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所在平台: Udemy |
课程主页: https://www.udemy.com/course/python-machine-learning-bootcamp/
课程评论：没有评论
课程名称：Python机器学习实战营 课程概述：机器学习正日益受到关注，原因不言而喻。能够正确利用机器学习的公司，可以解决许多传统软件开发难以处理的复杂问题。然而，构建高质量的机器学习模型并不总是容易的，因此拥有扎实的基础知识非常重要，以便在工作中遇到模型问题时能够采取有效的修复步骤。本课程将每个所覆盖模型首先与理论背景结合，帮助学员理解模型的工作原理，从而建立直观的认知。接着，通过实践环节，学员将实现机器学习模型并应用于实际数据。这样，学员将获得实践经验，同时也能建立不同机器学习模型的理论基础，从而在实际情况中更好地选择和调整模型。 课程内容包括多个机器学习方面的主题。我们将从一个示例机器学习项目开始，经历从构思到开发最终可工作的模型的过程。课程将教授数据准备、清洗、特征工程、优化和学习技术等重要技术。 完成整个机器学习项目后，我们将深入不同的机器学习领域，研究每项任务及相应的模型，并了解如何调整理论部分所学的参数。主要探讨的领域包括： - 分类 - 回归 - 集成方法 - 降维 - 无监督学习 课程结束时，学员应具备扎实的机器学习知识基础，能够应对不同类型的问题，准备在工作中或技术面试中应用机器学习。
Machine learning is continuously growing in popularity, and for good reason. Companies that are able to make proper use of machine learning can solve complex problems that otherwise proved very difficult with standard software development.However, building good machine learning models is not always easy, and it's very important to have a solid foundation so that if/when you encounter problems with models on the job, you understand what steps to take to fix them.That's why this course focuses on always introducing every model that we cover first with the theoretical background of how the model works, so that you can build a proper intuition around its behaviour. Then we'll have the practical component, where we'll implement the machine learning model and use it on actual data. This way you gain both hands-on, as well as a solid theoretical foundation, of how the different machine learning models work, and you'll be able to use this knowledge to better chose and fix models, depending on the situation.In this course we'll cover many different types of machine learning aspects.We'll start with going through a sample machine learning project from idea to developing a final working model. We'll learn many important techniques around data preparation, cleaning, feature engineering, optimizaiton and learning techniques, and much more.Once we've gone through the whole machine learning project we'll then dive deeper into several different areas of machine learning, to better understand each task, and how each of the models we can use to solve these tasks work, and then also using each model and understanding how we can tune all the parameters we learned about in the theory components.These different areas that we'll dive deeper in to are:- Classification- Regression- Ensembles- Dimensionality Reduction- Unsupervised LearningAt the end of this course you should have a solid foundation of machine learning knowledge. You'll be able to build out machine learning solutions to different types of problems you'll come across, and be ready to start applying machine learning on the job or in technical interviews.
