Online/ e-LMS
Mentor Based
Moderate
4 Weeks
About
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.
Program Structure
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.
Program Outcomes
- Master advanced machine learning techniques and concepts.
- Implement and optimize complex machine learning models.
- Gain hands-on experience with deep reinforcement learning and adversarial training.
- Utilize ensemble methods for improved model performance.
- Apply transfer learning to leverage pre-trained models.
- Develop practical skills with TensorFlow, PyTorch, and advanced Python programming.
- Complete real-world projects demonstrating advanced AI applications.
- Earn a certificate of completion recognized by industry leaders.
Fee Structure
Fee: INR 10,999 USD 164
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Key Takeaways
- Access to e-LMS
- Real Time Project for Dissertation
- Project Guidance
- Paper Publication Opportunity
- Self Assessment
- Final Examination
- e-Certification
- e-Marksheet
Future Career Prospects
- Senior Machine Learning Engineer
- AI Specialist
- Data Science Lead
- Research Scientist in AI
- AI Product Manager
- NLP Expert
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