Footnote:
Includes bibliographical references and index
Description:
What is machine learning? -- Automating machine learning -- Specify business problem -- Acquire subject matter expertise -- Define prediction target -- Decide on unit of analysis -- Success, risk, and continuation -- Accessing and storing data -- Data integration -- Data transformations -- Summarization -- Data reduction and splitting -- Startup processes -- Feature understanding and selection -- Build candidate models -- Understanding the process -- Evaluate model performance -- Comparing model pairs -- Interpret model -- Communicate model insights -- Set up prediction system -- Document modeling process for reproducibility -- Create model monitoring and maintenance plan -- Seven types of target leakage in machine learning and an exercise -- Time-aware modeling -- Time-series modeling.
"In this book, we teach the machine learning process using a new development in data science; automated machine learning. AutoML, when implemented properly, makes machine learning accessible to most people because it removes the need for years of experience in the most arcane aspects of data science, such as the math, statistics, and computer science skills required to become a top contender in traditional machine learning. Anyone trained in the use of AutoML can use it to test their ideas and support the quality of those ideas during presentations to management and stakeholder groups. Because the requisite investment is one semester-long undergraduate course rather than a year in a graduate program, these tools will likely become a core component of undergraduate programs, and over time, even the high-school curriculum"--