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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-for-beginner-ai-data-science/
课程评论：没有评论
课程名称：初学者的机器学习（人工智能）- 数据科学 课程概述：本课程旨在帮助初学者从零开始学习机器学习的基础知识，适合希望了解机器学习和人工智能基本概念的学生。课程包括视频讲解，涵盖基础入门、详细理论和图形解释。通过使用Python编程解决日常项目，课程还提供可下载的电子书和Python代码文件，覆盖了所有章节内容。讲座生动、有趣且节奏快速，能在较短时间内引导学员掌握整个课程内容。每个主题都进行了深入讲解，力求以最简单的方式帮助学员理解各个概念。该课程特别推荐给那些在大学和学院学习，尚未掌握机器学习基础的学生。 课程目标是以简单易懂的方式讲解机器学习和人工智能，追求每个定义和代码的简洁性和准确性。所有代码均通过Colab在线编辑器进行操作。Python作为一种流行的编程语言，因其用户友好而广受欢迎，逐渐取代Java成为初学者最常用的入门语言，使得学习者能够专注于掌握编程概念，而非繁琐的细节。 课程涵盖的主题包括： - 机器学习简介 - 监督学习、非监督学习和强化学习 - 机器学习类型 - 主成分分析（PCA） - 混淆矩阵 - 欠拟合和过拟合 - 分类 - 线性回归 - 非线性回归 - 支持向量机分类器 - 线性SVM和非线性SVM机器模型 - 核技术 - 使用Python的SVM项目 - K-近邻（KNN）分类器 - KNN机器模型中的k值 - 欧几里得距离和曼哈顿距离 - KNN机器模型中的离群值 - 使用Python的KNN机器模型项目 - 朴素贝叶斯分类器和贝叶斯规则 - 使用Python的朴素贝叶斯机器模型项目 - 逻辑回归分类器和非线性逻辑回归 - 使用Python的逻辑回归机器模型项目 - 决策树分类器 - 使用Python的决策树机器模型项目 本课程是机器学习入门的理想选择，帮助学生轻松掌握所需知识。
Learn Machine Learning from scratch, this course for beginners who want to learn the fundamental of machine learning and artificial intelligence. The course includes video explanation with introductions(basics), detailed theory and graphical explanations. Some daily life projects have been solved by using Python programming. Downloadable files of ebooks and Python codes have been attached to all the sections. The lectures are appealing, fancy and fast. They take less time to walk you through the whole content. Each and every topic has been taught extensively in depth to cover all the possible areas to understand the concept in most possible easy way. It's highly recommended for the students who don't know the fundamental of machine learning studying at college and university level.The objective of this course is to explain the Machine learning and artificial intelligence in a very simple and way to understand. I strive for simplicity and accuracy with every definition, code I publish. All the codes have been conducted through colab which is an online editor. Python remains a popular choice among numerous companies and organization. Python has a reputation as a beginner-friendly language, replacing Java as the most widely used introductory language because it handles much of the complexity for the user, allowing beginners to focus on fully grasping programming concepts rather than minute details. Below is the list of topics that have been covered:Introduction to Machine LearningSupervised, Unsupervised and Reinforcement learningTypes of machine learningPrincipal Component Analysis (PCA)Confusion matrixUnder-fitting & Over-fittingClassificationLinear RegressionNon-linear RegressionSupport Vector Machine ClassifierLinear SVM machine modelNon-linear SVM machine modelKernel techniqueProject of SVM in PythonK-Nearest Neighbors (KNN) Classifierk-value in KNN machine modelEuclidean distance Manhattan distanceOutliers of KNN machine modelProject of KNN machine model in PythonNaive Bayes ClassifierByes ruleProject of Naive Bayes machine model in PythonLogistic Regression ClassifierNon-linear logistic regressionProject of Logistic Regression machine model in PythonDecision Tree ClassifierProject of Decision Tree machine model in Python
