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所在平台: Udemy |
课程主页: https://www.udemy.com/course/datascience_machine-learning-nlp-bigdata-spark-pyspark/
课程评论：没有评论
**课程名称：** DataScience_Machine Learning - NLP - BigData - Spark- PySpark **课程概述：** 本课程旨在帮助学员掌握使用 Spark (Hadoop) 进行数据科学和机器学习的专业知识。学员将学习 K-Means 聚类、决策树、随机森林和朴素贝叶斯等机器学习算法。课程内容涵盖统计学、时间序列、文本挖掘的实际应用，并介绍深度学习。学员将通过在媒体、医疗、社交媒体、航空和人力资源领域的实际用例来实践所学知识。 **课程大纲：** 1. **数据科学导论：** * 理解数据科学的定义、作用以及在分析大规模非结构化数据方面的应用。 * 探讨数据科学的时代背景、商业智能与数据科学的区别、数据科学生命周期以及常用工具。 * 介绍大数据和 Hadoop，以及 R 和 Spark 的基础知识。 * 初步了解机器学习。 2. **统计推断：** * 学习数据分析中常用的统计技术和术语。 * 掌握统计推断的概念，理解中心的度量、离散度、概率、正态分布和二项分布。 3. **数据提取、整理和探索：** * 学习如何从不同来源提取数据，将其整理成结构化格式。 * 掌握数据分析流程，了解数据提取的类型，区分原始数据和处理后数据。 * 学习数据整理（Data Wrangling）和探索性数据分析（EDA）的方法。 * 掌握数据可视化技术。 4. **机器学习导论：** * 理解机器学习的定义、应用场景和处理流程。 * 学习机器学习的分类。 * 实践监督学习算法，如线性回归和逻辑回归。
Data Science with Spark(Big Data - Hadoop) Training lets you gain expertise in Machine Learning Algorithms like K-MeansClustering, Decision Trees, Random Forest, and Naive Bayes using Spark(Big Data - Hadoop). Data Science Trainingencompasses a conceptual understanding of Statistics, Time Series, Text Mining and an introductionto Deep Learning. Throughout this Data Science Course, you will implement real-life use-cases onMedia, Healthcare, Social Media, Aviation and HR.urriculumIntroduction to Data ScienceLearning Objectives - Get an introduction to Data Science in this module and see how Data Sciencehelps to analyze large and unstructured data with different tools.Topics:What is Data Science? What does Data Science involve?Era of Data Science Business Intelligence vs Data ScienceLife cycle of Data Science Tools of Data ScienceIntroduction to Big Data and Hadoop Introduction to RIntroduction to Spark Introduction to Machine LearningStatistical InferenceLearning Objectives - In this module, you will learn about different statistical techniques andterminologies used in data analysis.Topics:What is Statistical Inference? Terminologies of StatisticsMeasures of Centers Measures of SpreadProbability Normal DistributionBinary DistributionData Extraction, Wrangling and ExplorationLearning Objectives - Discuss the different sources available to extract data, arrange the data instructured form, analyze the data, and represent the data in a graphical format.Topics:Data Analysis Pipeline What is Data ExtractionTypes of Data Raw and Processed DataData Wrangling Exploratory Data AnalysisVisualization of DataIntroduction to Machine LearningLearning Objectives - Get an introduction to Machine Learning as part of this module. You willdiscuss the various categories of Machine Learning and implement Supervised Learning Algorithms.Topics:What is Machine Learning? Machine Learning Use-CasesMachine Learning Process Flow Machine Learning CategoriesSupervised Learning algorithm: LinearRegression and Logistic Regression
