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所在平台: Udemy |
课程主页: https://www.udemy.com/course/exploratory-data-analysis-for-ml/
课程评论：没有评论
**课程名称：** 使用 Python 进行数据分析与探索性数据分析 (EDA) **课程概述：** 本课程是一门面向初学者的机器学习入门课程，专注于数据分析和探索性数据分析 (EDA)。课程由一位在人工智能和科技领域拥有丰富经验的专家设计，旨在帮助学员深入理解数据，为构建机器学习模型打下坚实基础。 **课程亮点：** * **全面的数据分析知识：** 涵盖描述性统计、数据分布形状、大数定律、时间序列预测、回归与分类等基础及高级数据分析概念。 * **深入的 EDA 技术：** 详细讲解如何处理异常值、转换数据、管理不平衡数据集，并介绍如 Klib 和 Sweetviz 等强大的 EDA 库，仅需少量代码即可完成复杂的 EDA 任务。 * **实际应用导向：** 通过实例分析、练习和案例研究，帮助学员理解变量之间的关系，识别关键影响因素。 * **Python 编程实践：** 所有分析技术均使用 Python 进行讲解和实践，并为 Python 新手提供补充材料。 * **Web 应用构建：** 教授如何使用 Streamlit 构建用于 EDA 的 Web 应用程序。 * **最新内容更新：** 课程内容包含最新的非参数假设检验，以及条件散点图等先进技术。 **学员评价：** 学员普遍认为该课程内容充实、讲解清晰，对于理解数据、避免构建糟糕模型至关重要，是机器学习爱好者的理想入门选择。 **课程目标：** 通过本课程的学习，学员将能够： * 有效地理解和分析数据。 * 熟练运用 Python 进行数据分析和 EDA。 * 识别数据中的关键因素和模式。 * 为后续的机器学习建模做好充分准备。
Recent updatesMarch 2024: Expanded coverage of non parametric hypothesis testsJan 2023: EDA libraries (Klib, Sweetviz) that complete all the EDA activities with a few lines of code have been addedJan 2022: Conditional Scatter plots have been addedNov 2021: An exhaustive exercise covering all the possibilities of EDA has been added.Testimonials about the course"I found this course interesting and useful. Mr. Govind has tried to cover all important concepts in an effective manner. This course can be considered as an entry-level course for all machine learning enthusiasts. Thank you for sharing your knowledge with us." Dr. Raj Gaurav M."He is very clear. It's a perfect course for people doing ML based on data analysis." Dasika Sri Bhuvana V."This course gives you a good advice about how to understand your data, before start using it. Avoids that you create a bad model, just because the data wasn't cleaned." Ricardo VWelcome to the program on data analysis and exploratory data analysis!This program covers both basic as well as advanced data analysis concepts, analysis approaches, the associated programming, assignments and case studies:How to understand the relationship between variablesHow to identify the critical factor in dataDescriptive Statistics, Shape of distribution, Law of large numbersTime Series ForecastingRegression and ClassificationFull suite of Exploratory Data Analysis techniques including how to handle outliers, transform data, manage imbalanced datasetEDA libraries like Klib, SweetvizBuild a web application for exploratory data analysis using StreamlitProgramming Language UsedAll the analysis techniques are covered using python programming language. Python's popularity and ease of use makes it the perfect choice for data analysis and machine learning purposes. For the benefit of those who are new to python, we have added material related to python towards the end of the course.Course DeliveryThis course is designed by an AI and tech veteran and comes to you straight from the oven!
