Mentor Based
Advanced Machine Learning
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Course Overview
Program Syllabus
Module 1
About This Course
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.
Module 2
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.
Module 3
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.
Lead Instructor
Dr. Sarah Chen
PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.
Limited SeatsClosing Soon
Advanced Machine Learning
Professional Certification Program
🎥
FormatLive + Recorded
📅
Duration8 Weeks
📜
CertificationVerified
Module 4
Introduction to Advanced Machine Learning:
- Overview of Advanced Machine Learning Techniques.
- Key Concepts and Terminologies.
- Applications and Use Cases.
Module 5
Ensemble Methods:
- Bagging and Boosting.
- Random Forests.
- Gradient Boosting Machines (GBM).
- XGBoost and LightGBM.
Module 6
Deep Reinforcement Learning:
- Fundamentals of Reinforcement Learning.
- Deep Q-Learning.
- Policy Gradients.
- Proximal Policy Optimization (PPO).
Module 7
Adversarial Training:
- Understanding Adversarial Examples.
- Adversarial Attack Techniques.
- Defensive Techniques against Adversarial Attacks.
- Robust Model Training.
Module 8
Transfer Learning:
- Concept and Importance of Transfer Learning.
- Pre-trained Models and Fine-tuning.
- Domain Adaptation.
- Case Studies and Applications.
Module 9
Practical Implementation:
- Advanced Python Programming Techniques.
- Using TensorFlow for Advanced Model Building.
- Implementing Deep Learning Models with PyTorch.
- Integrating Models into Real-world Applications.
Module 10
Who Should Enrol?
- Graduate students in Computer Science and related fields.
- Professionals in data science, machine learning, and AI development seeking to deepen their expertise.
Module 11
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.
Module 13
What You’ll Gain
- Full access to e-LMS
- Real-world dry lab projects
- 1:1 project guidance
- Publication opportunity
- Self-assessment & final exam
- e-Certificate & e-Marksheet
Module 14
Join Our Hall of Fame!
Take your research to the next level with NanoSchool.
Module 15
Publication Opportunity
Get published in a prestigious open-access journal.
Module 16
Centre of Excellence
Become part of an elite research community.
Module 17
Networking & Learning
Connect with global researchers and mentors.
Module 18
Global Recognition
Worth ₹20,000 / $1,000 in academic value.
Module 19
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