| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Advanced |
| Duration | 6 Weeks |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹5499 / $82 |
| Tools | Python Scikit-learn TensorFlow Keras Pandas NumPy Matplotlib XGBoost |
About the Machine Learning and AI Fundamentals Course
The Machine Learning and AI Fundamentals course offers a comprehensive introduction to the core principles of machine learning and artificial intelligence.
Designed for AI professionals, this course covers supervised and unsupervised learning techniques, neural networks, deep learning, and natural language processing. Through a blend of engaging video lectures, interactive coding sessions, and real-world projects, participants will gain hands-on experience and practical skills, preparing them to excel in the AI industry.
Program Highlights
• Comprehensive coverage of Machine Learning and AI Fundamentals 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: Introduction to Machine Learning and AI Fundamentals
- Overview and historical evolution of Machine Learning and AI Fundamentals
- Key terminology, definitions, and core concepts in Machine Learning
- Current industry landscape, trends, and career opportunities
- Setting up the learning environment and essential tools
Module 2: Fundamentals and Theoretical Foundations
- Core principles and scientific/theoretical underpinnings of Machine Learning and AI Fundamentals
- Mathematical and analytical frameworks relevant to Machine Learning
- Comparative analysis of major approaches and methodologies
- Understanding key standards, guidelines, and best practices
Module 3: Supervised Learning
- Introduction to Supervised Learning concepts and methodologies
- Step-by-step practical implementation of Supervised Learning techniques
- Tools and platforms commonly used for Supervised Learning
- Troubleshooting, optimization, and best practices
Module 4: Unsupervised Learning
- Introduction to Unsupervised Learning concepts and methodologies
- Step-by-step practical implementation of Unsupervised Learning techniques
- Tools and platforms commonly used for Unsupervised Learning
- Troubleshooting, optimization, and best practices
Module 5: Feature Engineering
- Introduction to Feature Engineering concepts and methodologies
- Step-by-step practical implementation of Feature Engineering techniques
- Tools and platforms commonly used for Feature Engineering
- Troubleshooting, optimization, and best practices
Module 6: Advanced Topics and Emerging Trends in Machine Learning
- Cutting-edge research and innovations in Machine Learning and AI Fundamentals
- Integration with AI, automation, and modern technologies
- Industry case studies and real-world problem solving
- Future directions and career pathways in Machine Learning
Module 7: Capstone Project and Assessment
- End-to-end project implementation using Machine Learning and AI Fundamentals skills
- Peer review, collaborative exercises, and expert feedback
- Portfolio-ready project documentation and presentation
- Final assessment and course completion evaluation
Tools, Techniques, or Platforms Covered
Python Scikit-learn TensorFlow Keras Pandas NumPy Matplotlib XGBoost
Real-World Applications
- Apply Machine Learning and AI Fundamentals skills directly to academic research, thesis work, and publications
- Build a professional portfolio showcasing practical Machine Learning competencies
- Solve industry-relevant problems using Machine Learning and AI Fundamentals 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
