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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/data-machine-learning
课程评论：没有评论
课程名称：机器学习的数据 课程概述：本课程深入探讨数据在应用机器学习模型成功中的关键作用。完成此课程后，学习者将掌握以下技能： - 理解数据在学习、训练和操作阶段的关键要素 - 理解偏差和数据来源 - 实施改进模型普适性的技术 - 解释过拟合的后果并识别缓解措施 - 实施适当的测试和验证措施 - 展示通过合理的特征工程提升模型准确性的方法 - 探索算法参数对模型强度的影响 成功完成本课程的前提是具备基础的Python编程知识（例如，能够阅读和理解现有代码，熟悉条件语句、循环、变量、列表、字典和数组等）以及基础的线性代数（向量表示）和统计学（概率分布和均值/中位数/众数）知识。 该课程是Coursera和阿尔伯塔机器智能研究所联合推出的应用机器学习专业的第三个课程。 课程大纲： 1. 什么是好的数据？ - 描述如何从杂乱无章、未处理的数据过渡到干净的学习数据，以及成功数据准备所需的流程。 2. 为机器学习成功准备数据 - 聚焦于如何将已识别的数据源整合起来，以全面准备数据。 3. 为更多乐趣与利润的特征工程 - 讨论如何将通用数据转化为特定机器学习项目的成功燃料。 4. 坏数据 - 探索在数据识别和处理过程中可能出现的陷阱。
Name:What Does Good Data look like?
Description:We all know that data is important for machine learning success, but what does it really look like? What steps do you need to take to get from scattered, unprocessed data to nice clean learning data? This week takes an overarching view to describe how your problem and data needs interact, and what processes need to be in place for successful data preparation.
Name:Preparing your Data for Machine Learning Success
Description:Now that you have your data sources identified, you need to bring it all together. This week describes what you need to prepare data overall.
Name:Feature Engineering for MORE Fun & Profit
Description:Data is particular to a problem. This week we'll discuss how to turn generic data into successful fuel for specific machine learning projects.
Name:Bad Data
Description:There are so many ways data can go wrong! This week discussed some of the pitfalls in data identification and processing.
This course is all about data and how it is critical to the success of your applied machine learning model. Completing this course will give learners the skills to: Understand the critical elements of data in the learning, training and operation phases Understand biases and sources of data Implement techniques to improve the generality of your model Explain the consequences of overfitting and identify mitigation measures Implement appropriate test and validation measures. Demonstrate how the accuracy of your model can be improved with thoughtful feature engineering. Explore the impact of the algorithm parameters on model strength To be successful in this course, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the third course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.
