|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-and-statistical-modeling-with-r/
课程评论：没有评论
课程名称：使用R示例的机器学习与统计建模 课程概述：在数据中发现他人无法察觉的模式，并作出正确的决策！随着现代科技和互联网的发展，数据量日益激增。成功的公司深知，通过识别数据中的模式可以在竞争激烈的市场中获得优势。掌握机器学习和统计建模的知识将使您能够识别这些模式，成为您公司或机构的宝贵资产，并提升您的职业生涯！营销公司利用机器学习识别潜在客户及最佳产品展示方式；科学家通过机器学习在心理学、物理学及计算机科学等领域发掘新见解；IT公司利用机器学习开发新搜索工具或尖端移动应用；保险公司、银行及投资基金运用机器学习做出明智的财务决策，包括算法交易；咨询公司借助机器学习帮助客户做出决策。实际上，人工智能的实现离不开这些建模工具，我们已经生活在一个受到机器学习算法深刻影响的世界中。 1. 什么是机器学习？ 机器学习是一系列现代统计方法，旨在根据基础（训练）数据创建模型，以预测新输入数据的结果。测试数据集用于评估模型的准确性。总结来说，机器学习几乎等同于统计建模。 2. 学习这些方法难吗？ 遗憾的是，机器学习学习材料通常相当技术化，需要大量的先验知识才能理解。本课程的主要目标是让理解这些工具变得直观和简单。尽管您需要具备一定的统计学和统计编程知识，但本课程旨在面向没有数量领域（例如数学或统计学）专业背景的人。任何定期处理数据的人都能从中受益。 3. 课程结构如何？ 为确保更好的学习效果，每个部分包括理论讲解、实际操作（示范R语言的示例）和练习。这些部分都附有代码PDF，供您自行尝试示范的代码。 4. 如何最佳准备以获益于本课程？ 这取决于您的先验知识。大致来说，您应该了解如何在R中处理标准任务（如基础R课程和R级别1课程）。您还应了解建模和统计学的基础知识，并如何在R中实现（可以参考R中的统计学课程）。有关特别优惠和组合，请查看讲师简介下方的r-tutorials网页。 准备好开始学习了吗？期待您的参加！
See things in your data that no one else can see - and make the right decisions!Due to modern technology and the internet, the amount of available data grows substantially from day to day. Successful companies know that. And they also know that seeing the patterns in the data gives them an edge on increasingly competitive markets. Proper understanding and training in Machine Learning and Statistical Modeling will give you the power to identify those patterns. This can make you an invaluable asset for your company/institution and can boost your career!Marketing companies use Machine Learning to identify potential customers and how to best present products.Scientists use Machine Learning to capture new insights in nearly any given field ranging from psychology to physics and computer sciences.IT companies use Machine Learning to create new search tools or cutting edge mobile apps.Insurance companies, banks and investment funds use Machine Learning to make the right financial decisions or even use it for algorithmic trading.Consulting companies use Machine Learning to help their customers on decision making.Artificial intelligence would not be possible without those modeling tools.Basically we already live in a world that is heavily influenced by Machine Learning algorithms.1. But what exactly is Machine Learning?Machine learning is a collection of modern statistical methods for various applications. Those methods have one thing in common: they try to create a model based on underlying (training) data to predict outcomes on new data you feed into the model. A test dataset is used to see how accurate the model works. Basically Machine learning is the same as Statistical Modeling. 2. Is it hard to understand and learn those methods?Unfortunately the learning materials about Machine Learning tend to be quite technical and need tons of prior knowledge to be understood. With this course it is my main goal to make understanding those tools as intuitive and simple as possible. While you need some knowledge in statistics and statistical programming, the course is meant for people without a major in a quantitative field like math or statistics. Basically anybody dealing with data on a regular basis can benefit from this course. 3. How is the course structured?For a better learning success, each section has a theory part, a practice part where I will show you an example in R and at last every section is enforced with exercises. You can download the code pdf of every section to try the presented code on your own. 4. So how do I prepare best to benefit from that course?It depends on your prior knowledge. But as a rule of thumb you should know how to handle standard tasks in R (courses R Basics and R Level 1). You should also know the basics of modeling and statistics and how to implement that in R (Statistics in R course).For special offers and combinations just check out the r-tutorials webpage which you can find below the instructor profile. What R you waiting for? Martin
