Introduction to Advanced Machine Learning
- Overview of sophisticated machine learning techniques.
- Core concepts and terminology.
- Practical applications and case studies.
Ensemble Methods
- Techniques such as Bagging and Boosting.
- Working with Random Forests.
- Utilizing Gradient Boosting Machines (GBM), XGBoost, and LightGBM.
Deep Reinforcement Learning
- Fundamentals of reinforcement learning.
- Implementing Deep Q-Learning.
- Understanding Policy Gradients and Proximal Policy Optimization (PPO).
Adversarial Training
- Exploration of adversarial examples.
- Techniques for adversarial attacks and defenses.
- Training robust models to withstand adversarial challenges.
Transfer Learning
- The significance of transfer learning in modern AI.
- Working with pre-trained models and fine-tuning them.
- Domain adaptation strategies and their applications.
Practical Implementation
- Advanced Python programming for machine learning.
- Building complex models with TensorFlow.
- Implementing deep learning models using PyTorch.
- Deploying models in real-world scenarios.
Eligibility Criteria
- Graduate students in Computer Science or related disciplines.
- Professionals in data science, machine learning, or AI development looking to expand their expertise.
Learning Outcomes
- Achieve expertise in advanced machine learning techniques.
- Design, implement, and optimize sophisticated machine learning models.
- Gain hands-on experience with deep reinforcement learning and adversarial training.
- Improve model performance through ensemble methods.
- Utilize transfer learning to enhance model development.
- Develop advanced Python programming skills and proficiency with TensorFlow and PyTorch.
- Complete practical projects that demonstrate advanced AI applications.
- Earn an industry-recognized certificate upon completion.