Description
Introduction of Artificial Intelligence for Big Data:
“AI for Big Data: Leveraging AI Techniques for Large-Scale Data” is a specialized course designed to explore how artificial intelligence (AI) techniques can be applied to manage, analyze, and extract insights from large-scale data sets. This course covers the integration of AI and big data technologies, focusing on practical applications and advanced methods for handling massive amounts of data. Participants will learn about data processing frameworks, scalable AI algorithms, and real-world use cases of AI in big data environments. The course combines theoretical knowledge with hands-on exercises, preparing learners to deploy AI solutions that can efficiently process and analyze large-scale data.
Prerequisites:
- Proficiency in Python programming.
- Basic understanding of machine learning and AI concepts.
- Familiarity with big data technologies (e.g., Hadoop, Spark) is beneficial.
- Experience with data processing and analysis techniques.
Table of Contents:
- Introduction to AI and Big Data
1.1 Overview of Big Data and Its Challenges
1.2 Role of AI in Big Data Analytics
1.3 Key Concepts and Technologies in Big Data and AI - Big Data Technologies and Frameworks
2.1 Introduction to Big Data Frameworks: Hadoop, Spark
2.2 Data Storage Solutions: HDFS, NoSQL Databases(Ref: NoSQL-CouchDB Training)
2.3 Data Processing Techniques: MapReduce, Spark SQL - Data Preprocessing and Management
3.1 Data Cleaning and Transformation
3.2 Handling Missing and Inconsistent Data
3.3 Data Integration and Aggregation Techniques - AI Techniques for Large-Scale Data
4.1 Scalable Machine Learning Algorithms: Distributed Training and Inference
4.2 Implementing ML Models with Spark MLlib and TensorFlowOnSpark
4.3 Advanced AI Techniques: Deep Learning for Big Data - Real-Time Data Processing and Analytics
5.1 Streaming Data and Real-Time Analytics
5.2 Tools and Frameworks for Real-Time Processing: Apache Kafka, Apache Flink
5.3 Implementing Real-Time AI Solutions - Big Data and AI Use Cases
6.1 Case Studies: AI Applications in Finance, Healthcare, Retail
6.2 Analyzing Large-Scale Data for Predictive Analytics and Decision Making
6.3 Examples of AI-Driven Insights and Innovations - Model Deployment and Scalability
7.1 Deploying AI Models in Big Data Environments
7.2 Scalable Model Serving and Inference
7.3 Performance Optimization and Resource Management - Ethical and Practical Considerations
8.1 Ethical Issues in AI and Big Data Analytics
8.2 Ensuring Data Privacy and Security
8.3 Addressing Bias and Fairness in AI Models - Hands-on Projects
9.1 Project 1: Implementing a Scalable ML Model using Spark
9.2 Project 2: Building a Real-Time Data Pipeline with Kafka and AI Integration
9.3 Project 3: Analyzing Large-Scale Data for Predictive Insights with TensorFlow - Future Trends and Innovations
10.1 Emerging Trends in AI and Big Data Technologies
10.2 Innovations in Scalable AI Solutions
10.3 Preparing for the Future of Big Data Analytics and AI - Conclusion and Resources
11.1 Recap of Key Concepts and Techniques
11.2 Resources for Continued Learning and Professional Development
11.3 Next Steps for Advancing AI Capabilities in Big Data
Reviews
There are no reviews yet.