08-conclusion.ipynb

Kernel: Python [conda root]

Outlook

Approaching a machine learning problem

Humans in the loop

From prototype to production

Testing production systems

Building your own estimator

from sklearn.base import BaseEstimator, TransformerMixin

class MyTransformer(BaseEstimator, TransformerMixin):
    def __init__(self, first_paramter=1, second_parameter=2):
        # all parameters must be specified in the __init__ function
        self.first_paramter = 1
        self.second_parameter = 2
        
    def fit(self, X, y=None):
        # fit should only take X and y as parameters
        # even if your model is unsupervised, you need to accept a y argument!
        
        # Model fitting code goes here
        print("fitting the model right here")
        # fit returns self
        return self
    
    def transform(self, X):
        # transform takes as parameter only X
        
        # apply some transformation to X:
        X_transformed = X + 1
        return X_transformed

Where to go from here

Theory

Other machine learning frameworks and packages

Ranking, recommender systems, time series, and other kinds of learning

Probabilistic modeling, inference and probabilistic programming

Neural Networks

Scaling to larger datasets

Honing your skills

Conclusion