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所在平台: Udemy |
课程主页: https://www.udemy.com/course/smtbm-ml-py/
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课程名称：机器学习算法教程 概述：该课程全面涵盖机器学习，内容通过讲解和实践相结合的方式，使学习变得轻松。学员所获得的知识可以广泛应用于各种现实场景。无监督学习无需对模型进行监督，使模型能够自主发现以前未检测到的模式和信息，主要处理无标签数据，促使机器构建其世界的紧凑内在表示并生成创意内容。有监督学习则通过提供输入数据和相应的输出数据来训练机器学习模型，目标是寻找一种映射函数，将输入与输出对应起来，并从标记的训练数据中推导出函数关系。该课程详细探讨无监督学习和有监督学习，并包含众多附加主题。 课程内容包括： - 机器学习简介 - 深度学习简介 - 安装指导 - 无监督学习 - 聚类，关联分析 - 准备工作（主成分分析PCA），DBSCAN，均值漂移，K均值，关联规则实践 - 有监督学习 - 回归，分类 - 数据拆分实践 - K近邻算法及其实现 - 支持向量机（SVM）及其应用 - 线性回归 - 梯度下降概述 - 独热编码，鸢尾花数据集概览 - 朴素贝叶斯，逻辑回归，决策树（ID3算法） 该课程的结构旨在确保学员能够在实际应用中有效地利用所学知识，培养解决实际问题的能力。
The course covers Machine Learning in exhaustive way. The presentations and hands-on practical are made such that it's made easy. The knowledge gained through this tutorial series can be applied to various real world scenarios.UnSupervised learning does not require to supervise the model. Instead, it allows the model to work on its own to discover patterns and information that was previously undetected. It mainly deals with the unlabeled data. The machine is forced to build a compact internal representation of its world and then generate imaginative content.Supervised learning deals with providing input data as well as correct output data to the machine learning model. The goal of a supervised learning algorithm is to find a mapping function to map the input with the output. It infers a function from labeled training data consisting of a set of training examples.UnSupervised Learning and Supervised Learning are dealt in-detail with lots of bonus topics.The course contents are given below:Introduction to Machine LearningIntroductions to Deep LearningInstallationsUnsupervised LearningClustering, AssociationAgglomerative, Hands-on(PCA: Principal Component Analysis)DBSCAN, Hands-onMean Shift, Hands-onK Means, Hands-onAssociation Rules, Hands-onSupervised LearningRegression, ClassificationTrain Test Split, Hands-onk Nearest Neighbors, Hands-onkNN Algo ImplementationSupport Vector Machine (SVM), Hands-onSupport Vector Regression (SVR), Hands-onSVM (non linear svm params), Hands-onSVM kernel trick, Hands-onSVM mathematicsLinear Regression, Hands-onGradient Descent overviewOne Hot Encoding (Dummy vars)One Hot Encoding with Linear Regr, Hands-onNaive Bayes OverviewBayes' Concept , Hands-onNaive Bayes' Classifier, Hands-onLogistic Regression OverviewBinary Classification Logistic RegressionMulticlass Classification Logistic RegressionDecision TreeID3 Algorithm - ClassifierID3 Algorithm - RegressionInfo about Datasets
