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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/ibm-exploratory-data-analysis-for-machine-learning
课程评论：没有评论
课程名称：机器学习的探索性数据分析 课程概述：此课程是IBM机器学习专业证书的第一门课程，旨在介绍机器学习的基本概念及专业证书的内容。您将意识到优质数据的重要性，学习常见的数据检索、清洗、特征工程技术，并为初步分析和假设检验做好准备。 课程结束时，您将能够： - 从多个数据源（SQL、NoSQL数据库、API、云端）检索数据 - 描述和使用常见的特征选择和特征工程技术 - 处理分类特征、序数特征以及缺失值 - 使用多种技术检测和处理异常值 - 阐明特征缩放的重要性并使用各种缩放技术 适合人群：此课程面向有志于在商业环境中获得机器学习和人工智能实践经验的未来数据科学家。 先修技能：为充分利用本课程，您应具备Python开发环境的编程基础，以及微积分、线性代数、概率论和统计学的基本理解。 课程大纲： 1. 现代人工智能及其应用的简史：介绍人工智能和机器学习的概况，回顾现代AI的历史及其在商业和个人项目中的应用。 2. 数据检索与清洗：学习如何从不同来源获取数据并进行清洗，以确保数据质量。 3. 探索性数据分析与特征工程：通过可视化分析确认数据适合机器学习建模，并进行特征工程和转化。 4. 推断统计与假设检验：学习推断统计和假设检验的基本概念及示例，以获取数据质量的快速洞见，确认商业直觉，并指导后续的机器学习分析。 5. 最终项目：应用所学技能分析自己选择的数据集（课程材料或外部来源），进行数据清洗、特征工程、可视化及假设检验，以得出有意义的见解，最终通过AI评分工具自动评估工作成果。
Name:A Brief History of Modern AI and its Applications
Description:Artificial Intelligence is not new, but it is new in a sense that it is easier than ever to get started using Machine Learning in business settings. In this module, we will go over a quick introduction to AI and Machine Learning and we will visit a brief history of the modern AI. We will also explore some of the current applications of AI and Machine Learning for you, to think about how you want to leverage them in your day to day business practice or personal projects.
Name:Retrieving and Cleaning Data
Description:Good data is the fuel that powers Machine Learning and Artificial Intelligence. In this module, you will learn how to retrieve data from different sources, how to clean it to ensure its quality.
Name:Exploratory Data Analysis and Feature Engineering
Description:In this module you will learn how to conduct exploratory analysis to visually confirm it is ready for machine learning modeling by feature engineering and transformations.
Name:Inferential Statistics and Hypothesis Testing
Description:Inferential statistics and hypothesis testing are two types of data analysis often overlooked at early stages of analyzing your data. They can give you quick insights about the quality of your data. They also help you confirm business intuition and help you prescribe what to analyze next using Machine Learning. This module looks at useful definitions and simple examples that will help you get started creating hypothesis around your business problem and how to test them.
Name:Final Project
Description:In this assignment, you will apply the skills learned throughout the course to analyze a dataset of your choice, either from the course materials or an external source. You will perform data cleaning, feature engineering, exploratory data visualization, and hypothesis testing to derive meaningful insights. Upon completion, your work will be evaluated automatically by an AI grading tool.
This first course in the IBM Machine Learning Professional Certificate introduces you to Machine Learning and the content of the professional certificate. In this course you will realize the importance of good, quality data. You will learn common techniques to retrieve your data, clean it, apply feature engineering, and have it ready for preliminary analysis and hypothesis testing. By the end of this course you should be able to: Retrieve data from multiple data sources: SQL, NoSQL databases, APIs, Cloud Describe and use common feature selection and feature engineering techniques Handle categorical and ordinal features, as well as missing values Use a variety of techniques for detecting and dealing with outliers Articulate why feature scaling is important and use a variety of scaling techniques Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Machine Learning and Artificial Intelligence in a business setting. What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Calculus, Linear Algebra, Probability, and Statistics.
