Advanced Machine Learning with scikit-learn

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对sklearn 机器学习感兴趣的可以联系我 一起学习讨论919583975@qq.comAdvanced Machine Learning with scikit-learn Training VideoA Practical Training Course That Teaches Real World Skills

In this project-based Advanced Machine Learning with scikit-learn video tutorial series, you'll quickly have relevant skills for real-world applications.

Follow along with our expert instructor in this training course to get:

• Concise, informative and broadcast-quality Advanced Machine Learning with scikit-learn training videosdelivered to your desktop
• The ability to learn at your own pace with our intuitive, easy-to-use interface
• A quick grasp of even the most complex Advanced Machine Learning with scikit-learn subjectsbecause they're broken into simple, easy to follow tutorial videos
  

Practical working files further enhance the learning process and provide a degree of retention that is unmatched by any other form of Advanced Machine Learning with scikit-learn tutorial, online or offline... so you'll know the exact steps for your own projects.

• [url=]01. Introduction[/url]
  • [url=]What To Expect And About The Author[/url]
  • [url=]Setup[/url]
  • [url=]The Classifier Interface[/url]
  • [url=]The Regressor Interface[/url]
  • [url=]The Transformer Interface[/url]
  • [url=]The Cluster Interface[/url]
  • [url=]The Manifold Interface[/url]
  • [url=]scikit-Learn Interface Summary[/url]
  • [url=]Cross-Validation With Cross_Val_Score[/url]
  • [url=]Parameter Searches With GridSearchCV[/url]
  • 0111 How To Access Your Working Files
    
• [url=]02. Model Complexity, Overfitting And Underfitting[/url]
  • 0201 What Is Model Complexity And Overfitting?
  • 0202 Linear Models In-Depth
  • 0203 Kernel SVMs In-Depth
  • 0204 Random Forests In-Depth
  • 0205 Learning Curves For Analyzing Model Complexity
  • 0206 Validation Curves For Analyzing Model Parameters
  • 0207 Efficient Parameter Search With EstimatorCV Objects
    
• [url=]03. Pipelines[/url]
  • 0301 Motivation Of Using Pipelines
  • 0302 Defining A Pipeline And Basic Usage
  • 0303 Cross-Validation With Pipelines
  • 0304 Parameter Selection With Pipelines
    
• [url=]04. Advanced Metrics And Imbalanced Classes[/url]
  • 0401 Be Mindful Of Default Metrics
  • 0402 More Evaluation Methods For Classification
  • 0403 AUC
  • 0404 Defining Custom Metrics
    
• [url=]05. Model Selection For Unsupervised Learning[/url]
  • 0501 Guidelines For Unsupervised Model Selection
  • 0502 Model Selection For Density Models
  • 0503 Model Selection For Clustering
    
• [url=]06. Dealing With Categorical Variables, Dictionaries, And Incomplete Data[/url]
  • 0601 Why Real Data Is Messy
  • 0602 One-Hot Encoding For Categorical Data
  • 0603 Working With Dictionaries
  • 0604 Handling Incomplete Data
    
• [url=]07. Handling Text Data[/url]
  • 0701 Motivation
  • 0702 Bag-Of-Words Representations
  • 0703 Text Classification For Sentiment Analysis - Part 1
  • 0704 Text Classification For Sentiment Analysis - Part 2
  • 0705 The Hashing Trick
  • 0706 Other Representations - Distributed Word Representations
    
• [url=]08. Out Of Core Learning[/url]
  • 0801 The Trade-Offs Of Out Of Core Learning
  • 0802 The scikit-Learn Interface For Out Of Core Learning
  • 0803 Kernel Approximations For Large-Scale Non-Linear Classification
  • 0804 Subsample And Transform - Supervised Transformations For Out Of Core Learning
  • 0805 Application - Out-Of-Core Text Classification
    
• [url=]09. Conclusion[/url]
  • 0901 Summary
  • 0902 Where To Go From Here
  • 
    
    

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关键词：scikit-learn Learning Advanced machine ADVANCE relevant training course video