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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-mining-python/
课程评论：没有评论
课程名称：商业分析的数据挖掘与Python数据分析 课程概述：您是否希望像专业人士一样学习数据挖掘？您想通过数据科学和分析以及可解释的人工智能找到可行的商业洞察？您来对地方了。本课程将向您展示我在专业生涯中目睹的最具影响力的数据挖掘算法，帮助您获得有意义的洞察并解读数据。在信息量巨大的电子表格时代，您可能会感到不知所措，而数据挖掘技术正是在这种情况下应运而生，能够快速分析、寻找模式并为您提供结果。 课程吸引您的四个理由： 1. 理解模型直观：您将学习模型的直观，而不必过于关注数学。了解模型如何运作及其基础假设至关重要，我会通过文字、图表和隐喻来解释每个模型，让数学和希腊字母尽量最小化。 2. 课程结构全面：课程覆盖最有影响力的数据挖掘技术，包含我认为当前最实用和受欢迎的算法，包括监督学习、生存分析、Cox比例风险回归、CHAID、无监督学习、聚类分析、主成分分析（PCA）、关联规则学习等。 3. 逐行编码Python：编程对初学者来说可能很具挑战性，我会逐行带您完成每个Python代码片段，并逐步解释所需的所有参数和函数，让您最终拥有可以在问题中使用的代码模板。 4. 注重实践：每一节课结束时都会有一个挑战，让您立即应用所学内容。我会提供一个数据集和需要完成的任务，确保您能够真正掌握所有技术。每项技术将有两个案例研究。 希望这能激发您的兴趣，期待在课程中见到您！
Are you looking to learn how to do Data Mining like a pro? Do you want to find actionable business insights using data science and analytics and explainable artificial intelligence? You have come to the right place.I will show you the most impactful Data Mining algorithms using Python that I have witnessed in my professional career to derive meaningful insights and interpret data.In the age of endless spreadsheets, it is easy to feel overwhelmed with so much data. This is where Data Mining techniques come in. To swiftly analyze, find patterns, and deliver an outcome to you. For me, the Data Mining value added is that you stop the number crunching and pivot table creation, leaving time to come with actionable plans based on the insights.Now, why should you enroll in the course? Let me give you four reasons.The first is that you will learn the models' intuition without focusing too much on the math. It is crucial that you know why a model makes sense and the underlying assumptions behind it. I will explain to you each model using words, graphs, and metaphors, leaving math and the Greek alphabet to the bare minimum.The second reason is the thorough course structure of the most impactful Data Mining techniques for Data Science and Business Analytics. Based on my experience, the course curriculum has the algorithms I believe to be most impactful, up-to-date, and sought after. Here is the list of the algorithms we will learn:Supervised Machine LearningSurvival AnalysisCox Proportional Hazard RegressionCHAIDUnsupervised Machine LearningCluster Analysis - Gaussian Mixture ModelDimension Reduction - PCA and Manifold LearningAssociation Rule Learning· Explainable Artificial IntelligenceRandom Forest and Feature Seletion and ImportanceLIMEXGBoost and SHAPThe third reason is that we code Python together, line by line. Programming is challenging, especially for beginners. I will guide you through every Python code snippet. I will also explain all parameters and functions that you need to use, step by step. In the end, you will have code templates ready to use in your problems.The final reason is that you practice, practice, practice. At the end of each section, there is a challenge. The goal is that you apply immediately what you have learned. I give you a dataset and a list of actions you need to take to solve it. I think it is the best way to really cement all the techniques in you. Hence, there will be 2 case studies per technique.I hope to have spiked your interest, and I am looking forward to seeing you inside!
