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所在平台: Udemy |
课程主页: https://www.udemy.com/course/artificial-intelligence-and-machine-learning-complete-guide/
课程评论：没有评论
课程名称：人工智能与机器学习：完整指南 概述： 人工智能（AI）和机器学习（ML）是信息技术领域中最相关的领域，利用智能算法构建模拟人类能力的软件和硬件。随着机器学习市场的不断增长，相关专业人士的需求正逐步上升，许多研究表明，此领域的知识即将成为IT专业人员的一个必要条件。本课程为您提供进入该领域的理论和实践见解，内容涵盖从基础到最先进的技术。 本课程被视为全面的指南，会教授您开发适用于日常商业问题的人工智能解决方案所需的所有工具。课程内容分为七个部分：搜索算法、优化算法、模糊逻辑、机器学习、神经网络与深度学习、自然语言处理以及计算机视觉。您将学习每个主题的基本直觉，并逐步实现实际案例。 覆盖的项目/主题包括： - 使用贪婪搜索和A*（星形）搜索算法寻找城市地图上的最佳路线 - 使用渐进法、模拟退火和遗传算法选择最便宜的机票和最大化利润 - 利用模糊逻辑预测您将给餐厅的小费 - 使用朴素贝叶斯、决策树、规则、K近邻、逻辑回归和神经网络进行分类 - 使用线性回归预测房价 - 使用K均值算法对银行数据进行聚类 - 使用Apriori算法生成关联规则 - 数据预处理、降维和数据库中的异常检测 - 使用时间序列分析预测股票价格 - 在COVID-19病例数据库中进行数据可视化和探索 - 构建一个强化学习代理控制出租车进行旅客运输 - 使用卷积神经网络对猫和狗的图像进行分类 - 使用卷积神经网络对《辛普森一家》中的霍默和巴特的图像进行分类 - 使用自然语言处理技术进行词性标注、词形还原、词干提取、词云和命名实体识别 - 在Twitter数据集中实现情感分类器 - 在图像中进行人脸检测和识别 - 在视频中进行对象跟踪 - 使用先进的计算机视觉技术生成不存在于现实世界中的图像 每种问题的解决方案需要不同的技术，通过覆盖所有人工智能领域，您将知道在各种场景中使用哪些技术！课程将使用Python编程语言和图形工具Orange。如果您对Python不熟悉，将提供超过5小时的基础视频练习。本课程适合您第一次接触人工智能，因为它涵盖了理论和实践中所需的所有必要主题。如果您在该领域更有经验，可以将本课程作为参考，以学习新领域和复习概念。
The fields of Artificial Intelligence and Machine Learning are considered the most relevant areas in Information Technology. They are responsible for using intelligent algorithms to build software and hardware that simulate human capabilities. The job market for Machine Learning is on the rise in various parts of the world, and the trend is for professionals in this field to be in even higher demand. In fact, some studies suggest that knowledge in this area will soon become a prerequisite for IT professionals.To guide you into this field, this course provides both theoretical and practical insights into the latest Artificial Intelligence techniques. This course is considered comprehensive because it covers everything from the basics to the most advanced techniques. By the end, you will have all the necessary tools to develop Artificial Intelligence solutions applicable to everyday business problems. The content is divided into seven parts: search algorithms, optimization algorithms, fuzzy logic, machine learning, neural networks and deep learning, natural language processing, and computer vision. You will learn the basic intuition of each of these topics and implement practical examples step by step. Below are some of the projects/topics that will be covered:Finding optimal routes on city maps using greedy search and A* (star) search algorithmsSelection of the cheapest airline tickets and profit maximization using the following algorithms: hill climb, simulated annealing, and genetic algorithmsPrediction of the tip you would give to a restaurant using fuzzy logicClassification using algorithms such as Naïve Bayes, decision trees, rules, k-NN, logistic regression, and neural networksPrediction of house prices using linear regressionClustering bank data using k-means algorithmGeneration of association rules with Apriori algorithmData preprocessing, dimensionality reduction, and outlier detection in databasesPrediction of stock prices using time series analysisData visualization and exploration in the context of the COVID-19 disease databaseBuilding of a reinforcement learning agent to control a taxi for passenger transportationClassification of cat and dog images using convolutional neural networksClassification of Homer and Bart images from The Simpsons cartoon using convolutional neural networksPOS tagging, lemmatization, stemming, word cloud, and named entity recognition using natural language processing techniquesImplementation of a sentiment classifier in the context of a Twitter datasetFace detection and recognition in imagesObject tracking in videosGeneration of images that do not exist in the real world using advanced Computer Vision techniquesEach type of problem requires different techniques for its solution, so by covering all AI areas, you'll know which techniques to use in various scenarios! Throughout the course, we will use the Python programming language and the graphical tool Orange. If you are not familiar with Python, you will have access to over 5 hours of video exercises covering the basics of this programming language. This course is suitable for your first exposure to Artificial Intelligence, as it covers all the necessary topics in theory and practice. If you are more advanced in this field, you can use this course as a reference to learn new areas and review concepts.
