Program Objectives:

• Understand and apply advanced machine learning techniques.
• Implement ensemble methods, deep reinforcement learning, and adversarial training.
• Utilize transfer learning for efficient model development.
• Gain proficiency in using TensorFlow and PyTorch for building advanced models.
• Develop practical skills through hands-on coding exercises and real-world projects.
• Optimize machine learning models for performance and robustness.
• Prepare for advanced roles in AI and machine learning through comprehensive training and practical applications.

What you will learn?

Introduction to Advanced Machine Learning:

• Overview of Advanced Machine Learning Techniques.
• Key Concepts and Terminologies.
• Applications and Use Cases.

Ensemble Methods:

• Bagging and Boosting.
• Random Forests.
• Gradient Boosting Machines (GBM).
• XGBoost and LightGBM.

Deep Reinforcement Learning:

• Fundamentals of Reinforcement Learning.
• Deep Q-Learning.
• Policy Gradients.
• Proximal Policy Optimization (PPO).

Adversarial Training:

• Understanding Adversarial Examples.
• Adversarial Attack Techniques.
• Defensive Techniques against Adversarial Attacks.
• Robust Model Training.

Transfer Learning:

• Concept and Importance of Transfer Learning.
• Pre-trained Models and Fine-tuning.
• Domain Adaptation.
• Case Studies and Applications.

Practical Implementation:

• Advanced Python Programming Techniques.
• Using TensorFlow for Advanced Model Building.
• Implementing Deep Learning Models with PyTorch.
• Integrating Models into Real-world Applications.