Chapter 5. Applied Introduction to Machine Learning

Even though the forefront of artificial intelligence research captures headlines and our imaginations, do not let the esoteric reputation of machine learning distract from the full range of techniques with practical business applications. In fact, the power of machine learning has never been more accessible. Whereas some especially oblique problems require complex solutions, often, simpler methods can solve immediate business needs, and simultaneously offer additional advantages like faster training and scoring. Choosing the proper machine learning technique requires evaluating a series of tradeoffs like training and scoring latency, bias and variance, and in some cases accuracy versus complexity.

This chapter provides a broad introduction to applied machine learning with emphasis on resolving these tradeoffs with business objectives in mind. We present a conceptual overview of the theory underpinning machine learning. Later chapters will expand the discussion to include system design considerations and practical advice for implementing predictive analytics applications. Given the experimental nature of applied data science, the theme of flexibility will show up many times. In addition to the theoretical, computational, and mathematical features of machine learning techniques, the reality of running a business with limited resources, especially limited time, affects how you should choose and deploy strategies.

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