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所在平台: Udemy |
课程主页: https://www.udemy.com/course/algorithmic-introduction-to-machine-learning/
课程评论：没有评论
Coursera 机器学习入门课程内容总结 本课程旨在为希望深入了解流行机器学习算法工作原理的学习者提供“幕后”视角。 **课程涵盖内容：** * **数据预处理：** * 处理缺失值 * 数据编码（类别数据转换为标称数据） * 数据归一化 * **监督学习：** * 线性回归 * 决策树回归 * 决策树分类 * 朴素贝叶斯分类 * K 近邻分类 * **模型评估：** * 分类器评估 * 混淆矩阵分析 * **无监督学习：** * K-均值聚类 * 层次聚类 * **模型改进：** * 交叉验证 **课程目标：** 通过本课程的学习，您将对机器学习算法的工作方式有透彻的理解，从而能够构建更优秀的机器学习模型。
Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to "self-learn" from training data and improve over time, without being explicitly programmed. Machine learning algorithms are able to detect patterns in data and learn from them, in order to make their own predictions. This course is intended for people who wish to understand the functioning of popular machine learning algorithms. This gives a behind the scene look of who things are working. We will start by looking at some data pre-processing techniques, then we will move on to look at supervised and unsupervised learning algorithms. Finally, we will look at what cross valuation is and how it is done.In this course we will look at: Data Preprocessing [Handling Missing Values, Data Encoding (Conversion of Categorical Data into Nominal Data), Data Normalization] Supervised Learning[Linear Regression, Decision Tree Regression, Decision Tree Classification, Naive Bayes Classification, K Nearest Neignbour Classification] Model Evaluation [Evaluation of Classifiers, Deciding Confusion Matrix] Unsupervised Learning [K Means Clustering, Hierarchical Clustering] Model Improvement [Cross Validation]By the end of this course, you will have a thorough understanding of how these machine learning algorithms function which will in turn enable you to develop better ML models.
