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.

Data Analytics and Machine Learning for Big Data
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Data Analytics and Machine Learning for Big Data
This course is part of Microsoft Big Data Management and Analytics Professional Certificate

Instructor: Microsoft
Included with
Skills you'll gain
- Big Data
- Artificial Intelligence and Machine Learning (AI/ML)
- Feature Engineering
- Text Mining
- Scalability
- Unstructured Data
- Generative AI
- Model Evaluation
- Model Deployment
- PyTorch (Machine Learning Library)
- Transfer Learning
- Supervised Learning
- Large Language Modeling
- Natural Language Processing
- Distributed Computing
- PySpark
- Machine Learning
- Unsupervised Learning
- Deep Learning
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There are 5 modules in this course
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 datasetsāsuch as scalability, distributed computing, and the need to adapt algorithms for large-scale processing.
What's included
3 readings7 assignments
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.
What's included
3 readings10 assignments
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.
What's included
3 readings10 assignments
This module introduces deep learning fundamentals 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.
What's included
3 readings10 assignments
This module explores how generative AI transforms big data analytics by enabling intelligent, natural languageādriven 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.
What's included
3 readings9 assignments
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