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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-projects-with-python/
课程评论：没有评论
课程名称：使用 Python 进行数据科学项目 课程概述：本课程旨在为您提供关于行业标准数据分析和机器学习工具的实际指导，使用 Python 进行操作，并通过真实数据进行练习。您将学习如何使用 pandas 和 Matplotlib 对数据集进行批判性分析，运用汇总统计和图表提取所需的信息。课程将逐步深化您的知识，教您如何准备数据并将其输入机器学习算法，如正则化逻辑回归和随机森林，这些都可以通过 scikit-learn 包进行实现。您将发现如何调优算法，以便在新数据和未见数据上提供最佳预测。通过后续章节的学习，您将理解这些算法的工作原理及其输出，并洞察模型的预测能力，以及为什么会得出这些预测结果。 作者介绍：Stephen Klosterman 是 CVS Health 的机器学习数据科学家，他热衷于帮助将问题框架化为数据科学背景，提供易于被业务利益相关者理解和重视的机器学习解决方案。他拥有哈佛大学生物学博士学位，并曾担任数据科学课程的助教。Barbora Stetinova 在汽车行业工作，拥有 13 年的数据科学和机器学习经验，负责领导小团队、战略项目和控制主题。从 2018 年 9 月起，她成为 IT 部门的一员，参与汽车公司的数据科学实施。此外，自 2017 年 8 月起，她还参与汽车公司的战略小组项目，并与其他行业（如零售、传感器、建筑）的不同项目担任分析外部顾问。她还是 Elderberry 数据的培训师，专注于 MS Excel 和 Knime 分析平台的培训，提供面对面和在线学习形式（可在 Udemy 上获取）。
Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You'll discover how to tune the algorithms to provide the best predictions on new and, unseen data. As you delve into later chapters, you'll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions.About the AuthorStephen Klosterman is a machine learning data scientist at CVS Health. He enjoys helping to frame problems in a data science context and delivering machine learning solutions that business stakeholders understand and value. His education includes a Ph.D. in biology from Harvard University, where he was an assistant teacher of the data science course.Barbora Stetinova works in an Automotive industry earned experience in data science and machine learning, leading small team, leading strategical projects and in controlling topics for 13 years. Since Sept 2018 she is a member of IT department participating on the Data science implementation in an automotive company.In parallel, since Aug 2017, she is also engaged in strategical group projects for the automotive company and with side contract as an analytical external consultant for different industries (retail, sensorics, building) at Leadership Synergy Community. She is also a data science trainer for Elderberry data, specialized in MS Excel and Knime analytics platform in both face-to-face and elearning forms (available on Udemy).
