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所在平台: Udemy |
课程主页: https://www.udemy.com/course/master-machine-learning-in-python-with-scikit-learn/
课程评论：没有评论
课程名称：使用Scikit-Learn掌握Python中的机器学习 概述：你想在Python中开始学习机器学习和数据科学吗？本课程提供了一个全面且实践的机器学习入门介绍！我们将使用Scikit-Learn教你Python中的机器学习。Scikit-Learn是一个非常流行并且在许多机器学习任务中极其强大的库。在AI和机器学习的时代，掌握Scikit-Learn非常重要。课程将教你专业使用Scikit-Learn进行机器学习所需的所有知识。我们将从基础开始，逐步深入到更复杂的主题。 为什么选择我们？该课程是使用Scikit-Learn进行Python机器学习的全面介绍！我们不会回避技术细节，希望你能通过学到的Scikit-Learn技能脱颖而出。课程中包含精心制作的练习，以巩固我们教授的主题。在视频之间，我们提供小练习，帮助你加深理解。此外，我们还有较大的练习任务，你将获得一个Jupyter Notebook，解决与单个主题相关的一系列问题。这些练习包括数据处理和清理，贴近真实的机器学习场景。 我们是一对热衷于制作高质量课程的夫妻（Eirik和Stine）。Eirik在数据科学领域专业使用Scikit-Learn，而Stine则有大学编程教学的经验。我们都热爱Scikit-Learn，期待教你关于它的一切！ 我们将涵盖的主题：本课程将涵盖许多不同的主题，按出现顺序包括： - Scikit-Learn简介 - 线性回归 - 逻辑回归 - 数据预处理与管道 - 多项式回归 - 决策树和随机森林 - 交叉验证 - 正则化技术 - 支持向量机 - 降维与主成分分析（PCA） - 深度学习中的神经网络基础 - 监督学习与无监督学习 - 还有更多！ 通过完成我们的课程，你将对机器学习和Python库Scikit-Learn感到熟悉，并获得在Pandas中进行数据预处理的经验。这为你职业生涯中的机器学习工作打下了良好的基础。 还在犹豫吗？本课程提供30天退款政策，如果你对课程不满意，可以无忧退回。如果在阅读后仍然不确定，可以查看一些免费的预览，看看你是否喜欢它们。希望很快见到你！
Do you want to get started with machine learning and data science in Python? This course is a comprehensive and hands-on introduction to machine learning! We will use Scikit-Learn to teach you machine learning in Python!What this course is all about:We will teach you the ins and outs of machine learning and the Python library Scikit-Learn (sklearn). Scikit-Learn is super popular and incredibly powerful for many machine learning tasks. In the age of AI and ML, learning about machine learning in Scikit-Learn is crucial. The course will teach you everything you need to know to professionally use Scikit-Learn for machine learning. We will start with the basics, and then gradually move on to more complicated topics.Why choose us?This course is a comprehensive introduction to machine learning in Python by using Scikit-Learn! We don't shy away from the technical stuff and want you to stand out with your newly learned Scikit-Learn skills.The course is filled with carefully made exercises that will reinforce the topics we teach. In between videos, we give small exercises that help you reinforce the material. Additionally, we have larger exercises where you will be given a Jupiter Notebook sheet and asked to solve a series of questions that revolve around a single topic. The exercises include data processing and cleaning, making them much closer to real-life machine learning.We're a couple (Eirik and Stine) who love to create high-quality courses! Eirik has used Scikit-Learn professional as a data scientist, while Stine has experience with teaching programming at the university level. We both love Scikit-Learn and can't wait to teach you all about it!Topics we will cover:We will cover a lot of different topics in this course. In order of appearance, they are:Introduction to Scikit-LearnLinear RegressionLogistic RegressionPreprocessing and PipelinesPolynomial RegressionDecision Trees and Random ForestsCross-ValidationRegularization Techniques Support Vector MachinesDimensionality Reduction & PCABasics of Neural Networks used in Deep LearningSupervised and Unsupervised Learningand much more! By completing our course, you will be comfortable with both machine learning and the Python library Scikit-Learn. You will also get experience with preprocessing the data in Pandas. This gives you a great starting point for working professionally with machine learning.Still not decided?The course has a 30-day refund policy, so if you are unhappy with the course, then you can get your money back painlessly. If are still uncertain after reading this, then take a look at some of the free previews and see if you enjoy them. Hope to see you soon!
