[Revised Edition] The Complete Guide to Python Machine Learning

35. Building your first machine learning model - Iris species prediction

36. Getting familiar with scikit-learn's underlying framework - Introduction to key APIs/modules and built-in example datasets

37. Separation of training and test data sets

38. Cross-validation - Understanding K-Fold and Stratified K-Fold - 01

39. Cross-Validation - Understanding K-Fold and Stratified K-Fold - 02

40. Cross-validation performance evaluation cross_val_score() and GridSearchCV for hyperparameter tuning - 01

41. Cross-validation performance evaluation cross_val_score() and GridSearchCV for hyperparameter tuning - 02

42. Data Preprocessing - Encoding - 01

43. Data Preprocessing - Encoding - 02

44. Data Preprocessing - Scaling - 01

45. Data Preprocessing - Scaling - 02

46. Titanic Survivor Prediction with Scikit-learn - 01

47. Titanic Survivor Prediction with Scikit-learn - 02

49. Introduction to Confusion Matrix, Precision, and Recall

50. Tradeoff between precision and recall - 01

51. Tradeoff between precision and recall - 02

52. Understanding F1 Score and ROC-AUC - 01

53. Understanding F1 Score and ROC-AUC - 02

54. Assessment Practice - Pima Indian Diabetes Prediction

56. Installing Graphviz for visualizing decision tree models

57. Understanding the Structure of Decision Tree Models through Visualization

58. Understanding Decision Tree Key Hyperparameters

59. Understanding Decision Tree Feature Importance

60. Understanding Decision Tree Overfitting

61. Decision Tree Practice Using User Behavior Recognition Data - 01

62. Decision Tree Practice Using User Behavior Recognition Data - 02

63. Overview of ensemble learning and understanding voting

64. Understanding Bagging and Random Forests

65. Random Forest Practice

66. Understanding Boosting and Introduction and Practice of Gradient Boosting

67. Introduction to XGBoost

68. Wisconsin Breast Cancer Prediction Using XGBoost (Using Python Native XGBoost)

69. Wisconsin Breast Cancer Prediction Using XGBoost (using scikit-learn Wrapper XGBoost)

70. Introducing LightGBM

71. Wisconsin Breast Cancer Prediction Using LightGBM

72. Hyperparameter tuning problem based on grid search

73. Bayesian Optimization Overview

74. Introducing the HyperOpt package for Bayesian optimization

75. HyperOpt Basics Tutorial

76. Tuning XGBoost hyperparameters using HyperOpt

77. Classification Exercise 1: Predicting Santander Bank Customer Satisfaction in Kaggle Competition - 01

78. Classification Exercise 1: Predicting Santander Bank Customer Satisfaction in Kaggle Competition - 02

79. Classification Exercise 1: Predicting Santander Bank Customer Satisfaction in Kaggle Competition - 03

81. Classification Exercise 2: Credit Card Fraud Prediction Exercise - 02

82. Classification Exercise 2: Credit Card Fraud Prediction Exercise - 03

83. Classification Exercise 2: Credit Card Fraud Prediction Exercise - 04

84. Classification Exercise 2: Credit Card Fraud Prediction Exercise - 05

85. Introducing the Stacking Model - Basic Stacking

86. Introducing the Stacking Model - Cross-Validation Stacking

87. Classification Summary

88. <New> Understanding Feature Selection

89. <NEW> Recursive Feature Elimination and SelectFromModel Practice

90. <NEW> Introduction and Practice of Permutation Importance

91. <NEW> Why feature importance cannot be an absolute criterion for feature selection?

