Day 1:
Introduction to Big Data and AI
Overview of Big Data: Characteristics, Types, and Sources
The Importance of Big Data in the Modern Business Landscape
Introduction to Artificial Intelligence: Key Concepts and Technologies
Relationship Between Big Data and AI: How They Work Together
Day 2:
Big Data Technologies and Tools
Data Collection and Storage Techniques for Big Data
Data Processing Frameworks: Hadoop, Spark, and Other Big Data Tools
Data Quality and Cleansing: Ensuring Accurate Insights
Introduction to Data Warehousing and Cloud-Based Solutions
Day 3:
Machine Learning and Deep Learning
Overview of Machine Learning: Types and Algorithms
Supervised vs. Unsupervised Learning: Key Differences and Applications
Introduction to Deep Learning and Neural Networks
Hands-On: Building a Simple Machine Learning Model
Day 4:
Advanced AI Techniques and Applications
Natural Language Processing (NLP) and Computer Vision
Reinforcement Learning and Its Applications
Case Studies: Successful AI Implementations in Various Industries
Hands-On: Training a Deep Learning Model for Image Recognition
Day 5:
Ethical Considerations and Practical Applications
Ethical Issues in Big Data and AI: Privacy, Bias, and Accountability
Best Practices for Data Security and Compliance with Regulations
Implementing AI Solutions in Business: Challenges and Strategies
Final Project: Solving a Real-World Problem Using Big Data and AI
