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所在平台: Udemy |
课程主页: https://www.udemy.com/course/complete-machine-learning-data-science-libraries-with-python/
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课程名称：全面机器学习与数据科学（Python ML A-Z） 课程概述：人工智能是下一个数字前沿，它对商业和社会产生深远影响。根据《财富商业洞察》的预测，全球人工智能市场规模预计到2026年将达到2025.7亿美元。本课程关注数据科学与机器学习（ML），不仅提供实践性强的“动手”学习体验，还包括多个实际案例，帮助学生理解工业需求和工作文化，这是开发任何高水平AI应用所必需的。本课程包含多个机器学习项目，包括： 1. 项目 - 使用K均值聚类进行客户细分 2. 项目 - 使用机器学习（Python）进行虚假新闻检测 3. 项目 - 使用机器学习预测COVID-19感染概率 4. 项目 - 使用K均值聚类进行图像压缩 课程内容包括： - 数据科学概述 - 人工智能、机器学习和深度学习的概念 - 机器学习的概念：监督学习、无监督学习和强化学习 - 数据分析的Python工具：Numpy、Pandas、Matplotlib - 使用Google Colab、Anaconda和Jupyter Notebook进行工作环境设置 - 监督学习：回归、分类、多元线性回归（波士顿房价预测）、逻辑回归（鸢尾花数据集）、朴素贝叶斯分类器（葡萄酒数据集和文本分类）、决策树、K近邻算法（KNN）、支持向量机算法和随机森林算法 - 无监督学习的类型、优缺点以及聚类的定义 - K均值聚类和图像压缩 - 模型拟合过程：欠拟合、过拟合及最佳拟合，如何避免过拟合 - 特征工程和Python基础 近年来，无人驾驶汽车、数字助手、机器人工厂工作人员和智能城市的出现证明了智能机器的可能性。人工智能正在改变零售、制造、金融、医疗和媒体等多个行业，并持续拓展新的领域。每天都有新的应用、产品或服务推出，展示其利用机器学习不断变得更智能和更优秀。 注意：课程描述中提供了参考资料，可通过下载链接获取数据集。
Artificial Intelligence is the next digital frontier, with profound implications for business and society. The global AI market size is projected to reach $202.57 billion by 2026, according to Fortune Business Insights.This Data Science & Machine Learning (ML) course is not only ‘Hands-On' practical based but also includes several use cases so that students can understand actual Industrial requirements, and work culture. These are the requirements to develop any high level application in AI. In this course several Machine Learning (ML) projects are included.1) Project - Customer Segmentation Using K Means Clustering2) Project - Fake News Detection using Machine Learning (Python)3) Project COVID-19: Coronavirus Infection Probability using Machine Learning4) Project - Image compression using K-means clustering Color Quantization using K-MeansThis course include topics --What is Data Science Describe Artificial Intelligence and Machine Learning and Deep Learning Concept of Machine Learning - Supervised Machine Learning , Unsupervised Machine Learning and Reinforcement LearningPython for Data Analysis- Numpy Working envirnment-Google ColabAnaconda Installation Jupyter Notebook Data analysis-PandasMatplotlib What is Supervised Machine LearningRegressionClassification Multilinear Regression Use Case- Boston Housing Price Prediction Save Model Logistic Regression on Iris Flower Dataset Naive Bayes Classifier on Wine Dataset Naive Bayes Classifier for Text Classification Decision TreeK-Nearest Neighbor(KNN) Algorithm Support Vector Machine AlgorithmRandom Forest Algorithm IWhat is UnSupervised Machine Learning Types of Unsupervised Learning Advantages and Disadvantages of Unsupervised Learning What is clustering? K-means Clustering Image compression using K-means clustering Color Quantization using K-Means Underfitting, Over-fitting and best fitting in Machine Learning How to avoid Overfitting in Machine LearningFeature EngineeringTeachable MachinePython BasicsIn the recent years, self-driving vehicles, digital assistants, robotic factory staff, and smart cities have proven that intelligent machines are possible. AI has transformed most industry sectors like retail, manufacturing, finance, healthcare, and media and continues to invade new territories. Everyday a new app, product or service unveils that it is using machine learning to get smarter and better.NOTE:- In description reference notes also provided , open reference notes , there is Download link. You can download datasets there.
