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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-and-ayush/
课程评论：没有评论
课程名称：机器学习与人工智能 - 初学者 概述：开启人工智能与机器学习的世界，这门面向初学者的课程将为您奠定坚实的基础！无论您是学生、渴望成为数据科学家，还是科技爱好者，课程都将提供关于人工智能（AI）、机器学习（ML）和数据科学的清晰知识。从AI的基本概念入手，包括什么是AI、它的不同子集（机器学习、深度学习、计算机视觉、强化学习）及其相互关系，然后深入探讨数据科学在现代AI应用中的角色。 该课程简化了复杂的主题，例如： - 线性与多元线性回归 - 成本函数与梯度下降 - 多项式回归 - 支持向量机（SVM） - 决策树回归 - 随机森林算法 - K均值聚类 所有内容均通过真实世界的示例、可视化解释及公式解析来确保实际理解。课程不需要之前的编码经验或数学背景，旨在使概念直观且可操作，让您不仅理解理论，还能学会应用。 您将学习到的内容包括： - 什么是AI及其主要子集（ML、DL、CV、RL） - 有监督学习与无监督学习 - AI/ML的实际应用 - 数据科学如何驱动机器学习 - 如何构建和理解线性与多元线性回归模型 - 成本函数与梯度下降的工作原理 - 简单解释的决策树、随机森林和SVM - K均值聚类的基础知识 通过这门课程，您将获得关于AI和机器学习的全面知识，并具备在实际场景中应用这些知识的能力。
Unlock the world of Artificial Intelligence and Machine Learning with this beginner-friendly course! Whether you're a student, aspiring data scientist, or tech enthusiast, this course gives you a solid foundation in AI, ML, and Data Science-with zero fluff and full clarity.You'll start with the basics of AI, including what AI is, its various subsets (Machine Learning, Deep Learning, Computer Vision, Reinforcement Learning), and how they relate to each other. From there, we dive deep into the role of Data Science in modern AI applications.This course simplifies complex topics like:Linear and Multiple Linear RegressionCost Functions & Gradient DescentPolynomial RegressionSupport Vector Machines (SVM)Decision Tree RegressionRandom Forest AlgorithmK-Means ClusteringAll with real-world examples, visual explanations, and formula breakdowns to ensure practical understanding.No prior coding experience or math-heavy background is needed. This course is designed to make concepts intuitive and actionable-so you not only understand the theory but know how to use it.What You'll Learn:What is AI and its key subsets (ML, DL, CV, RL)Supervised vs Unsupervised LearningReal-world applications of AI/MLHow data science powers machine learningBuild and understand linear & multiple linear regression modelsHow cost functions and gradient descent workDecision Trees, Random Forests & SVMs explained simplyBasics of clustering with K-Means
