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所在平台: Udemy |
课程主页: https://www.udemy.com/course/uniform-ml-dl/
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**课程名称：** Scalecast: 机器学习与深度学习 **课程概述：** 本课程专注于使用Scalecast库进行统一建模、报告和数据可视化，涵盖scikit-learn、statsmodels和TensorFlow等多种库的模型。Scalecast简化了数据存储和处理流程，将所有相关数据、预测和衍生指标集中管理，并提供高度的自定义选项。 **核心内容：** * **预测的重要性：** 课程强调通过历史观察进行预测所带来的竞争优势，例如优化库存、提高流动性、减少营运资本和提升客户满意度的能力。 * **时间序列预测技术：** * **ARIMA：** 介绍ARIMA作为一种重要的机器学习技术，特别适用于根据时间预测因变量。 * **LSTM：** 重点讲解LSTM（长短期记忆网络）在深度学习中用于处理序列数据的优势，特别是其捕捉长期模式的能力，以及Scalecast库提供的TensorFlow LSTM模型，用于简化时间序列预测任务。 * **Scalecast库特性：** 介绍Scalecast库的其他功能，包括滞后、趋势和季节性选择，利用网格搜索和时间序列进行超参数调优，以及数据转换。 **教学方法：** Scalecast库旨在简化时间序列预测的实现过程，底层支持TensorFlow。课程将引导学员如何利用该库的各项功能，例如： * 滞后、趋势和季节性特征的选择。 * 通过网格搜索和时间序列方法进行超参数调优。 * 数据转换的应用。 * 应用scikit-learn模型、ARIMA和TensorFlow LSTM进行预测。 * 多元数据的处理（Assignment）。
Uniform modeling (i.e. models from a diverse set of libraries, including scikit-learn, statsmodels, and tensorflow), reporting, and data visualizations are offered through the Scalecast interfaces. Data storage and processing then becomes easy as all applicable data, predictions, and many derived metrics are contained in a few objects with much customization available through different modules.The ability to make predictions based upon historical observations creates a competitive advantage. For example, if an organization has the capacity to better forecast the sales quantities of a product, it will be in a more favorable position to optimize inventory levels. This can result in an increased liquidity of the organizations cash reserves, decrease of working capital and improved customer satisfaction by decreasing the backlog of orders. In the domain of machine learning, there's a specific collection of methods and techniques particularly well suited for predicting the value of a dependent variable according to time, ARIMA is one of the important technique.LSTM is the Recurrent Neural Network (RNN) used in deep learning for its optimized architecture to easily capture the pattern in sequential data. The benefit of this type of network is that it can learn and remember over long sequences and does not rely on pre-specified window lagged observation as input. The scalecast library hosts a TensorFlow LSTM that can easily be employed for time series forecasting tasks. The package was designed to take a lot of the headache out of implementing time series forecasts. It employs TensorFlow under-the-hood. Some of the features are:Lag, trend, and seasonality selectionHyperparameter tuning using grid search and time seriesTransformationsScikit models ARIMALSTMMultivariate- Assignment
