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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/machine-learning-classification-algorithms
课程评论：没有评论
课程名称：机器学习算法：监督学习全流程 概述：本课程帮助学员理解机器学习项目的基本原理。学员将学习并实施监督学习技术，通过真实案例分析业务场景，优化决策树、k-近邻和支持向量机的使用。同时，学员将掌握不同数据准备步骤的实践影响，并描述应用机器学习中的常见生产问题。为了顺利完成课程，学员需具备一定基础的Python编程能力（能阅读和调试现有代码，熟悉条件语句、循环、变量、列表、字典和数组），并具有基础的线性代数（向量表示）和统计学（概率分布、均值/中位数/众数）知识。 本课程是Coursera平台提供的应用机器学习专业化的第二门课程，由阿尔伯塔机器智能研究所主办。 课程大纲： 第一部分：使用决策树和k-NN进行分类 在这一周中，学员将学习监督学习的基础知识，特别是分类。此外，将介绍两个分类算法：决策树和k-NN。学员将通过Jupyter笔记本开始编程，熟悉在使用机器学习进行分类时会遇到的问题。 第二部分：为乐趣和利润而设计的函数 在课程的第二周，学员将学习回归算法，这是监督学习的另一面。将介绍找到最优解的思路、优化标准及相关问题。通过回归了解模型复杂性与准确性之间的相互作用，并初步体验回归与分类的关系。 第三部分：为分类服务的回归：支持向量机 这一周将深入探讨如何将回归用于分类。学员将了解支持向量机算法的基本要素，认识到许多看似不相关的机器学习算法之间的联系。同时，将介绍逻辑回归、神经网络和支持向量机，并展示其中两个的实现。 第四部分：对比模型 在课程的尾声，学员将学习如何评估模型的实际表现以及如何进一步优化。将回顾特别针对回归和分类的评估问题，并介绍一些分析模型表现的其他工具。本周涉及的主题旨在增强学员对模型的信心，使其能够利用机器学习实现业务目标的潜力。
Part: 1
Title:Classification using Decision Trees and k-NN
Description:Welcome to Supervised Learning, Tip to Tail! This week we'll go over the basics of supervised learning, particularly classification, as well as teach you about two classification algorithms: decision trees and k-NN. You'll get started programming on the platform through Jupyter notebooks and start to familiarize yourself with all the issues that arise when using machine learning for classification.
Part: 2
Title:Functions for Fun and Profit
Description:Welcome to the second week of the course! In this week you'll learn all about regression algorithms, the other side of supervised learning. We'll introduce you to the idea of finding lines, optimization criteria, and all the associated issues. Through regression we'll see the interactions between model complexity and accuracy, and you'll get a first taste of how regression and classification might relate.
Part: 3
Title:Regression for Classification: Support Vector Machines
Description:This week we'll be diving straight in to using regression for classification. We'll describe all the fundamental pieces that make up the support vector machine algorithms, so that you can understand how many seemingly unrelated machine learning algorithms tie together. We'll introduce you to logistic regression, neural networks, and support vector machines, and show you how to implement two of those.
Part: 4
Title:Contrasting Models
Description:Now at the tail end of the course, we're going to go over how to know how well your model is actually performing and what you can do to get even better performance from it. We'll review assessment questions particular to regression and classification, and introduce some other tools that really help you analyze your model performance. The topics covered this week aim to give you confidence in your models, so you're ready to unlock the power of machine learning for your business goals.
This course takes you from understanding the fundamentals of a machine learning project. Learners will understand and implement supervised learning techniques on real case studies to analyze business case scenarios where decision trees, k-nearest neighbours and support vector machines are optimally used. Learners will also gain skills to contrast the practical consequences of different data preparation steps and describe common production issues in applied ML. To be successful, 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 second course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.
