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所在平台: Udemy |
课程主页: https://www.udemy.com/course/learning-path-r-complete-machine-learning-deep-learning/
课程评论：没有评论
课程名称：学习路径：R：完整的机器学习与深度学习 课程概述：您是否希望深入了解机器学习和深度学习？如果是，这个学习路径正适合您。Packt的在线视频学习路径是一系列按逻辑和步骤安排的单独视频产品，使每个视频都建立在之前视频所学的技能基础上。R是数据科学领域的领先技术之一。本学习路径从基础开始，教您如何在现实场景中利用R开发和实现机器学习及深度学习算法。 学习路径的开始部分将涵盖R的一些基本概念，以帮助您复习R的知识，然后深入到高级技术。您将首先设置环境，并在R中执行数据ETL。接下来，您会学习重要的机器学习主题，包括数据分类、回归、聚类、关联规则挖掘和降维。 后续内容将介绍深度学习和人工神经网络的基础知识，重点探索ANNs、RNNs和CNNs等主题。最后，您将了解深度学习在多个领域的应用，并理解可扩展性、高性能计算（HPC）和特征工程的实际实施。 完成此学习路径后，您将对所有这些算法和技术有扎实的理解，并能够在数据科学项目中高效地实施它们。请不要担心如果现在觉得这些内容过于复杂，因为我们汇集了以下尊敬作者的最佳作品，以确保您的学习旅程顺利： 作者介绍： - Selva Prabhakaran是一名数据科学家，拥有7年数据科学经验，曾在大型电子商务公司处理复杂的现实数据科学问题，提供生产级解决方案。 - Yu-Wei Chiu (David Chiu)是LargitData的创始人，专注于提供大数据和机器学习产品，曾在趋势科技任职软件工程师，负责构建大数据平台。 - Vincenzo Lomonaco是博洛尼亚大学深度学习博士生，ContinuousAI开源项目创始人，致力于在持续学习和人工智能的背景下连接人们并重组资源，同时也是计算机科学与工程系的教学助理。 通过这个课程，您将为未来的技术挑战做好充分准备。
Are you looking to gain in-depth knowledge of machine learning and deep learning? If yes, then this Learning Path just right for you. 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. R is one of the leading technologies in the field of data science. Starting out at a basic level, this Learning Path will teach you how to develop and implement machine learning and deep learning algorithms using R in real-world scenarios. The Learning Path begins with covering some basic concepts of R to refresh your knowledge of R before we deep-dive into the advanced techniques. You will start with setting up the environment and then perform data ETL in R. You will then learn important machine learning topics, including data classification, regression, clustering, association rule mining, and dimensionality reduction. Next, you will understand the basics of deep learning and artificial neural networks and then move on to exploring topics such as ANNs, RNNs, and CNNs. Finally, you will learn about the applications of deep learning in various fields and understand the practical implementations of scalability, HPC, and feature engineering. By the end of the Learning Path, you will have a solid knowledge of all these algorithms and techniques and be able to implement them efficiently in your data science projects. Do not worry if this seems too far-fetched right now; we have combined the best works of the following esteemed authors to ensure that your learning journey is smooth: About the Authors Selva Prabhakaran is a data scientist with a large e-commerce organization. In his 7 years of experience in data science, he has tackled complex real-world data science problems and delivered production-grade solutions for top multinational companies. Yu-Wei, Chiu (David Chiu) is the founder of LargitData, a startup company that mainly focuses on providing Big Data and machine learning products. He has previously worked for Trend Micro as a software engineer, where he was responsible for building Big Data platforms for business intelligence and customer relationship management systems. In addition to being a startup entrepreneur and data scientist, he specializes in using Spark and Hadoop to process Big Data and apply data mining techniques for data analysis. Vincenzo Lomonaco is a deep learning PhD student at the University of Bologna and founder of ContinuousAI, an open source project aiming to connect people and reorganize resources in the context of continuous learning and AI. He is also the PhD students' representative at the Department of Computer Science of Engineering (DISI) and teaching assistant of the courses machine learning and computer architectures in the same department.