93. Understanding the regression cost function RSS and gradient descent

94. Minimizing the cost function RSS via gradient descent - Partial differentiation of the RSS function

95. Minimizing the cost function RSS via gradient descent - Implementing gradient descent in Python code

96. Introduction to scikit-learn LinearRegression class and regression evaluation metrics

97. Scikit-learn API for regression evaluation and caveats

99. Understanding Polynomial Regression and Predicting Boston Housing Prices Using Polynomial Regression

100. Understanding Underfitting and Overfitting Using Polynomial Regression

101. Overview of Regularized Linear Regression

102. Understanding Ridge Regression

103. Understanding Lasso and Elastic Net Regression

104. Data transformation for linear regression models

105. Understanding Logistic Regression

106. Wisconsin Cancer Prediction Model Practice Using Logistic Regression

107. Understanding Regression Trees

108. Predicting Boston House Prices Using Regression Trees

109. Regression Exercise 1: Bike Rental (Sharing) Demand Forecasting - 01

110. Regression Exercise 1: Bike Rental (Sharing) Demand Forecasting - 02

111. Regression Practice 2: Kaggle Competition House Price Prediction - Advanced Regression Techniques - 01

112. Regression Practice 2: Kaggle Competition House Price Prediction - Advanced Regression Techniques - 02

113. Regression Practice 2: Kaggle Competition House Price Prediction - Advanced Regression Techniques - 03

114. Regression Practice 2: Kaggle Competition House Price Prediction - Advanced Regression Techniques - 04

115. Regression Summary

116. Dimension reduction overview

117. Understanding PCA(Principal Component Analysis)

118. Understanding scikit-learn PCA class

119. Dimensionality reduction exercise on the iris data set using PCA

120. Dimensionality Reduction Practice on Credit Card Dataset Using PCA

121. Understanding LDA and Dimension Reduction Practice on Iris Dataset

122. Understanding SVD

123. Matrix factorization practice using SVD and understanding NMF (Non Negative Matrix Factorization)

125. K-Means Clustering Practice Using Scikit-learn - 01

126. K-Means Clustering Practice Using Scikit-learn - 02

127. K-Means Clustering Practice Using Scikit-learn - 03

128. Clustering Performance Evaluation - Understanding Silhouette Analysis

129. Clustering Performance Evaluation - Silhouette Analysis Practice

130. A method for optimizing the number of clusters by visualizing the average silhouette coefficient per cluster

131. Understanding MeanShift Clustering

132. Understanding KDE(Kernel Density Estimation) and MeanShift Clustering Practice Using Scikit-learn

133. Understanding GMM(Gaussian Mixture Model) Clustering

134. GMM clustering practice using scikit-learn

135. Understanding DBSCAN Clustering

136. DBSCAN clustering practice using scikit-learn

137. Clustering Exercise 1: Customer Segmentation Implementation Exercise

139. Practice various preprocessing of text data

140. Understanding Feature Vectorization of Text - Simple Count and TF-IDF Feature Vectorization of BOW(Bag of Words) Model

141. Introducing the main feature vectorization classes in scikit-learn

142. Understanding sparse matrices

143. Text Classification - 20 Newsgroup Classification Practice - 01

144. Text Classification - 20 Newsgroup Classification Practice - 02

145. Text Classification - 20 Newsgroup Classification Practice - 03

146. Understanding sentiment analysis and practicing sentiment analysis of IMDB movie reviews based on supervised learning

147. Understanding sentiment analysis based on a sentiment vocabulary dictionary and practicing sentiment analysis of IMDB movie reviews using it

148. Understanding Topic Modeling and LDA

149. 20 Newsgroup Topic Modeling Practice Using Scikit-learn LDA - 01

150. 20 Newsgroup Topic Modeling Practice Using Scikit-learn LDA - 02

151. Understanding Document Clustering and Practice with Document Clustering Using Opinion Review Data Set - 01

152. Document Clustering Practice Using Opinion Review Dataset - 02

154. Practice implementing document similarity using cosine similarity

155. Practice implementing document similarity using the Opinion Review data set

156. Introduction to Korean NLP Overview and Korean Morphological Engine KoNLPy

157. Installing the Korean morpheme engine KoNLPy

158. Korean NLP Practice - Naver Movie Review Sentiment Analysis - 01

159. Korean NLP Practice - Naver Movie Review Sentiment Analysis - 02

160. Practical Text Analysis: 01 - Kaggle Competition Mercari Price Suggestion Hands-on Overview

161. Practical Text Analysis: 02 - Downloading the Mercari Price Suggestion Dataset and Using the Kaggle Kernel

162. Practical Text Analysis: 03 - Analyzing and Transforming the Mercari Price Suggestion Dataset

163. Practical Text Analysis: 04 - Performing Mercari Price Suggestion Feature Encoding and Feature Vectorization

164. Practical Text Analysis: 05 - Learning, Predicting, and Evaluating the Mercari Price Suggestion Model

165. Text Analysis Summary

167. Understanding content-based filtering and implementing content-based filtering using the TMDB5000 data set - 01

168. Content-based filtering implementation practice using TMDB5000 data set - 02

170. Implementation of item-based nearest neighbor collaborative filtering movie recommendation

171. Understanding collaborative filtering based on latent factors and matrix decomposition using gradient descent

172. Implementation of matrix factorization based on gradient descent using Python code and implementation of movie recommendation based on latent factor collaborative filtering

173. Overview of Surprise, a recommendation system package for Python - Introduction to the main modules

174. Basic implementation of a recommendation system using Surprise

175. Introduction to Surprise's recommendation algorithm and an overview of cross-validation and hyperparameter tuning

176. Building a personalized movie recommendation system using Surprise

177. Recommended Summary

178. Starting to visualize

179. Overview and comparison of Matplotlib and Seaborn

180. Understanding Matplotlib - Figures and Axes

181. Matplotlib Figure and Axes Practice

182. Visualization Practice Using Various Components of Matplotlib - 01

183. Visualization Practice Using Various Components of Matplotlib - 02

184. Types of visualization based on the type of information

185. Understanding Seaborn's Axis-level and Figure-level functions

186. Seaborn Histogram Visualization Practice

187. Seaborn Bar Plot Visualization Practice - 01

188. Seaborn Bar Plot Visualization Practice - 02

189. Seaborn Violin Plot Practice

190. Learn visualization techniques using subplots in Seaborn

191. Seaborn Box Plot and Scatter Plot Practice

192. Seaborn Correlation Heatmap Practice