Master Study AI

Big Data and AI Integration

data-science.

 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

 

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