High-end practical Python data analysis and machine learning combat numpy/pandas/matplotlib and other commonly used libraries

Course Description:??

The course style is easy to understand, real case actual cases. Carefully select the real data set as a case, through the Python Data Science library Numpy,pandas,matplot combined with the machine learning Library Scikit-learn to complete some of the column machine learning cases. The course is based on actual combat and all lessons are combined with code to demonstrate how to use these Python libraries to complete a real data case. Combining the algorithm with the project, choosing the classic Kaggle project, starting with the data preprocessing, we start with a step-by-step code to get you started machine learning. Designed to help students get started quickly how to use the Python library to complete machine learning cases.

----------------------Course Catalogue------------------------------

│?? ├<01-python Scientific Computing Library-numpy>
│?? │?? ├ Lesson 01. Course Introduction (Subject and outline). flv
│?? │?? ├ Lesson 02. Machine Learning Overview. flv
│?? │?? ├ Class 03. Install the Python environment using anaconda. flv
│?? │?? ├ Lesson 04. Course data, code, PPT (in the Resources Interface). swf
│?? │?? ├ class 05. Scientific Computing Library numpy.flv
│?? │?? ├ class 06. NumPy infrastructure. flv
│?? │?? ├ class 07. NumPy Matrix Foundation. flv_d.flv
│?? │?? ├ Class 08. NumPy commonly used functions. flv_d.flv
│?? │?? ├ class 09. Matrix common Operations. flv_d.flv
│?? │?? └ class 10. Different copy operation comparison. flv_d.flv
│?? ├<02-python Data Analysis Processing Library-pandas>
│?? │?? ├ class 11. Pandas data read. flv
│?? │?? ├ class 12. Pandas indexes and calculations. flv_d.flv
│?? │?? ├ class 13. Pandas data preprocessing instance. flv_d.flv
│?? │?? ├ Class 14. Pandas commonly used pretreatment methods. flv_d.flv
│?? │?? ├ class 15. Pandas the custom function. flv_d.flv
│?? │?? └ class 16. Series structure. flv_d.flv
│?? ├<03-python Data Visualization Library-matplotlib>
│?? │?? ├ lesson 17. Line chart drawing. flv
│?? │?? ├ Lesson 18. Sub-chart operations. flv_d.flv
│?? │?? ├ class 19. Bar and scatter plots. flv_d.flv
│?? │?? ├ class 20. Column chart and box diagram. flv_d.flv
│?? │?? └ class 21. Details set. flv_d.flv
│?? ├<04-python Visual Library Seaborn>
│?? │?? ├ class 22. Seaborn Introduction. flv
│?? │?? ├ Lesson 23. Overall layout style settings. flv_d.flv
│?? │?? ├ Class 24. Style details settings. flv_d.flv
│?? │?? ├ class 25. Color palette. flv_d.flv
│?? │?? ├ class 26. Color palette. flv_d.flv
│?? │?? ├ Lesson 27. Palette color settings. flv_d.flv
│?? │?? ├ class 28. Single variable analysis drawing. flv_d.flv
│?? │?? ├ Lesson 29. Regression Analysis Drawing. flv_d.flv
│?? │?? ├ Lesson 30. Multivariate Analysis drawing. flv_d.flv
│?? │?? ├ Class 31. Classification attribute drawing. flv_d.flv
│?? │?? ├ class 32. Facetgrid how to use it. flv_d.flv
│?? │?? └ Class 33. Facetgrid draw multivariable. flv_d.flv
│?? ├<05-Regression algorithm >
│?? │?? ├ class 34. Heat map Drawing. flv_d.flv
│?? │?? ├ Class 35. A review of regression algorithms. flv_d.flv
│?? │?? ├ class 36. Regression error principle derivation. flv_d.flv
│?? │?? ├ Class 37. How the regression algorithm obtains the optimal solution. flv_d.flv
│?? │?? ├ Class 38. Simple linear regression is completed based on formula derivation. flv_d.flv
