Data Analytics and Machine Learning for Big Data

本课程是 Microsoft Big Data Management and Analytics 专业证书的一部分

位教师： Microsoft

访问权限由 Coursera Learning Team 提供

5个模块

深入了解一个主题并学习基础知识。

3 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

5个模块

深入了解一个主题并学习基础知识。

3 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将获得的技能

Transfer Learning
Scalability
Unstructured Data
Model Deployment
Model Evaluation
Unsupervised Learning
PySpark
Deep Learning
Text Mining
Artificial Intelligence and Machine Learning (AI/ML)
Distributed Computing
Feature Engineering
Big Data
Natural Language Processing
Generative AI
Supervised Learning
PyTorch (Machine Learning Library)
Machine Learning
技能部分已折叠。显示 9 项技能，共 18 项。

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作业

46 任务¹

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授课语言：英语（English）

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本课程是 Microsoft Big Data Management and Analytics 专业证书专项课程的一部分

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该课程共有5个模块

This advanced course teaches machine learning and AI techniques for big data systems. Learners will build end-to-end ML pipelines with PySpark ML, implement supervised and unsupervised models, and apply NLP techniques at scale. The course also explores deep learning, distributed training, and integrating Generative AI into big data workflows.

By the end of this course, you will be able to: Implement ML pipelines using PySpark ML Build supervised, unsupervised, and recommendation models Apply NLP and text analytics to large datasets Integrate Generative AI and LLMs with big data systems Tools & Software: PySpark ML, PyTorch, TensorFlow, Azure Machine Learning, Azure OpenAI Service Skills: Machine learning, NLP, Deep learning, Generative AI, Model evaluation

Machine learning appears quite different when data exceeds the capacity of a single system. In this section, learners explore the foundational ideas behind machine learning in big data environments and how familiar approaches change at scale. You will examine supervised and unsupervised learning, regression and classification problems, and the practical challenges that arise with massive datasets—such as scalability, distributed computing, and the need to adapt algorithms for large-scale processing.

涵盖的内容

6个视频3篇阅读材料7个作业

6个视频总计29分钟

Machine Learning Transforms Big Data into Business Intelligence 4分钟
ML Problem Classification and Business Mapping 7分钟
Data Quality Drives ML Success at Scale 4分钟
Distributed Data Preparation Workflows 6分钟
Rigorous Evaluation Prevents ML Disasters at Scale 4分钟
Implementing Scalable Model Evaluation 5分钟

3篇阅读材料总计90分钟

Machine Learning Fundamentals for Big Data Environments 30分钟
Big Data ML Preparation Techniques 30分钟
ML Model Evaluation for Big Data Systems 30分钟

7个作业总计210分钟

ML Fundamentals for Big Data Mastery 30分钟
Machine Learning Problem Analysis 30分钟
ML Fundamentals for Big Data Assessment 30分钟
ML Data Preparation Pipeline 30分钟
Data Preparation for ML at Scale Assessment 30分钟
Scalable Model Evaluation 30分钟
Model Evaluation at Scale Assessment 30分钟

A practical foundation for building scalable machine learning solutions using PySpark ML in big data environments. The content focuses on designing and implementing end-to-end machine learning pipelines with transformers and estimators, while developing regression, classification, and clustering models that scale across distributed systems. Emphasis is placed on real-world implementation and informed platform selection for enterprise deployments using Azure Databricks, Microsoft Fabric, and Azure HDInsight, ensuring solutions are both technically robust and operationally viable at scale.

