJECT - Face Recognition/1. OpenCV - Working with Images.mp4 34MB
  166. 5. Logistic Regression/15. Multiclass Classification One Vs One.mp4 33MB
  167. 22. PROJECT Pokemon Image Classification/6. Model Architecture.mp4 33MB
  168. 4. Linear Regression - Multiple Features/3. Loss Function.mp4 33MB
  169. 22. PROJECT Pokemon Image Classification/3. Structured Data.mp4 32MB
  170. 22. PROJECT Pokemon Image Classification/10. Predictions.mp4 30MB
  171. 4. Linear Regression - Multiple Features/10. A Note about Shapes.mp4 30MB
  172. 5. Logistic Regression/13. Logistic Regression using Sk-Learn.mp4 30MB
  173. 8. K-Nearest Neigbours/3. KNN Data Prep.mp4 29MB
  174. 3. Linear Regression/16. Code 06 - Evaluation.mp4 29MB
  175. 4. Linear Regression - Multiple Features/2. Hypothesis.mp4 29MB
  176. 21. Deep Learning Introduction - Neural Network/1. Biological Neural Network.mp4 28MB
  177. 21. Deep Learning Introduction - Neural Network/6. 3 Layer NN.mp4 28MB
  178. 3. Linear Regression/1. Introduction to Linear Regression.mp4 27MB
  179. 11. Project - Dominant Color Extraction/1. Introduction.mp4 25MB
  180. 11. Project - Dominant Color Extraction/2. Reading Images.mp4 24MB
  181. 6. Dimensionality Reduction Feature Selection/3. Filter Method.mp4 23MB
  182. 6. Dimensionality Reduction Feature Selection/4. Wrapper Method.mp4 23MB
  183. 4. Linear Regression - Multiple Features/7. Code 03 - Loss Function.mp4 23MB
  184. 5. Logistic Regression/8. Code 03 - Binary Cross Entropy Loss.mp4 19MB
  185. 10. K-Means/2. Code 01 - Data Prep.mp4 19MB
  186. 22. PROJECT Pokemon Image Classification/7. Softmax Function.mp4 18MB
  187. 7. Principal Component Analysis (PCA)/6. PCA Summary.mp4 18MB
  188. 22. PROJECT Pokemon Image Classification/8. Model Training.mp4 17MB
  189. 6. Dimensionality Reduction Feature Selection/1. Curse of Dimensionality.mp4 17MB
  190. 8. K-Nearest Neigbours/7. KNN and Data Standardisation.mp4 15MB
  191. 9. PROJECT - Face Recognition/6. Face Recognition Project Intro.mp4 15MB
  192. 6. Dimensionality Reduction Feature Selection/2. Feature Selection Vs. Feature Extraction.mp4 15MB
  193. 8. K-Nearest Neigbours/5. Euclidean and Manhattan Distance.mp4 15MB
  194. 6. Dimensionality Reduction Feature Selection/5. Embedded Method.mp4 13MB
  195. 8. K-Nearest Neigbours/6. Deciding value of K.mp4 7MB
  196. 6. Dimensionality Reduction Feature Selection/6.1 train.csv 120KB
  197. 17. PROJECT Titanic Survival Prediction/1.1 titanic_train.csv 59KB
  198. 1. Introduction/9. Pre-requisites.html 889B
  199. 12. Naive Bayes Algorithm/7.1 golf.csv 430B
  200. 8. K-Nearest Neigbours/9. KNN using Sk-Learn.html 405B
  201. 1. Introduction/10. Code Repository.html 236B
  202. 22. PROJECT Pokemon Image Classification/1.1 Dataset Link.html 129B
  203. 0. Websites you may like/[FreeCourseSite.com].url 127B
  204. 10. K-Means/0. Websites you may like/[FreeCourseSite.com].url 127B
  205. 15. Decision Trees/0. Websites you may like/[FreeCourseSite.com].url 127B
  206. 4. Linear Regression - Multiple Features/0. Websites you may like/[FreeCourseSite.com].url 127B
  207. 0. Websites you may like/[CourseClub.Me].url 122B
  208. 10. K-Means/0. Websites you may like/[CourseClub.Me].url 122B
  209. 15. Decision Trees/0. Websites you may like/[CourseClub.Me].url 122B
  210. 4. Linear Regression - Multiple Features/0. Websites you may like/[CourseClub.Me].url 122B
  211. 0. Websites you may like/[GigaCourse.Com].url 49B
  212. 10. K-Means/0. Websites you may like/[GigaCourse.Com].url 49B
  213. 15. Decision Trees/0. Websites you may like/[GigaCourse.Com].url 49B
  214. 4. Linear Regression - Multiple Features/0. Websites you may like/[GigaCourse.Com].url 49B