Machine learning systems automatically learn programs from data, offering an attractive alternative to manual programming. Widely used in areas like web search, spam filtering, and drug design, machine learning is expected to drive future innovation. However, much of the "folk knowledge" needed to successfully develop machine learning applications is not readily available in textbooks, leading to inefficiencies and suboptimal results. This article summarizes 12 key lessons for machine learning practitioners. Machine learning involves three components: representation, evaluation, and optimization. Representation defines the set of classifiers a learner can use. Evaluation distinguishes good classifiers from bad ones, while optimization searches for the best classifier. The article discusses classification, a common task in machine learning, and highlights the importance of generalization, which is the goal of machine learning. Generalization is crucial because training data is unlikely to be seen again in testing. Data alone is not enough for learning. Machine learning requires knowledge or assumptions beyond the data to generalize. The "no free lunch" theorems show that no learner can beat random guessing over all possible functions. However, real-world functions often have patterns that allow effective learning. Overfitting, where a model performs well on training data but poorly on new data, is a common issue. It can be mitigated through techniques like cross-validation and regularization. High-dimensional data poses challenges, as the curse of dimensionality makes generalization harder. However, the "blessing of non-uniformity" can help by focusing on lower-dimensional manifolds. Theoretical guarantees in machine learning are important but often loose. They provide probabilistic bounds on generalization but do not always translate to practical success. Feature engineering is crucial, as the choice of features significantly impacts learning outcomes. More data often leads to better results than more complex algorithms. Ensemble methods, which combine multiple learners, often outperform single models. However, they are not a substitute for Bayesian model averaging. Simplicity does not always imply accuracy, and complex models can sometimes be more effective. Correlation does not imply causation, and machine learning models should not be treated as causal unless explicitly designed for that purpose. The article concludes that machine learning requires both theoretical understanding and practical expertise. It emphasizes the importance of combining formal and informal knowledge, and provides resources for further study.Machine learning systems automatically learn programs from data, offering an attractive alternative to manual programming. Widely used in areas like web search, spam filtering, and drug design, machine learning is expected to drive future innovation. However, much of the "folk knowledge" needed to successfully develop machine learning applications is not readily available in textbooks, leading to inefficiencies and suboptimal results. This article summarizes 12 key lessons for machine learning practitioners. Machine learning involves three components: representation, evaluation, and optimization. Representation defines the set of classifiers a learner can use. Evaluation distinguishes good classifiers from bad ones, while optimization searches for the best classifier. The article discusses classification, a common task in machine learning, and highlights the importance of generalization, which is the goal of machine learning. Generalization is crucial because training data is unlikely to be seen again in testing. Data alone is not enough for learning. Machine learning requires knowledge or assumptions beyond the data to generalize. The "no free lunch" theorems show that no learner can beat random guessing over all possible functions. However, real-world functions often have patterns that allow effective learning. Overfitting, where a model performs well on training data but poorly on new data, is a common issue. It can be mitigated through techniques like cross-validation and regularization. High-dimensional data poses challenges, as the curse of dimensionality makes generalization harder. However, the "blessing of non-uniformity" can help by focusing on lower-dimensional manifolds. Theoretical guarantees in machine learning are important but often loose. They provide probabilistic bounds on generalization but do not always translate to practical success. Feature engineering is crucial, as the choice of features significantly impacts learning outcomes. More data often leads to better results than more complex algorithms. Ensemble methods, which combine multiple learners, often outperform single models. However, they are not a substitute for Bayesian model averaging. Simplicity does not always imply accuracy, and complex models can sometimes be more effective. Correlation does not imply causation, and machine learning models should not be treated as causal unless explicitly designed for that purpose. The article concludes that machine learning requires both theoretical understanding and practical expertise. It emphasizes the importance of combining formal and informal knowledge, and provides resources for further study.