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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-for-data-science/
课程评论:没有评论
课程名称:数据科学的机器学习入门 课程概述:本课程由“后院数据科学家”带领学员探索机器学习在数据科学中的应用,适合所有人群。本课程不仅阐释机器学习的概念,还将其与当今科技和社会变革的紧密关系结合起来。课程内容包括计算机科学的基础定义、数据的意义、人工智能的概念、机器学习的原理,以及数据科学如何与这些概念相互关联。 课程的更新版本包含62节讲座和15个部分,涵盖了机器学习的核心概念以及如何通过机器学习解决现实问题。学员将学习到如何处理数据、选择合适的机器学习算法、以及建立模型的关键步骤。此外,课程还将讨论机器学习在生活中的实际应用,强调在数据科学领域的重大影响。 课程的重心是实际应用,通过“泰坦尼克号”案例,学员将学习数据准备、算法选择、模型评估以及结果展示等实际环节。同时,课程附带一项额外内容“进一步探索的魔法”,以帮助学员在完成课程后继续学习。 本课程特别适合希望对机器学习有基本了解并希望将其应用于数据科学领域的初学者。加入我们,开启你的机器学习探索之旅吧!
Disclaimer:The second of this course demonstrates techniques using Jupyter Notebooks from Anaconda. You are welcome to follow along (however), it is not required to do these exercises to complete this course. If you are a Udemy Business user, please check with your employer before downloading software.Welcome!: Thank you all for the huge response to this emerging course! We are delighted to have over 20,000 students in over 160 different countries. I'm genuinely touched by the overwhelmingly positive and thoughtful reviews. It's such a privilege to share and introduce this important topic with everyday people in a clear and understandable way. I'm also excited to announce that I have created real closed captions for all course material, so weather you need them due to a hearing impairment, or find it easier to follow long (great for ESL students!).I've got you covered. Most importantly: To make this course "real", we've expanded. In November of 2018, the course went from 41 lectures and 8 sections, to 62 lectures and 15 sections! We hope you enjoy the new content! Unlock the secrets of understanding Machine Learning for Data Science!In this introductory course, the "Backyard Data Scientist" will guide you through wilderness of Machine Learning for Data Science. Accessible to everyone, this introductory course not only explains Machine Learning, but where it fits in the "techno sphere around us", why it's important now, and how it will dramatically change our world today and for days to come. Our exotic journey will include the core concepts of:The train wreck definition of computer science and one that will actually instead make sense. An explanation of data that will have you seeing data everywhere that you look! One of the "greatest lies" ever sold about the future computer science. A genuine explanation of Big Data, and how to avoid falling into the marketing hype. What is Artificial intelligence? Can a computer actually think? How do computers do things like navigate like a GPS or play games anyway? What is Machine Learning? And if a computer can think - can it learn? What is Data Science, and how it relates to magical unicorns! How Computer Science, Artificial Intelligence, Machine Learning, Big Data and Data Science interrelate to one another. We'll then explore the past and the future while touching on the importance, impacts and examples of Machine Learning for Data Science:How a perfect storm of data, computer and Machine Learning algorithms have combined together to make this important right now. We'll actually make sense of how computer technology has changed over time while covering off a journey from 1956 to 2014. Do you have a super computer in your home? You might be surprised to learn the truth. We'll discuss the kinds of problems Machine Learning solves, and visually explain regression, clustering and classification in a way that will intuitively make sense.Most importantly we'll show how this is changing our lives. Not just the lives of business leaders, but most importantly…you too!To make sense of the Machine part of Machine Learning, we'll explore the Machine Learning process:How do you solve problems with Machine Learning and what are five things you must do to be successful? How to ask the right question, to be solved by Machine Learning. Identifying, obtaining and preparing the right data … and dealing with dirty data!How every mess is "unique" but that tidy data is like families!How to identify and apply Machine Learning algorithms, with exotic names like "Decision Trees", "Neural Networks" "K's Nearest Neighbors" and "Naive Bayesian Classifiers" And the biggest pitfalls to avoid and how to tune your Machine Learning models to help ensure a successful result for Data Science. Our final section of the course will prepare you to begin your future journey into Machine Learning for Data Science after the course is complete. We'll explore:How to start applying Machine Learning without losing your mind. What equipment Data Scientists use, (the answer might surprise you!) The top five tools Used for data science, including some surprising ones. And for each of the top five tools - we'll explain what they are, and how to get started using them. And we'll close off with some cautionary tales, so you can be the most successful you can be in applying Machine Learning to Data Science problems. Bonus Course! To make this "really real", I've included a bonus course! Most importantly in the bonus course I'll include information at the end of every section titled "Further Magic to Explore" which will help you to continue your learning experience. In this bonus course we'll explore:Creating a real live Machine Learning Example of Titanic proportions. That's right - we are going to predict survivability onboard the Titanic! Use Anaconda Jupyter and python 3.xA crash course in python - covering all the core concepts of Python you need to make sense of code examples that follow. See the included free cheat sheet! Hands on running Python! (Interactively, with scripts, and with Jupyter)Basics of how to use Jupyter NotebooksReviewing and reinforcing core concepts of Machine Learning (that we'll soon apply!)Foundations of essential Machine Learning and Data Science modules:NumPy - An Array ImplementationPandas - The Python Data Analysis LibraryMatplotlib - A plotting library which produces quality figures in a variety of formatsSciPy - The fundamental Package for scientific computing in PythonScikit-Learn - Simple and efficient tools data mining, data analysis, and Machine LearningIn the titanic hands on example we'll follow all the steps of the Machine Learning workflow throughout:Asking the right question.Identifying, obtaining, and preparing the right dataIdentifying and applying a Machine Learning algorithmEvaluating the performance of the model and adjustingUsing and presenting the modelWe'll also see a real world example of problems in Machine learning, including underfit and overfit. The bonus course finishes with a conclusion and further resources to continue your Machine Learning journey. So I invite you to join me, the Backyard Data Scientist on an exquisite journey into unlocking the secrets of Machine Learning for Data Science - for you know - everyday people like you! Sign up right now, and we'll see you - on the other side.