舉報 
會員
The Data Science Workshop
最新章節：
Summary
Wherethere’sdata,there’sinsight.Withsomuchdatabeinggenerated,thereisimmensescopetoextractmeaningfulinformationthat’llboostbusinessproductivityandprofitability.Bylearningtoconvertrawdataintogame-changinginsights,you’llopennewcareerpathsandopportunities.TheDataScienceWorkshopbeginsbyintroducingdifferenttypesofprojectsandshowingyouhowtoincorporatemachinelearningalgorithmsinthem.You’lllearntoselectarelevantmetricandevenassesstheperformanceofyourmodel.Totunethehyperparametersofanalgorithmandimproveitsaccuracy,you’llgethands-onwithapproachessuchasgridsearchandrandomsearch.Next,you’lllearndimensionalityreductiontechniquestoeasilyhandlemanyvariablesatonce,beforeexploringhowtousemodelensemblingtechniquesandcreatenewfeaturestoenhancemodelperformance.Inabidtohelpyouautomaticallycreatenewfeaturesthatimproveyourmodel,thebookdemonstrateshowtousetheautomatedfeatureengineeringtool.You’llalsounderstandhowtousetheorchestrationandschedulingworkflowtodeploymachinelearningmodelsinbatch.Bytheendofthisbook,you’llhavetheskillstostartworkingondatascienceprojectsconfidently.Bytheendofthisbook,you’llhavetheskillstostartworkingondatascienceprojectsconfidently.
目錄(121章)
倒序
- 封面
- 版權信息
- Preface
- About the Book
- 1. Introduction to Data Science in Python
- Introduction
- Application of Data Science
- Overview of Python
- Python for Data Science
- Scikit-Learn
- Summary
- 2. Regression
- Introduction
- Simple Linear Regression
- Multiple Linear Regression
- Conducting Regression Analysis Using Python
- Multiple Regression Analysis
- Assumptions of Regression Analysis
- Explaining the Results of Regression Analysis
- Summary
- 3. Binary Classification
- Introduction
- Understanding the Business Context
- Feature Engineering
- Data-Driven Feature Engineering
- Correlation Matrix and Visualization
- Summary
- 4. Multiclass Classification with RandomForest
- Introduction
- Training a Random Forest Classifier
- Evaluating the Model's Performance
- Maximum Depth
- Minimum Sample in Leaf
- Maximum Features
- Summary
- 5. Performing Your First Cluster Analysis
- Introduction
- Clustering with k-means
- Interpreting k-means Results
- Choosing the Number of Clusters
- Initializing Clusters
- Calculating the Distance to the Centroid
- Standardizing Data
- Summary
- 6. How to Assess Performance
- Introduction
- Splitting Data
- Assessing Model Performance for Regression Models
- Assessing Model Performance for Classification Models
- The Confusion Matrix
- Receiver Operating Characteristic Curve
- Area Under the ROC Curve
- Saving and Loading Models
- Summary
- 7. The Generalization of Machine Learning Models
- Introduction
- Overfitting
- Underfitting
- Data
- Random State
- Cross-Validation
- cross_val_score
- LogisticRegressionCV
- Hyperparameter Tuning with GridSearchCV
- Hyperparameter Tuning with RandomizedSearchCV
- Model Regularization with Lasso Regression
- Ridge Regression
- Summary
- 8. Hyperparameter Tuning
- Introduction
- What Are Hyperparameters?
- Finding the Best Hyperparameterization
- Tuning Using Grid Search
- GridSearchCV
- Random Search
- Summary
- 9. Interpreting a Machine Learning Model
- Introduction
- Linear Model Coefficients
- RandomForest Variable Importance
- Variable Importance via Permutation
- Partial Dependence Plots
- Local Interpretation with LIME
- Summary
- 10. Analyzing a Dataset
- Introduction
- Exploring Your Data
- Analyzing Your Dataset
- Analyzing the Content of a Categorical Variable
- Summarizing Numerical Variables
- Visualizing Your Data
- Boxplots
- Summary
- 11. Data Preparation
- Introduction
- Handling Row Duplication
- Converting Data Types
- Handling Incorrect Values
- Handling Missing Values
- Summary
- 12. Feature Engineering
- Introduction
- 13. Imbalanced Datasets
- Introduction
- Understanding the Business Context
- Challenges of Imbalanced Datasets
- Strategies for Dealing with Imbalanced Datasets
- Generating Synthetic Samples
- Summary
- 14. Dimensionality Reduction
- Introduction
- Creating a High-Dimensional Dataset
- Strategies for Addressing High-Dimensional Datasets
- Comparing Different Dimensionality Reduction Techniques
- Summary
- 15. Ensemble Learning
- Introduction
- Ensemble Learning
- Simple Methods for Ensemble Learning
- Advanced Techniques for Ensemble Learning
- Summary 更新時間：2021-06-11 18:27:53
推薦閱讀
- VMware View Security Essentials
- C語言程序設計（第3版）
- 軟件界面交互設計基礎
- OpenCV for Secret Agents
- 精通軟件性能測試與LoadRunner實戰（第2版）
- 大學計算機基礎（第2版）（微課版）
- 小學生C++創意編程（視頻教學版）
- HTML5從入門到精通 （第2版）
- SQL 經典實例
- Visualforce Developer’s guide
- QlikView Unlocked
- Arduino電子設計實戰指南：零基礎篇
- Java服務端研發知識圖譜
- Mastering Machine Learning with scikit-learn
- C++面向對象程序設計教程
- Serverless從入門到進階：架構、原理與實踐
- 區塊鏈技術與智能服務應用
- C#多線程編程實戰
- 計算機邏輯設計
- Node.js Web Development（Third Edition）
- Drupal 7 Webform Cookbook
- Perl語言IC設計實踐
- Grunt Cookbook
- 新標準C++程序設計
- 云原生時代的CoreDNS學習指南
- HTML5技術與移動出版
- Salesforce Platform App Builder Certification Handbook
- Building Hybrid Clouds with Azure Stack
- LabVIEW 2014基礎實例教程
- Java從入門到精通（第4版）
