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Hands-On Machine Learning with Scikit-Learn and Scientific Python Toolkits Quotes
Hands-On Machine Learning with Scikit-Learn and Scientific Python Toolkits: A practical guide to implementing supervised and unsupervised machine learning algorithms in Python
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Hands-On Machine Learning with Scikit-Learn and Scientific Python Toolkits Quotes
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“I deliberately chose not to follow the statistical convention here so that our natural language processing friends feel at home once they realize that this (Pearson correlation coefficient equation) is the exact same equation as for cosine similarity.”
― Hands-On Machine Learning with Scikit-Learn and Scientific Python Toolkits: A practical guide to implementing supervised and unsupervised machine learning algorithms in Python
― Hands-On Machine Learning with Scikit-Learn and Scientific Python Toolkits: A practical guide to implementing supervised and unsupervised machine learning algorithms in Python
“Another name for statistical mean is expected value. That's because the mean serves as a biased estimation of the data.”
― Hands-On Machine Learning with Scikit-Learn and Scientific Python Toolkits: A practical guide to implementing supervised and unsupervised machine learning algorithms in Python
― Hands-On Machine Learning with Scikit-Learn and Scientific Python Toolkits: A practical guide to implementing supervised and unsupervised machine learning algorithms in Python
“You can tell whether a man is clever by his answers. You can tell whether a man is wise by his questions. Naguib Mahfouz”
― Hands-On Machine Learning with Scikit-Learn and Scientific Python Toolkits: A practical guide to implementing supervised and unsupervised machine learning algorithms in Python
― Hands-On Machine Learning with Scikit-Learn and Scientific Python Toolkits: A practical guide to implementing supervised and unsupervised machine learning algorithms in Python
“Generating different train and test splits is called cross-validation. This helps us have a more reliable estimation of our model's accuracy”
― Hands-On Machine Learning with Scikit-Learn and Scientific Python Toolkits: A practical guide to implementing supervised and unsupervised machine learning algorithms in Python
― Hands-On Machine Learning with Scikit-Learn and Scientific Python Toolkits: A practical guide to implementing supervised and unsupervised machine learning algorithms in Python
“The first thing to do when developing a model is to understand the problem you are trying to solve thoroughly. This does not only involve understanding what problem you are solving, but also why you are solving it, what impact are you expecting to have, and what the currently available solution is that you are comparing your new solution to.”
― Hands-On Machine Learning with Scikit-Learn and Scientific Python Toolkits: A practical guide to implementing supervised and unsupervised machine learning algorithms in Python
― Hands-On Machine Learning with Scikit-Learn and Scientific Python Toolkits: A practical guide to implementing supervised and unsupervised machine learning algorithms in Python
“Exploring the unknown requires tolerating uncertainty." – Brian Greene”
― Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits: A practical guide to implementing supervised and unsupervised machine learning algorithms in Python
― Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits: A practical guide to implementing supervised and unsupervised machine learning algorithms in Python
