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所在平台: Udemy |
课程主页: https://www.udemy.com/course/learning-path-python-advanced-machine-learning-with-python/
课程评论：没有评论
课程名称：学习路径：Python：高级机器学习与Python 课程概述： 想要迈入数据科学的世界，并学习有效的机器学习工具与技术吗？那么这条学习路径非常适合你。Packt的视频学习路径是将多个独立视频产品逻辑性地组合起来的系列，每个视频都在前一个视频所学技能的基础上展开。机器学习和数据科学是当今技术领域的热门词汇。机器学习是理解数据的算法应用与科学，作为计算机科学中最令人兴奋的领域，其复苏的兴趣与数据科学的流行息息相关。我们生活在一个数据泛滥的时代，利用自我学习算法，您可以将数据转化为知识，获得极具洞察力的数据分析结果。 Python在近几年已经超越其他编程语言，因其多功能性而备受青睐，它已广泛应用于人工智能、机器学习、自然语言处理、数据科学等多个复杂过程。 学习路径亮点： - 使用机器学习和Python解决有趣的现实问题 - 通过Python可视化多维数据并提取有用特征 学习旅程概览： 本学习路径是您进入机器学习的入口，课程将从机器学习和Python语言的介绍开始。您将学习重要概念，包括探索性数据分析、数据预处理、特征提取、数据可视化、聚类、分类、回归和模型性能评估。通过多个项目，您将掌握几个重要机器学习算法的机制，并将逐步被引导从零开始构建自己的模型。您会学习如何解决数据驱动问题，并使用强大而简洁的Python语言实现解决方案。课程中将提供有趣且易于跟随的示例，包括新闻主题分类、垃圾邮件检测、在线广告点击率预测和股价预测，让您全程投入。 课程最后将包含六个独立项目，帮助您精通Python下的机器学习。最终，您将对机器学习生态系统有广泛的了解，并掌握应用机器学习技术的最佳实践。在学习结束时，您将能够熟练运用Python包和库，实施自己的机器学习模型。 专家介绍： 我们汇集了几位著名作者的优秀作品，以确保您的学习旅程顺利。Yuxi (Hayden) Liu 是一家最大的私人加拿大人工智能研发公司的应用研究科学家，专注于开发机器学习系统与模型，并为特定任务实施适当架构。他曾在多个计算广告公司担任数据科学家，应用机器学习技术进行广告优化，拥有多篇IEEE期刊和会议论文。Alexander T. Combs是一名经验丰富的数据科学家和开发者，具有金融数据提取、自然语言处理和定量建模的背景，目前是纽约市数据科学沉浸式项目的全职首席讲师。
Are you interested to enter into the world of data science and learn the most effective machine learning tools and techniques with Python? then you should surely go for this Learning Path. Packt's Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. Machine learning and data science are some of the top buzzwords in the technical world today. Machine learning - the application and science of algorithms that makes sense of data, is the most exciting field of all the computer sciences! The resurgent interest in machine learning is due to the same factors that have made data science more popular than ever. We are living in an age where data comes in abundance; using the self-learning algorithms from the field of machine learning, you can turn this data into knowledge. Machine learning gives you unimaginably powerful insights into data. Python has topped the charts in the recent years over other programming languages. The usage of Python is such that it cannot be limited to only one activity. Its growing popularity has allowed it to enter into some of the most popular and complex processes such as artificial intelligence, machine learning, natural language processing, data science, and so on. The highlights of this Learning Path are: Solve interesting, real-world problems using machine learning and Python as the learning journey unfolds Use Python to visualize data spread across multiple dimensions and extract useful features Let's take a quick look at your learning journey. This Learning Path is your entry point to machine learning. It starts with an introduction to machine learning and Python language. You'll learn the important concepts such as exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression, and model performance evaluation. With the help of the various projects included, you'll acquire the mechanics of several important machine learning algorithms. You'll also be guided step-by-step to build your own models from scratch. You'll learn to tackle data-driven problems and implement your solutions with the powerful yet simple Python language. Interesting and easy-to-follow examples-including news topic classification, spam email detection, online ad click-through prediction, and stock prices forecasts-will keep you glued to the screen. Moving further, six different independent projects will help you master machine learning in Python. Finally, you'll have a broad picture of the machine learning ecosystem and mastered best practices for applying machine learning techniques. By the end of this Learning Path, you'll have learned to apply various machine learning algorithms with Python packages and libraries to implement your own machine learning models. Meet Your Experts: We have combined the best works of the following esteemed authors to ensure that your learning journey is smooth: Yuxi (Hayden) Liu is currently an applied research scientist working in the largest privately-owned Canadian artificial intelligence R & D company. He is focused on developing machine learning systems and models and implementing appropriate architectures for given learning tasks, including deep neural networks, convolutional neural networks, recurrent networks, SVM, and random forest. He has worked for a few years as a data scientist at several computational advertising companies, where he applied his machine learning expertise in ad optimization. Yuxi earned his degree from the University of Toronto, and published five first-authored IEEE transactions and conference papers during his master's research. He has authored a Packt book titled Python Machine Learning By Example, which was ranked the #1 best seller in Amazon India in 2017. He is also a machine learning education enthusiast and provides weekly training in machine learning. Alexander T. Combs is an experienced data scientist, strategist, and developer with a background in financial data extraction, natural language processing and generation, and quantitative and statistical modeling. He is currently a full-time lead instructor for a data science immersive program in New York City.
