舉報 
會員
The Machine Learning Workshop
Machinelearningalgorithmsareanintegralpartofalmostallmodernapplications.Tomakethelearningprocessfasterandmoreaccurate,youneedatoolflexibleandpowerfulenoughtohelpyoubuildmachinelearningalgorithmsquicklyandeasily.WithTheMachineLearningWorkshop,you'llmasterthescikit-learnlibraryandbecomeproficientindevelopingclevermachinelearningalgorithms.TheMachineLearningWorkshopbeginsbydemonstratinghowunsupervisedandsupervisedlearningalgorithmsworkbyanalyzingareal-worlddatasetofwholesalecustomers.Onceyou'vegottogripswiththebasics,you’lldevelopanartificialneuralnetworkusingscikit-learnandthenimproveitsperformancebyfine-tuninghyperparameters.Towardstheendoftheworkshop,you'llstudythedatasetofabank'smarketingactivitiesandbuildmachinelearningmodelsthatcanlistclientswhoarelikelytosubscribetoatermdeposit.You'llalsolearnhowtocomparethesemodelsandselecttheoptimalone.BytheendofTheMachineLearningWorkshop,you'llnotonlyhavelearnedthedifferencebetweensupervisedandunsupervisedmodelsandtheirapplicationsintherealworld,butyou'llalsohavedevelopedtheskillsrequiredtogetstartedwithprogrammingyourveryownmachinelearningalgorithms.
目錄(55章)
倒序
- 封面
- 版權信息
- Preface
- 1. Introduction to Scikit-Learn
- Introduction
- Introduction to Machine Learning
- Scikit-Learn
- Data Representation
- Data Preprocessing
- Scikit-Learn API
- Supervised and Unsupervised Learning
- Summary
- 2. Unsupervised Learning – Real-Life Applications
- Introduction
- Clustering
- Exploring a Dataset – Wholesale Customers Dataset
- Data Visualization
- Mean-Shift Algorithm
- DBSCAN Algorithm
- Evaluating the Performance of Clusters
- Summary
- 3. Supervised Learning – Key Steps
- Introduction
- Supervised Learning Tasks
- Model Validation and Testing
- Evaluation Metrics
- Error Analysis
- Summary
- 4. Supervised Learning Algorithms: Predicting Annual Income
- Introduction
- Exploring the Dataset
- The Na?ve Bayes Algorithm
- The Decision Tree Algorithm
- The Support Vector Machine Algorithm
- Error Analysis
- Summary
- 5. Supervised Learning – Key Steps
- Introduction
- Artificial Neural Networks
- Applying an Artificial Neural Network
- Performance Analysis
- Summary
- 6. Building Your Own Program
- Introduction
- Program Definition
- Saving and Loading a Trained Model
- Interacting with a Trained Model
- Summary
- Appendix
- 1. Introduction to Scikit-Learn
- 2. Unsupervised Learning – Real-Life Applications
- 3. Supervised Learning – Key Steps
- 4. Supervised Learning Algorithms: Predicting Annual Income
- 5. Artificial Neural Networks: Predicting Annual Income
- 6. Building Your Own Program 更新時間：2021-06-18 18:24:05
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