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所在平台: Udemy |
课程主页: https://www.udemy.com/course/practical-machine-learning-python/
课程评论：没有评论
课程名称：Python 实用机器学习实例 课程概述：您是一名对构建机器学习和深度学习模型感兴趣的开发者吗？您想在迅速发展的人工智能领域中掌握技能吗？通过实践的案例学习是获得这些技能的最快、最简单方式。LinkedIn发布的年度“新兴职位”清单中，人工智能专家是增长最快的职位类别，相关招聘在过去几年增长了74%！ 本课程将通过多个实用的机器学习实例，包括图像识别、情感分析、欺诈检测等，带您深入学习。您将掌握现代框架的使用，如Tensorflow 2/Keras、NumPy、Pandas和Matplotlib。同时，您还将学习如何使用强大而免费的云开发环境，比如Google Colab。 每个实例都是独立的，并遵循一致的结构，您可以按任意顺序进行学习。在每个实例中，您将了解： - 问题的性质 - 如何分析和可视化数据 - 如何选择合适的模型 - 如何为训练和测试准备数据 - 如何构建、测试和改进机器学习模型 - 常见问题的解答 - 下一步该做什么 当然，对于每个实例，您需要掌握一些基础知识。基础部分将根据需要进行介绍。您可以按照自己的兴趣和节奏进行学习。 为什么选择我作为您的讲师？ - 实践经验：我积极开发现实世界中的机器学习系统，并将这一经验带入课程。 - 教学经验：我已有20年以上的写作和教学经历。 - 质量承诺：我不断更新课程，提供改进和新材料。 - 持续支持：您可以随时问我任何问题，我会迅速回复。 精选评价： 课程解释清晰简明，没有行话，笔记本展示整齐有序，是创建机器学习模型的逐步指南，非常适合初学者了解模型是如何根据问题创建以及提高准确性所用的技术。共享的资源与讲师的及时回应使我们能够深入了解主题，值得所有机器学习爱好者推荐。 - Ashraf UI 课程易于理解，实例展示良好，在Google Colab上进行的实践练习非常不错，可以轻松测试函数或代码行。最后三个部分是深度学习的实用练习，展示良好且有详细注释。 - Iheb GANDOUZ 讲解方式很酷。我以前上课一个小时就会感到无聊，但这位讲师的指导让我感到很有趣。 - Anu Priya J 更新内容： 2020年1月：新增数学与机器学习基础部分，包括逻辑回归、损失与成本函数、梯度下降及反向传播，所有实例更新为Tensorflow 2（也提供Tensorflow 1示例）、Jupyter Notebook介绍、Python快速入门和基础线性代数。 2020年3月：新增情感和自然语言处理部分，包括现代的BERT分类模型，准确性惊人。 2020年4/5月：众多作业改进，例如自学或指导方式，增加Google Colab讲座、Python快速入门、自己分类图片等内容。
Are you a developer interested in building machine learning and deep learning models? Do you want to be proficient in the rapidly growing field of artificial intelligence? One of the fastest and easiest ways to learn these skills is by working through practical hands-on examples.LinkedIn released it's annual "Emerging Jobs" list, which ranks the fastest growing job categories. The top role is Artificial Intelligence Specialist, which is any role related to machine learning. Hiring for this role has grown 74% in the past few years!In this course, you will work through several practical, machine learning examples, such as image recognition, sentiment analysis, fraud detection, and more. In the process, you will learn how to use modern frameworks, such as Tensorflow 2/Keras, NumPy, Pandas, and Matplotlib. You will also learn how use powerful and free development environments in the cloud, like Google Colab.Each example is independent and follows a consistent structure, so you can work through examples in any order. In each example, you will learn:The nature of the problemHow to analyze and visualize dataHow to choose a suitable modelHow to prepare data for training and testingHow to build, test, and improve a machine learning modelAnswers to common questionsWhat to do nextOf course, there are some required foundations you will need for each example. Foundation sections are presented as needed. You can learn what interests you, in the order you want to learn it, on your own schedule.Why choose me as your instructor?Practical experience. I actively develop real world machine learning systems. I bring that experience to each course.Teaching experience. I've been writing and teaching for over 20 years.Commitment to quality. I am constantly updating my courses with improvements and new material. Ongoing support. Ask me anything! I'm here to help. I answer every question or concern promptly.Selected Reviewsclear explanations..to the point and no jargon..neat presentation of notebooks with codes..it's a step by step guide on creating machine learning models using Google colab..the models explained here are basic and thus perfect for beginners ,to understand how machine learning models are created based on the given problem and about techniques used to improve the accuracy..with the resources shared and Mr.Madhu's immediate response to messages/QA,one can learn more about a topic..highly recommended to all machine learning enthusiasts. - Ashraf UIThe cours is easy to understand and well presented, same thing for the practical examples Using google colab was a very good idea to present the course and to do the exercices , we can easily test a function or a line of code. The last three sections are very intresting, they are practical exercices for deep learning well presented and commented - Iheb GANDOUZThe way it is explained is really cool. I used to be bored after an hour during lectures, but the guide somehow makes it very interesting..- Anu Priya JJanuary 2020 updates:New mathematics and machine learning foundation section includingLogistic regression, loss and cost functions, gradient descent, and backpropagationAll examples updated to use Tensorflow 2 (Tensorflow 1 examples are available also)Jupyter note introductionPython quick startBasic linear algebraMarch 2020 updates:A sentiment and natural language processing sectionThis includes a modern BERT classification model with surprisingly high accuracyApril/May 2020 updates:Numerous assignment improvements, e.g. self-paced or guided approachAdd lectures on Google Colab, Python quick start, classify your own images and more!
