Table of Contents:
“...Starting from business requirements -- Defining ML problems -- Is ML the
best solution? -- ML problem categories -- ML model inputs and outputs -- Measuring ML solutions and
data readiness -- ML model performance measurement --
Data readiness -- Collecting
data --
Data engineering --
Data sampling and balancing -- Numerical value transformation -- Categorical value transformation -- Missing value handling -- Outlier
processing -- Feature engineering -- Feature selection -- Feature synthesis -- Summary -- Further reading -- Chapter 4: Developing and Deploying ML Models -- Splitting the dataset...
”
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