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Animation and VFX (Visual Effects) courses provide specialized training in creating visually compelling and technically sophisticated content for film, television, video games, and other media. These courses cover various aspects of animation and visual effects, from foundational principles to advanced techniques, using industry-standard software and tools
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Information Technologies
Advanced Information Technologies (IT) courses cover a wide range of topics, including cybersecurity, cloud computing, data science, artificial intelligence, and more. These courses are designed to provide students with the skills and knowledge required to excel in the ever-evolving field of IT.
Eduka Education Create an immersive and dynamic learning environment for ICSE and CBSE students from Class I to XII with a 3D animated and smart classroom setup. Utilize 3D animations to deliver engaging lessons that simplify complex concepts, and employ smart boards and digital resources for interactive teaching. Align the curriculum with ICSE and CBSE standards, integrating adaptive learning platforms and gamification for personalized and enjoyable learning experiences.
DIPLOMA IN ARTIFICIAL INTELLIGENCE
A Diploma in Artificial Intelligence (AI) aims to equip students with the knowledge and skills required to develop AI systems and applications. This program covers a wide range of topics including machine learning, deep learning, natural language processing, computer vision, and ethical considerations in AI. Below is a detailed outline of such a program, structured to be completed within one year.
Course Duration
- Total Duration: 1 year
- Modules: Each module can range from 4 to 8 weeks
- Total Hours: Approximately 600-800 hours
1. Introduction to Artificial Intelligence
- Duration: 2 weeks
- Topics Covered:
- Overview of AI
- History and evolution of AI
- Key concepts and terminology
- Applications of AI
2. Mathematics for AI
- Duration: 6 weeks
- Topics Covered:
- Linear algebra
- Probability and statistics
- Calculus
- Optimization techniques
3. Programming for AI
- Duration: 6 weeks
- Topics Covered:
- Introduction to Python
- Data structures and algorithms
- Libraries and frameworks for AI (NumPy, pandas, scikit-learn)
- Data preprocessing and manipulation
4. Machine Learning Fundamentals
- Duration: 8 weeks
- Topics Covered:
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Evaluation metrics and model selection
- Software/Tools:
- scikit-learn
- TensorFlow
- Keras
- PyTorch
5. Deep Learning
- Duration: 8 weeks
- Topics Covered:
- Neural networks
- Convolutional neural networks (CNNs)
- Recurrent neural networks (RNNs)
- Transfer learning and fine-tuning
- Software/Tools:
- TensorFlow
- Keras
- PyTorch
6. Natural Language Processing (NLP)
- Duration: 6 weeks
- Topics Covered:
- Text preprocessing
- Tokenization and embeddings
- Sentiment analysis and text classification
- Sequence-to-sequence models and transformers
- Software/Tools:
- NLTK
- spaCy
- Hugging Face Transformers
7. Computer Vision
- Duration: 6 weeks
- Topics Covered:
- Image processing and augmentation
- Object detection and segmentation
- Image classification and recognition
- Generative adversarial networks (GANs)
- Software/Tools:
- OpenCV
- TensorFlow
- Keras
8. AI Ethics and Society
- Duration: 4 weeks
- Topics Covered:
- Ethical considerations in AI
- Bias and fairness in AI systems
- Privacy and security
- AI regulations and policies
9. AI in Industry
- Duration: 4 weeks
- Topics Covered:
- AI applications in different industries (healthcare, finance, manufacturing, etc.)
- Case studies and real-world projects
- AI deployment and scalability
- Software/Tools:
- Various industry-specific AI tools and platforms
10. Project Management and Collaboration
- Duration: 4 weeks
- Topics Covered:
- Project planning and management
- Agile and Scrum methodologies
- Team collaboration tools
- Version control with Git
- Software/Tools:
- Jira
- Trello
- GitHub
11. Capstone Project
- Duration: 8 weeks
- Topics Covered:
- Conceptualizing and planning an AI project
- Developing the project from start to finish
- Model training and evaluation
- Presenting the final project
- Software/Tools:
- All previously used tools and software
Additional Tools and Software
- IDEs: Jupyter Notebook, PyCharm, VS Code
- Libraries: NumPy, pandas, scikit-learn, TensorFlow, Keras, PyTorch
- NLP Tools: NLTK, spaCy, Hugging Face Transformers
- Computer Vision Tools: OpenCV, TensorFlow, Keras
- Collaboration: Slack, Microsoft Teams
Conclusion
This Diploma in Artificial Intelligence is designed to provide students with a comprehensive education in AI, covering both theoretical concepts and practical applications. The course spans a range of topics from fundamental principles to advanced techniques, preparing students for careers in AI and machine learning. Students will gain hands-on experience with industry-standard tools and software, enabling them to build, evaluate, and deploy AI systems effectively.