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Course Modules:
Module 1: Introduction to Big Data & AI Synergy
What is big data?
How AI benefits from large-scale datasets
Overview of data-driven vs. model-driven systems
Module 2: Big Data Architecture & Technologies
Understanding data lakes, data warehouses, and data pipelines
Overview of Hadoop ecosystem
Introduction to Apache Spark and its MLlib library
Module 3: Data Ingestion & Real-Time Streaming
Tools for ingestion: Apache Kafka, Flume, NiFi
Batch vs. stream processing
Real-time analytics use cases
Module 4: Distributed Data Processing with Spark
Working with RDDs and DataFrames
Spark SQL for big data querying
Machine learning at scale with Spark MLlib
Module 5: Data Preparation at Scale
Data cleaning and transformation with PySpark
Handling missing data, outliers, and encoding
Data pipelines and ETL optimization
Module 6: Machine Learning on Big Data
Scaling traditional ML algorithms
Clustering and classification on distributed data
Integrating Scikit-learn and Spark ML
Module 7: Deep Learning with Big Data
Using TensorFlow and PyTorch with large datasets
Distributed training on cloud platforms
GPU vs CPU training pipelines
Module 8: Cloud Platforms for Big Data & AI
AWS, Azure, and Google Cloud for data processing
Using Databricks, BigQuery, and SageMaker
Automating workflows with Airflow
Module 9: Data Governance, Security & Compliance
Managing access to large data environments
Ensuring AI model transparency and fairness
Compliance with GDPR, HIPAA, and enterprise policies
Module 10: Final Capstone Project
Choose from:
Building a scalable fraud detection system
Creating a recommendation engine for millions of users
Streaming real-time sentiment analysis using Kafka and Spark
Present end-to-end pipeline with results and documentation
🧠Master Study NLP Fundamentals: The Foundation of Language Understanding in AI
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