│?? │?? └ class 39. Logistic regression and gradient descent. flv_d.flv
│?? ├<06-Decision Tree >
│?? │?? ├ lesson 40. Use gradient descent to solve regression problems. flv_d.flv
│?? │?? ├ Class 41. A survey of decision tree algorithms. flv_d.flv
│?? │?? ├ class 42. Decision tree Entropy principle. flv_d.flv
│?? │?? ├ Class 43. Decision tree construction example. flv_d.flv
│?? │?? ├ Class 44. Information gain principle. flv_d.flv
│?? │?? ├ Lesson 45. The effect of the information gain rate. flv_d.flv
│?? │?? ├ class 46. Decision tree pruning strategy. flv_d.flv
│?? │?? └ Class 47. Random forest model. flv_d.flv
│?? ├<07-Bayesian algorithm >
│?? │?? ├ Lesson 48. The decision tree parameters are detailed. flv_d.flv
│?? │?? ├ class 49. Bayesian algorithm Overview. flv_d.flv
│?? │?? ├ lesson 50. Bayesian derivation example. flv_d.flv
│?? │?? ├ class 51. Bayesian spelling correction example. flv_d.flv
│?? │?? └ class 52. Spam filter instances. flv_d.flv
│?? ├<08-Support Vector Machine >
│?? │?? ├ Lesson 53. Bayesian Implementation spell checker. flv_d.flv
│?? │?? ├ class 54. Support Vector Confidential solve the problem. flv_d.flv
│?? │?? ├ Class 55. Support Vector Machine objective function. flv_d.flv
│?? │?? ├ class 56. Support Vector machine objective function solving. flv_d.flv
│?? │?? ├ class 57. Support Vector Machine Solution example. flv_d.flv
│?? │?? ├ class 58. Support vector machine soft interval problem. flv_d.flv
│?? │?? └ class 59. Support Vector kernel transformation. flv_d.flv
│?? ├<09-Neural network >
│?? │?? ├ Class 60. The SMO algorithm solves the support vector machine. flv_d.flv
│?? │?? ├ class 61. Initial knowledge of neural networks. flv_d.flv
│?? │?? ├ class 62. Computer vision challenges. flv_d.flv
│?? │?? ├ class 63. K Neighbor attempt image classification. flv_d.flv
│?? │?? ├ class 64. The role of hyper-parameters. flv_d.flv
│?? │?? ├ Class 65. Principle of linear classification-flv_d.flv
│?? │?? ├ Class 66. Neural network-loss function. flv_d.flv
│?? │?? ├ class 67. Neural network-regularization penalty. flv_d.flv
│?? │?? ├ class 68. Neural network-softmax classifier. flv_d.flv
│?? │?? ├ lesson 69. Neural Networks-Optimizing the image interpretation. flv_d.flv
│?? │?? ├ lesson 70. Neural network-gradient descent detail problem. flv_d.flv
│?? │?? ├ Class 71. Neural network-reverse propagation. flv_d.flv
│?? │?? ├ class 72. Neural network architecture. flv_d.flv
│?? │?? ├ Lesson 73. Example of a neural network demonstration. flv_d.flv
│?? │?? └ class 74. Neural network over-fitting solution. flv_d.flv
│?? ├<10-xgboost Integration Algorithm >
│?? │?? ├ Lesson 75. Feel the power of the neural network. flv_d.flv
│?? │?? ├ class 76. Integrated algorithm ideas. flv_d.flv
│?? │?? ├ lesson 77.xgboost Fundamentals. flv_d.flv
│?? │?? ├ Lesson 78.xgboost objective function derivation. flv_d.flv
│?? │?? ├ class 79.xgboost solution example. flv_d.flv
│?? │?? ├ class 80.xgboost installation. flv_d.flv
│?? │?? └ 81.xgboost actual demo. flv_d.flv
│?? ├<11-Natural language processing word vector model-word2vec>
│?? │?? ├ class 82. AdaBoost algorithm overview. flv_d.flv
│?? │?? ├ Lesson 83. Natural language processing and deep learning plus ff1318860.flv_d.flv
│?? │?? ├ class 84. Language model. flv_d.flv
│?? │?? ├ class 85.-n-gram model. flv_d.flv
│?? │?? ├ class 86. Word vector. flv_d.flv
│?? │?? ├ class 87. Neural network model. flv_d.flv
│?? │?? ├ class 88. Hierarchical.Softmax.flv_d.flv
