Embark on a transformative journey with EdAthena's AI PGPX program. Tailor your education with a range of specializations and electives, complemented by real-world case studies to enhance practical learning. This program is divided into three phases, designed to provide comprehensive knowledge and skills in AI and ML.
The PGPX program at EdAthena is designed to equip you with the essential skills required to excel in the field of artificial intelligence and machine learning. Through comprehensive modules, hands-on projects, and real-world case studies, you will gain a deep understanding of Python programming, machine learning concepts, and advanced AI techniques. Each phase builds on the previous one, ensuring a seamless learning experience and a robust foundation in AI and ML.
Strategic AI Frameworks: Learn to develop and implement AI strategies within organizations, leveraging the latest technologies in data engineering and machine learning to drive business growth and innovation.
Ethical and Scalable Data Solutions: Gain insights into ethical considerations and best practices for managing large-scale data storage solutions, including NoSQL, SQL, and NewSQL databases.
Domain-Specific Expertise: Acquire specialized knowledge in applying AI to various sectors such as finance, healthcare, manufacturing, marketing, and retail, enhancing your ability to lead AI initiatives in diverse industries.
Advanced AI Techniques: Master advanced AI methodologies, including supervised and unsupervised learning, CNN reinforcement learning, and transfer learning, to optimize and innovate AI applications.
Real-World Application and Deployment: Focus on the practical deployment of AI models, continuous monitoring, and maintenance, ensuring that AI solutions remain effective and adaptive to changes in data and business needs.
Capstone Project: Undertake a comprehensive capstone project that allows you to apply your learning to a real-world problem, demonstrating your ability to strategize, implement, and manage AI solutions in a professional setting.
A. AI Fundamentals and Infrastructure
• Python Programming Fundamentals
• Overview of AI and Machine Learning
B. Data Engineering and Ethics
• AI in Data Engineering
• Data Storage Solutions: NoSQL, SQL, and NewSQL
C. Machine Learning Techniques
• Supervised and Unsupervised Learning
• CNN Reinforcement and Transfer Learning
A. AI in Financial and Healthcare Domains
• AI for Finance: Predictive Analytics and Algorithmic Trading
• AI for Healthcare: Medical Imaging and Diagnostics
B. AI in Manufacturing, Marketing, and Retail
• Predictive Maintenance and Demand Forecasting
• AI in Cybersecurity: Threat Detection and Response
A. AI Product Development and Deployment
• Developing AI-Driven Products and Services
• Deploying AI Models at Scale
B. AI Monitoring and Maintenance
• Continuous Monitoring of AI Models
• Handling Model Drift and Data Changes
C. Innovative AI Applications and Capstone Project
• Exploring Emerging AI Technologies
• Implementing an AI Solution to a Real-World Problem