Elevate your career with EdAthena's Post Graduate Program Executive in AI Strategy (PGPX). This comprehensive program equips executives with the strategic insights and technical knowledge needed to drive AI-powered innovation within their organizations.
The AI & ML PGP 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.
Comprehensive Curriculum: The program covers essential topics in Python programming, machine learning, and advanced AI techniques, ensuring a robust foundation in AI and ML.
Hands-On Learning: Emphasizes practical learning through real-world case studies and hands-on projects, allowing students to apply theoretical knowledge to real-world scenarios.
Specializations and Electives: Offers a range of specializations and electives, enabling students to tailor their education to their specific interests and career goals.
A. Programming and Data Structures
• Python Programming Fundamentals
• Data Structures and Algorithms
B. Databases and Data Management
• SQL Queries and Database Management
• Exploratory Data Analysis and Visualization
C. Statistics and Probability
• Descriptive and Inferential Statistics
• Probability Distributions and Hypothesis Testing
A. Machine Learning Foundations and Workflow
• Machine Learning Concepts and Workflow
• Data Preprocessing and Feature Engineering
B. Supervised and Unsupervised Learning
• Model Evaluation, Optimization, and Advanced Techniques
• Hyperparameter Tuning and Model Optimization
A. Fundamentals of Neural Networks and Optimization
• Neural Networks Fundamentals
• Generative Models: Autoencoders and GANs
B. Convolutional and Recurrent Neural Networks
• CNN for Computer Vision
• RNN for Sequence Data
C. Advanced Techniques, Transfer Learning, and Deployment
• Transfer Learning and Fine-Tuning Pre-Trained Models
• Deploying Deep Learning Models in Real-World Applications