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In a typical machine learning application, practitioners have a dataset consisting of input data points to train on. The raw data itself may not be in a form such that all algorithms may be applicable to it out of the box. An expert may have to apply appropriate data pre-processing, feature engineering, feature extraction, and feature selection methods that make the dataset amenable for machine learning. Following those preprocessing steps, practitioners must then perform algorithm selection and hyperparameter optimization to maximize the predictive performance of their machine learning model. Clearly all of those steps induce their own challenges, accumulating to a significant hurdle to get started with machine learning.

In a typical machine learning application, practitioners have a dataset consisting of input data points to train on. The raw data itself may not be in a form such that all algorithms may be applicable to it out of the box. An expert may have to apply appropriate data pre-processing, feature engineering, feature extraction, and feature selection methods that make the dataset amenable for machine learning. Following those preprocessing steps, practitioners must then perform algorithm selection and hyperparameter optimization to maximize the predictive performance of their machine learning model. Clearly all of those steps induce their own challenges, accumulating to a significant hurdle to get started with machine learning.

在一个典型的机器学习应用程序中，程序的使用者会使用一个由输入数据点组成的数据集去进行训练。原始数据本身的形式可能并不适用于所有算法。专家可能需要使用相应的'''<font color="#ff8000">数据预处理 data pre-processing </font>'''、'''<font color="#ff8000">特征工程 feature engineering</font>'''、'''<font color="#ff8000">特征提取 feature extraction</font>'''和'''<font color="#ff8000">特征选择方法 feature selectin methods</font>'''等方法，使数据集适合机器学习。按照这些预处理步骤，程序的使用者必须执行'''<font color="#ff8000">算法选择 algorithm </font>'''和'''<font color="#ff8000">超参数优化 hyperparameter optimization</font>'''，以最大限度地提升他们的机器学习模型的预测性能。显然，这些步骤都为它们自身带来了挑战。这些挑战一旦累积到一定程度，就会成为机器学习的重大障碍。

在一个典型的机器学习应用程序中，程序的使用者会使用一个由输入数据点组成的数据集去进行训练。原始数据本身的形式可能并不适用于所有算法。专家可能需要使用相应的'''<font color="#ff8000">数据预处理 data pre-processing </font>'''、'''<font color="#ff8000">特征工程 feature engineering</font>'''、'''<font color="#ff8000">特征提取 feature extraction</font>'''和'''<font color="#ff8000">特征选择方法 feature selectin methods</font>'''等，使数据集适合机器学习。按照这些预处理步骤，程序的使用者必须执行'''<font color="#ff8000">算法选择 algorithm </font>'''和'''<font color="#ff8000">超参数优化 hyperparameter optimization</font>'''，以最大限度地提升他们的机器学习模型的预测性能。显然，这些步骤都为它们自身带来了挑战。这些挑战一旦累积到一定程度，就会成为机器学习的重大障碍。