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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-simply-explained-by-a-data-scientist/
课程评论：没有评论
课程名称：机器学习（数据科学家的简明解释） 课程概述： 本课程是一个入门工作坊，全面解释机器学习的概念。我们将深入探讨监督学习、无监督学习和强化学习，详细讲解与各类机器学习相关的算法和应用案例。本工作坊首次以非数据科学家和工程师也能理解的方式来解读机器学习。我曾在Chegg、Thinkful、General Assembly、Springboard、Tech Talent South和世界数据科学研究院教授数据科学和机器学习专业课程。 机器学习的定义： 机器学习赋予计算机在没有程序编写的情况下做出决策的能力。监督学习的主要目标是根据过去的结构化和/或标记数据准确预测未来；无监督学习的主要目标是根据相似性对非结构化数据进行分类；强化学习的主要目标是利用机器人模拟人类行为。 机器学习的应用案例包括： - 客户和客户满意度追踪，金融服务公司通过机器学习监测客户幸福感。 - 基于用户活动识别即将流失的客户。 - 市场趋势响应与市场分析。 - 银行业中用于批准或拒绝信用申请或银行账户。 - 基于购买行为识别欺诈。 - 社交媒体推荐关注的人或被关注的人。 - 零售业帮助确定未来或现有产品的购买建议。 - Netflix等平台基于用户偏好推荐节目。 - 自动调整与价格匹配保证竞争的公司定价。 课程内容包括： - 机器学习的基本概念。 - 监督学习算法。 - 无监督学习算法。 - 机器学习的应用案例。 - 分类应用案例。 - 无监督学习的实际意义。 - 无监督学习算法。 - 聚类算法。 - 关联算法。 - 降维算法。 - 硬聚类与软聚类。 - 层次聚类与划分聚类。 - 降维及其应用案例。 - 特征提取。 本课程旨在让学习者能够理解机器学习的核心概念及其实际应用，适合所有对机器学习感兴趣的人。
This is an introductory workshop that will thoroughly explain Machine Learning. We dive into Supervised Learning, Unsupervised Learning, and Reinforcement Learning. I breakdown all of the algorithms and use cases associated with the different forms of Machine Learning.This is the first Machine Learning workshop that ACTUALLY breaks down Machine Learning in a way that can be understood from people that are not Data Scientists and Engineers!I have professionally taught Data Science and Machine Learning at Chegg, Thinkful, General Assembly, Springboard, Tech Talent South, & the World Data Science Institute.What is Machine Learning:Machine Learning gives Computer the ability to make decisions without being programmed.The primary goal of supervised learning is to accurately predict the future based on past structured and/or labeled data!The primary goal of unsupervised learning is to group unstructured data based on similarities!The primary goal of reinforment learning is to simulate human behavior using robots!Machine Learning use cases:Customer and client satisfaction. Machine learning helps financial services firms track customer happinessUsed in several platforms to identify a customer is about to leave based on user activityReacting to market trendsMarket analysisUsed in banking to approve or disapprove credit applications/bank accountsIdentify fraud based on purchasing behaviorsSocial media to recommend who you should follow or who should follow youUsed in retail to help us determine what product we should buy in future or with current productAlso used for platforms like Netflix to identify suggested shows for us to watchAutomatically adjusts prices companies that compete with Price Match GuaranteeSiri, Alexa, and Google MapUber uses for hot zones and price settingImage and Speech RecognitionPredicting lifespan based on specific data points previously collectedStock picks, sports picks, and the list goes on…..What you'll learn:*What Machine Learning actually is?*Supervised Learning Algorithms*Unsupervised Learning Algorithms*Machine Learning Use Cases*Classification Use Cases*What Unsupervised Learning actually is?*Unsupervised Learning Algorithms*Clustering Algorithms*Association Algorithms*Dimensionality Reduction Algorithms*Hard Clustering*Soft Clustering*Hierarchical Clustering*Partitioning Clustering*Dimension Reduction*Dimension Reduction Use Cases*Feature Extraction
