| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Advanced |
| Duration | 10 Weeks |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹14999 / $175 |
| Tools | Python Scikit-learn TensorFlow Keras Pandas NumPy Matplotlib XGBoost |
About the Advanced AI and Machine Learning for Professionals Course
The Advanced AI and Machine Learning for Professionals course is designed for individuals in AI and data science roles looking to deepen their expertise. Over 10 weeks, participants will explore advanced topics such as deep learning, reinforcement learning, and computer vision.
This hands-on program helps learners apply cutting-edge AI techniques to solve complex real-world problems. By the end of the course, you will be equipped to handle sophisticated AI tasks and implement advanced models in your projects.
Program Highlights
• Comprehensive coverage of Advanced AI and Machine Learning for Professionals from fundamentals to advanced applications
• Hands-on projects and real-world case studies in Machine Learning
• Expert-curated curriculum aligned with current industry standards
• Access to recorded lectures and e-LMS platform for flexible, self-paced learning
• e-Certification and e-Marksheet upon successful completion
• Dedicated mentor support and interactive doubt-clearing sessions
• Exposure to industry-standard tools and platforms used in Machine Learning
• Career-oriented training for academic and professional growth in Machine Learning
Course Curriculum
Module 1: Advanced Machine Learning Techniques (3 Weeks)
- Overview of advanced algorithms
- Ensemble methods: boosting, bagging, stacking
- Dimensionality reduction (PCA, LDA)
- Time series forecasting models
Module 2: Deep Learning Specialization (3 Weeks)
- Deep learning basics: neural networks, activation functions
- Architectures: CNNs, RNNs
- Hyperparameter tuning and optimization
- Transfer learning with pre-trained models
Module 3: Reinforcement Learning (2 Weeks)
- Introduction to reinforcement learning (RL)
- Markov decision processes (MDPs), policies, and rewards
- Deep Q-networks (DQN) and policy gradients
- Applications: robotics, gaming, autonomous systems
Module 4: Computer Vision and Image Processing (2 Weeks)
- Fundamentals of computer vision
- Feature extraction and object detection
- Working with OpenCV and deep learning
- Image segmentation, face recognition
Tools, Techniques, or Platforms Covered
Python Scikit-learn TensorFlow Keras Pandas NumPy Matplotlib XGBoost
Real-World Applications
- Apply Advanced AI and Machine Learning for Professionals skills directly to academic research, thesis work, and publications
- Build a professional portfolio showcasing practical Machine Learning competencies
- Solve industry-relevant problems using Advanced AI and Machine Learning for Professionals methodologies and tools
- Contribute to open-source projects and collaborative research in Machine Learning
- Prepare for competitive examinations, interviews, and professional certifications in Machine Learning
Who Should Attend & Prerequisites
- Students pursuing degrees in Machine Learning, science, engineering, or related disciplines
- Working professionals seeking to upskill or transition into Machine Learning roles
- Researchers and academicians looking to adopt modern techniques in Machine Learning
- Entrepreneurs, freelancers, and self-learners interested in practical Machine Learning knowledge
Certification
