机器学习和人工智能课程

用sklearn介绍机器学习和| Numpy | Pandas | Matplotlib | ML数学和统计，本课程是对机器学习和人工智能的全面介绍。它是为没有这些主题经验的初学者设计的。到课程结束时，学生将在机器学习和人工智能的基础知识方面有很强的基础。他们将能够建立和训练机器学习模型来解决现实世界的问题。机器学习和人工智能是当今最重要和发展最快的两个技术领域。机器学习是在没有明确编程的情况下，训练计算机从数据中学习并做出预测的过程。人工智能是一个更广泛的领域，包括机器学习，以及自然语言处理和计算机视觉等其他领域。机器学习和人工智能已经在广泛的应用中使用，包括:推荐系统:机器学习被用来为推荐系统提供动力，例如网飞和亚马逊用来向用户推荐产品和电影的系统。欺诈检测:机器学习用于检测欺诈交易和其他类型的欺诈。医疗诊断:机器学习正被用来开发新的工具，以帮助医生诊断疾病和推荐治疗方法。自动驾驶汽车:自动驾驶汽车依靠机器学习来感知周围环境，并决定如何导航。Machine Learning and Artificial Intelligent for Starters

课程益处：这门课程将为学生提供以下益处:机器学习基础的坚实基础，以及建立和训练机器学习模型的能力应用机器学习和人工智能解决现实世界问题的能力在就业市场中的竞争优势先决条件本课程没有正式的先决条件。然而，学生应该具有Python或另一种编程语言的基本编程经验。学习目标完成本课程后，学生将能够:定义机器学习和人工智能解释不同类型的机器学习算法构建和训练机器学习模型评估机器学习模型应用机器学习和人工智能解决现实世界的问题

由Fanuel Mapuwei创作
MP4 |视频:h264，1280×720 |音频:AAC，44.1 KHz，2声道
类型:电子学习|语言:英语|时长:58节课(10小时12分钟)|大小:3.26 GB

你会学到什么
用于机器学习的Python编程
机器学习算法
熊猫数据分析
ML的统计数据
ML的数学

要求
不需要编程经验。

Introduction to Machine Learning and AI with sklearn | Numpy| Pandas | Matplotlib | ML Mathematics and Statistics

What you’ll learn
Python programming for Machine Learning
Machine Learning Algorithm
Pandas Data Analysis
Statistics for ML
Mathematics for ML

Requirements
No programming experience needed.

Description
This course is a comprehensive introduction to machine learning and artificial intelligence. It is designed for beginners who have no prior experience with these topics. By the end of the course, students will have a strong foundation in the fundamentals of machine learning and AI. They will be able to build and train machine-learning models to solve real-world problems.Machine learning and artificial intelligence are two of the most important and rapidly developing fields of technology today. Machine learning is the process of training computers to learn from data and make predictions without being explicitly programmed. Artificial intelligence is a broader field that encompasses machine learning, as well as other areas such as natural language processing and computer vision.Machine learning and AI are already being used in a wide range of applications, including:Recommender systems: Machine learning is used to power recommender systems, such as those used by Netflix and Amazon to recommend products and movies to their users.Fraud detection: Machine learning is used to detect fraudulent transactions and other types of fraud.Medical diagnosis: Machine learning is being used to develop new tools to help doctors diagnose diseases and recommend treatments.Self-driving cars: Self-driving cars rely on machine learning to perceive their surroundings and make decisions about how to navigate.Course BenefitsThis course will provide students with the following benefits:A strong foundation in the fundamentals of machine learning and AIThe ability to build and train machine learning modelsThe ability to apply machine learning and AI to solve real-world problemsA competitive advantage in the job marketPrerequisitesThere are no formal prerequisites for this course. However, students should have essential programming experience in Python or another programming language.Learning ObjectivesUpon completion of this course, students will be able to:Define machine learning and artificial intelligenceExplain the different types of machine learning algorithmsBuild and train machine learning modelsEvaluate machine learning modelsApply machine learning and AI to solve real-world problems