| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Beginner |
| Duration | 8 Weeks |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹10749 / $145 |
| Tools | Python R TensorFlow PyTorch scikit-learn NLTK spaCy OpenCV Jupyter Notebook |
About the Basics of AI Course
Unveiling the Foundations of Artificial Intelligence — this comprehensive 8-week program provides a panoramic overview of Artificial Intelligence, exploring its key principles, applications, and methodologies. Participants will gain deep insights into core AI concepts including machine learning, neural networks, natural language processing, and computer vision.
Designed specifically for beginners, the program emphasizes practical understanding through hands-on exercises, real-world case studies, and industrial datasets. Whether you're looking to launch a career in AI or simply understand the technology shaping our future, this program builds the solid foundation you need to thrive in the AI-driven world.
Program Highlights
• Comprehensive coverage of Basics of AI from fundamentals to advanced applications
• Hands-on projects and real-world case studies in AI
• 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
• Practical experience with tools: Python, R, TensorFlow, PyTorch
• Career-oriented training for academic and professional growth in AI
Course Curriculum
Module 1: Introduction to Artificial Intelligence
- Explore the definition, history, and evolution of AI alongside key concepts of intelligence and automation
- Analyze the philosophical and ethical implications of AI development across different eras
- Navigate the AI ecosystem including popular programming languages (Python, R) and frameworks (TensorFlow, PyTorch)
Module 2: Machine Learning
- Master supervised learning techniques including regression, classification, and evaluation metrics
- Apply unsupervised learning algorithms such as K-means clustering and PCA dimensionality reduction
- Implement reinforcement learning concepts including reward systems and Deep Q-Learning
Module 3: Deep Learning
- Build neural networks from scratch understanding activation functions, loss functions, and backpropagation
- Architect advanced deep learning models including CNNs, RNNs, and Generative Adversarial Networks
- Optimize model performance through hyperparameter tuning and regularization techniques
Module 4: Natural Language Processing
- Process and represent text data using N-grams, Bag of Words, and TF-IDF vectorization
- Develop core NLP applications including sentiment analysis, named entity recognition, and machine translation
- Leverage transformer architectures like BERT and GPT for modern language understanding tasks
Module 5: Computer Vision
- Process images and videos using fundamental image processing and object detection techniques
- Deploy advanced vision architectures like U-Net and Mask R-CNN for image segmentation
- Solve real-world problems in healthcare diagnostics, automotive safety, and security surveillance
Module 6: AI in Practice
- Implement AI solutions across healthcare, finance, retail, robotics, and smart city applications
- Investigate emerging trends in AI research and scientific discovery methodologies
- Evaluate responsible AI frameworks, ethical implications, and regulatory compliance requirements
Module 7: Advanced Topics in AI
- Design explainable AI models that provide transparency and build user trust
- Apply federated learning techniques for privacy-preserving decentralized machine learning
- Contribute to AI for social good initiatives in environmental sustainability and public health
Tools, Techniques, or Platforms Covered
Python R TensorFlow PyTorch scikit-learn NLTK spaCy OpenCV Jupyter Notebook
Real-World Applications
- Apply Basics of AI skills directly to academic research, thesis work, and publications
- Build a professional portfolio showcasing practical AI competencies
- Solve industry-relevant problems using Basics of AI methodologies and tools
- Contribute to open-source projects and collaborative research in AI
- Prepare for competitive examinations, interviews, and professional certifications in AI
Who Should Attend & Prerequisites
- Industry-recognized e-Certification + e-Marksheet from NSTC
- Hands-on training with practical projects and industrial datasets
- Dedicated expert mentorship and doubt resolution
Certification
