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所在平台: Udemy |
课程主页: https://www.udemy.com/course/big-data-and-nlp-with-python-2-in-1/
课程评论：没有评论
**课程名称：Python大数据与自然语言处理 (NLP) 2合1** **课程概述：** 本课程是为有Python基础并希望进入数据科学领域的专业人士量身定制。您将深入学习数据科学的两大关键领域：大数据和自然语言处理（NLP），并熟练掌握Python语言及Apache Spark等最新大数据技术。 本课程是“2合1”模式，共包含两个核心部分： **第一部分：Python大数据实战** * **MongoDB入门与运用：** * 理解MongoDB的特性及其与SQL和结构化数据的区别。 * 学习设置MongoDB数据库和执行基本查询。 * 掌握使用PyMongo库在Python中与MongoDB交互，包括检索数据、构建复杂的聚合管道。 * 通过实际项目，构建使用PyMongo的数据处理管道。 * **Apache Spark大数据处理：** * 了解Spark作为分布式计算框架在处理大规模数据集中的关键作用。 * **综合实战项目：** * 将MongoDB和Spark结合，完成一项真实的数据科学工作流程，例如分析Reddit评论数据，并进行机器学习任务，预测评论的受欢迎程度。 **第二部分：下一代Python自然语言处理** * **NLP基础理论与应用：** * 理解NLP如何帮助从海量文本数据中提取有用信息。 * 学习使用最新的Python NLP库。 * **NLP实战项目：** * 构建一个垃圾短信检测器，解决实际的NLP问题。 * 学习将文本数据转换为数字表示（词向量化）以便进行分析。 * 掌握对新文档进行准确标记和评分，以及数据聚类的方法。 * **高级NLP技术（概念与方法）：** * 学习使用向量空间模型对文本进行建模。 * 了解语义解析，用于分解句子结构。 * 探索神经网络，并学习如何生成逼真的文本。 **学习目标：** 通过本课程的学习，您将能够： * 高效地摄取、查询和分析数据，尤其是在使用MongoDB和Spark时。 * 运用实用的NLP技术和方法分析文本数据。 * 熟练掌握Python在处理大数据和NLP任务中的应用。 * 独立完成数据科学项目，将所学知识付诸实践。 **讲师介绍：** Alexis Rutherford，麻省理工学院媒体实验室研究科学家，物理学博士。在联合国、Facebook等机构拥有近十年的Python数据分析和建模经验，在流行病学、族裔暴力、疫苗犹豫和宪法改革等领域解决过诸多复杂问题，并为社交媒体数据、法律文件和新闻文章构建过数据管道。他热衷于分享数据科学和数据隐私领域的见解，并经常在博客和社交媒体上发表文章。
Natural language processing and Big Data are the most interesting subfields of data science. You will learn to use the most popular programming language, Python with the latest Big Data technology, Apache Spark. If you're a data science professional who is familiar with Python and wants to take first steps in the world of data science by acquiring NLP and Big Data skills, then this learning path is for you. This comprehensive 2-in-1 course teaches you how to efficiently ingest, query, and analyze data using MongoDB and Spark. You will also learn practical NLP techniques and methods to analyze your text data. It's a perfect blend of concepts and practical examples which makes it easy to understand and implement. It follows a logical flow where you will be able to build on your understanding of the different Big Data and NLP techniques with every section. This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible. The first course, Working with Big Data in Python, starts off with explaining the use of MongoDB, how it differs from SQL and structured data, and setting up your first database and query. You will then learn how to make use of MongoDB and Python such as including the pyMongo library, retrieving results from MongoDB cursors, and building up complex aggregation pipelines using operators. You will also work on an example which builds a data pipeline using PyMongo. Next, you will be introduced to Spark as the main software framework for working with large datasets across distributed computing resources. Finally, you will explore another live example of a data science workflow using MongoDB and Spark which includes the analysis of Reddit comments and machine learning task to predict comment popularity. The second course, Next Generation Natural Language Processing with Python, begins with explaining how NLP can help you extract useful information from large collections of text data, and how you can use the latest Python libraries for NLP. You will then learn how to solve a practical problem using NLP by building a spam SMS detector. You will also learn to convert words into numbers that can be analyzed. Next, you will learn how to accurately label new documents to get an accuracy score and cluster your data together. You will be glanced through more advanced analysis wherein you will learn to model text by using vector space models and semantic parsing to break down the components of a sentence. Finally, you will work with neural networks and learn how to write believable text. By the end of this Learning Path, you'll be able to use the latest libraries of Big Data and NLP in Python for your day-to-day data science tasks. Meet Your Expert(s): We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth: Alexis Rutherford is a Research Scientist at MIT Media Lab. He has a PhD in Physics and nearly 10 years of experience of using Python for data analysis and modeling gained at the United Nations, Facebook, and elsewhere. He has tackled many problems using data analysis including epidemiology, ethnic violence, vaccine hesitancy, and constitutional change and has built pipelines for social media data, legal documents, and news articles among others. He blogs and tweets regularly on data science and data privacy.