│?? │?? ├ class 89. Cbow model instance. flv_d.flv
│?? │?? ├ Class 90. Cbow to solve the target. flv_d.flv
│?? │?? └ class 91. Gradient rise solution. flv_d.flv
│?? ├<12-k Neighborhood and Clustering >
│?? │?? ├ class 92. Negative sampling model. flv_d.flv
│?? │?? ├ class 93. Unsupervised clustering issues. flv_d.flv
│?? │?? ├ class 94. Clustering results and outlier analysis. flv_d.flv
│?? │?? ├ class 95. The K-means cluster case is an assessment of NBA players. flv_d.flv
│?? │?? ├ class 96. Use Kmeans for image compression. flv_d.flv
│?? │?? └ class 97. K Nearest Neighbor algorithm principle. flv_d.flv
│?? ├&LT;13-PCA dimensionality reduction and SVD matrix decomposition >
│?? │?? ├ Class 100. PCA instance. flv_d.flv
│?? │?? ├ Class 101. SVD singular value decomposition principle. flv_d.flv
│?? │?? ├ class 98. K Nearest Neighbor Algorithm code implementation. flv_d.flv
│?? │?? └ class 99. PCA Fundamentals. flv_d.flv
│?? ├<14-scikit-learn model establishment and evaluation >
│?? │?? ├ class 102. SVD recommendation System Application examples. flv_d.flv
│?? │?? ├ class 103. Use the Python library to analyze car fuel efficiency. flv
│?? │?? ├ class 104. Use the Scikit-learn library to establish a regression model. flv_d.flv
│?? │?? ├ Lesson 105. Use logistic regression to improve model effects. flv_d.flv
│?? │?? ├ Lesson 106: Model effect measurement criteria. flv_d.flv
│?? │?? ├ class 107. The value of ROC indicators and test sets. flv_d.flv
│?? │?? └ class 108. Cross-validation. flv_d.flv
│?? ├<15-python Library Analysis of Kobe Bryant career data >
│?? │?? ├ class 109. Multi-category issues. flv_d.flv
│?? │?? ├ Class 110. Kobe.bryan Career data reading and introduction. flv
│?? │?? ├ class 111. Visualization of feature data. flv_d.flv
│?? │?? └ class 112. Data preprocessing. flv_d.flv
│?? ├<16-Machine Learning Project-Titanic Rescue Forecast >
│?? │?? ├ Lesson 113. Build the model using Scikit-learn. flv_d.flv
│?? │?? ├ class 114. Crew data Analysis-flv
│?? │?? ├ class 115. Data preprocessing. flv_d.flv
│?? │?? ├ lesson 116. Use regression algorithms for predictions. flv_d.flv
│?? │?? └ class 117. Use a random forest improvement model. flv_d.flv
│?? ├<17-Machine Learning Project Combat-transaction data anomaly detection >
│?? │?? ├ class 118. Analysis of the importance of random forest characteristics. flv_d.flv
│?? │?? ├ Lesson 119. Case background and objectives. flv_d.flv
│?? │?? ├ class 120. Sample unbalanced solution. flv_d.flv
│?? │?? ├ Class 121. The next sampling strategy. flv_d.flv
│?? │?? ├ Lesson 122. Cross-validation. flv_d.flv
│?? │?? ├ class 123. Model evaluation method. flv_d.flv
│?? │?? ├ Lesson 124. Regularization of punishment. flv_d.flv
│?? │?? ├ class 125. Logistic regression model. flv_d.flv
│?? │?? ├ class 126. Confusion matrix. flv_d.flv
│?? │?? └ class 127. The effect of the logistic regression threshold on the result. flv_d.flv
│?? ├<18-python Text data analysis: News classification Tasks >
│?? │?? ├ class 128. Smote sample generation strategy. flv_d.flv
│?? │?? ├ Lesson 129. Text analysis and keyword extraction. flv_d.flv
│?? │?? ├ class 130. Similarity calculation. flv_d.flv
│?? │?? ├ class 131. News data and mission brief. flv_d.flv
│?? │?? ├ class 132. TF-IDF keyword extraction. flv_d.flv
│?? │?? └ class 133. LDA modeling. flv_d.flv
│?? ├<19-python Time Series Analysis >
│?? │?? ├ class 134. News classification based on Bayesian algorithm. flv_d.flv
