Advanced Machine Learning
Master the Future of AI with Advanced Machine Learning Techniques.
Skills you will gain:
The Advanced Machine Learning course is designed for those who wish to deepen their understanding of sophisticated machine learning techniques and their real-world applications. This course covers advanced topics such as ensemble methods, deep reinforcement learning, adversarial training, and transfer learning. Participants will gain hands-on experience with advanced Python programming and popular frameworks like TensorFlow and PyTorch. By the end of the course, learners will be proficient in building and deploying state-of-the-art machine learning models, ready to tackle complex AI challenges.
Aim: To equip advanced learners and professionals with the expertise to implement, optimize, and deploy complex machine learning models, preparing them for cutting-edge challenges in the AI industry.
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.
Intended For :
- Graduate students in Computer Science and related fields.
- Professionals in data science, machine learning, and AI development seeking to deepen their expertise.
Career Supporting Skills