涵盖的内容

6个视频3篇阅读材料10个作业

6个视频总计36分钟

Democratizing Machine Learning at Enterprise Scale 4分钟
PySpark ML Pipeline Development Across Platforms 10分钟
Supervised Learning Success Stories in Enterprise Big Data 5分钟
Supervised Learning Model Development 6分钟
Recommendation Systems Drive Business Growth 4分钟
Building Scalable Recommendation Systems 8分钟

3篇阅读材料总计90分钟

PySpark ML Architecture and Platform Comparison 30分钟
Supervised Learning Algorithms for Big Data 30分钟
Unsupervised Learning and Recommendation Systems 30分钟

10个作业总计300分钟

PySpark ML Implementation Mastery 30分钟
ML Pipeline Component Development 30分钟
ML Platform Comparison and Pipeline Creation 30分钟
PySpark ML Platform Fundamentals Assessment 30分钟
Supervised Learning Implementation 30分钟
Supervised Learning Model Development 30分钟
Supervised Learning at Scale Assessment 30分钟
Recommendation System Implementation 30分钟
Recommendation System Development 30分钟
Unsupervised Learning and Recommendations Assessment 30分钟

Large-scale text analytics introduces the challenges and techniques required to process and analyze unstructured text at enterprise scale using distributed computing frameworks. The focus is on applying natural language processing (NLP) techniques in scalable architectures to support text classification, sentiment analysis, and entity and relationship extraction across massive text corpora. Emphasis is placed on practical, production-oriented approaches for handling high-volume text data, with integration of Azure Cognitive Services to enhance accuracy, scalability, and operational efficiency in real-world analytics solutions.

涵盖的内容

6个视频3篇阅读材料10个作业

6个视频总计39分钟

Unlocking Value from Unstructured Text at Scale 5分钟
Building Scalable Text Processing Pipelines 9分钟
Advanced NLP Drives Business Intelligence 5分钟
Implementing Advanced NLP at Scale 7分钟
Production-Scale Text Classification Transforms Business Operations 4分钟
Building Production Text Classification Systems 8分钟

3篇阅读材料总计90分钟

Distributed Text Processing Techniques 30分钟
Advanced NLP Techniques for Big Data 30分钟
Scalable Text Classification Architectures 30分钟

10个作业总计300分钟

Text Analytics and NLP Mastery 30分钟
Text Preprocessing Pipeline Development 30分钟
Scalable Text Preprocessing Design 30分钟
Text Processing at Scale Assessment 30分钟
Advanced NLP Implementation and Monitoring 30分钟
NLP System Architecture Design 30分钟
Advanced NLP Techniques Assessment 30分钟
Text Classification System Development 30分钟
Text Classification System Implementation 30分钟
Text Classification at Scale Assessment 30分钟

Deep Learning for Big Data introduces the fundamentals of deep learning and advanced architectures specifically adapted for big data environments. Students will learn to implement neural networks for big data applications, apply transfer learning techniques with pre-trained models, and scale deep learning training across distributed clusters using modern frameworks and optimization techniques.

涵盖的内容

6个视频3篇阅读材料10个作业

6个视频总计31分钟

Deep Learning Revolutionizes Big Data Analytics 5分钟
Neural Network Implementation in Big Data Frameworks 5分钟
Advanced Architectures Transform Complex Data Analysis 6分钟
CNN and RNN Implementation at Scale 5分钟
Distributed Deep Learning Enables Breakthrough Scale 4分钟
Implementing Distributed Deep Learning Training 5分钟

3篇阅读材料总计90分钟

Deep Learning Architectures for Big Data 30分钟
Advanced Deep Learning Architectures for Scale 30分钟
Distributed Deep Learning Training Strategies 30分钟

10个作业总计300分钟

Deep Learning for Big Data Mastery 30分钟
Neural Network Implementation 30分钟
Neural Network for Big Data Classification 30分钟
Deep Learning Fundamentals Assessment 30分钟
Advanced Architecture Implementation 30分钟
Deep Learning Architecture Design 30分钟
Advanced Deep Learning Architectures Assessment 30分钟
Distributed Training Implementation and Management 30分钟
Distributed Deep Learning Training 30分钟
Distributed Deep Learning Training Assessment 30分钟

Generative AI and Big Data Integration explores how generative AI transforms big data analytics by enabling intelligent, natural language–driven workflows at scale. You will learn how foundation models and large language models integrate with distributed data pipelines to automate insights, enhance analytics, and power modern data applications. Through hands-on labs, you will implement LLM integration, apply fine-tuning for domain-specific use cases, and design production-ready GenAI solutions for real-world big data scenarios.