│?? │?? ├ Lesson 135. Introduction to Chapters. flv
│?? │?? ├ class 136. Pandas generates a time series. flv_d.flv
│?? │?? ├ class 137. Pandas data resampling. flv_d.flv
│?? │?? ├ class 138. Pandas sliding window. flv_d.flv
│?? │?? ├ class 139. Data smoothness and difference method. flv_d.flv
│?? │?? ├ class 140. Arima model. flv_d.flv
│?? │?? ├ class 141. Correlation function Evaluation method. flv_d.flv
│?? │?? ├ Lesson 142. Establish an ARIMA model. flv_d.flv
│?? │?? ├ class 143. Parameter selection. flv_d.flv
│?? │?? ├ class 144. Stock forecast cases. flv_d.flv
│?? │?? └ class 145. Use the Tsfresh Library for classification tasks. flv_d.flv
│?? ├<20-using Gensim library to construct Chinese wiki Baidu data word vector model >
│?? │?? ├ class 146. wikipedia entry EDA.flv_d.flv
│?? │?? ├ class 147. Use the Gensim library to construct the word vector. flv_d.flv
│?? │?? ├ class 148. Wikipedia Chinese data processing. flv_d.flv
│?? │?? └ class 149. Gensim structural Word2vec model. flv_d.flv
│?? ├<21-machine learning projects-loan applications maximize profits >
│?? │?? ├ lesson 150. Test the similarity of the model results. flv_d.flv
│?? │?? ├ class 151. Data cleansing filtering useless features. flv_d.flv
│?? │?? ├ class 152. Data preprocessing. flv_d.flv
│?? │?? └ class 153. Conditions and practices for maximizing profits. flv_d.flv
│?? ├<22-Machine Learning Project Combat-user Churn alert >
│?? │?? ├ class 154. Predict the results and solve the problem of sample imbalance. flv_d.flv
│?? │?? ├ class 155. Data background introduction. flv_d.flv
│?? │?? ├ Class 156. Data preprocessing. flv_d.flv
│?? │?? ├ Lesson 157. Try multiple classifier effects. flv_d.flv
│?? │?? └ lesson 158. The significance of the measurement of the outcome. flv_d.flv
│?? ├<23-Exploratory data analysis-soccer match Datasets >
│?? │?? ├ class 159. The application threshold is worth the result. flv_d.flv
│?? │?? ├ lesson 160. Introduction to the content. flv_d.flv
│?? │?? ├ Lesson 161. Data background introduction. flv
│?? │?? ├ class 162. Data reading and preprocessing. flv_d.flv
│?? │?? ├ class 163. Data segmentation module. flv_d.flv
│?? │?? ├ lesson 164. Visual analysis of missing values. flv_d.flv
│?? │?? ├ class 165. Feature visualization display. flv_d.flv
│?? │?? ├ class 166. Analysis of relationships among multiple features. flv_d.flv
│?? │?? └ class 167. Visual analysis of reports. flv_d.flv
│?? ├<24-Exploratory Data Analysis-agri-food Organization Datasets >
│?? │?? ├ class 168. The relationship between red card and color. flv_d.flv
│?? │?? ├ Lesson 169. Introduction to data background. flv_d.flv
│?? │?? ├ Class 170. Data slicing analysis. flv_d.flv
│?? │?? ├ class 171. Univariate analysis. flv_d.flv
│?? │?? ├ class 172. Kurtosis and skewness. flv_d.flv
│?? │?? ├ class 173. Data logarithmic transformation. flv_d.flv
│?? │?? └ class 174. Data analysis dimension. flv_d.flv
│?? ├<25-Machine Learning Project actual combat-http log Cluster analysis >
│?? │?? ├ class 175. Visual display of variable relationships. flv_d.flv
│?? │?? ├ class 176. Establish feature engineering. flv_d.flv
│?? │?? ├ class 177. Feature data preprocessing. flv_d.flv
│?? │?? └ class 178. Using clustering algorithm to derive the anomalous IP point. flv_d.flv

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High-end practical Python data analysis and machine learning combat numpy/pandas/matplotlib and other commonly used libraries