 课程大纲:
        
    课程大纲:         Machine Learning with Python – 2 Days培训
Introduction to Applied Machine Learning
        Statistical learning vs. Machine learning
        Iteration and evaluation
        Bias-Variance trade-off
        Machine Learning with Python
        Choice of libraries
        Add-on tools
        Regression
        Linear regression
        Generalizations and Nonlinearity
        Exercises
        Classification
        Bayesian refresher
        Naive Bayes
        Logistic regression
        K-Nearest neighbors
        Exercises
        Cross-validation and Resampling
        Cross-validation approaches
        Bootstrap
        Exercises
        Unsupervised Learning
        K-means clustering
        Examples
        Challenges of unsupervised learning and beyond K-means
 
     
     
         
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