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Artificial Intelligence Advanced Program
Artificial Intelligence
Deep learning, NLP, computer vision and deploying AI models.
An advanced programme for people who already write Python and know machine learning basics. You'll build neural networks, work with language and image models, and deploy a model to production, finishing with an industry-level capstone.
What you'll learn
- Fundamentals of artificial intelligence
- Advanced Python for AI
- Deep neural network architecture
- Convolutional neural networks (CNN)
- Recurrent neural networks (RNN) and LSTM
- Natural language processing (NLP)
- Computer vision and image processing
- Generative AI and GANs
- Transformer models (BERT, GPT)
- Reinforcement learning
- AI ethics and responsible AI
- Model deployment and production
Curriculum
10 modules
Weeks 1–2AI Foundations
- History and evolution of AI
- Types of AI: narrow, general, super
- Machine learning vs deep learning vs AI
- AI applications across industries
- Python setup for AI development
Weeks 3–6Deep Learning Fundamentals
- Neural networks from scratch
- Activation functions and optimization
- Backpropagation and gradient descent
- TensorFlow and PyTorch
- Keras for rapid prototyping
- Regularization techniques
- Batch normalization and dropout
Weeks 7–10Convolutional Neural Networks
- CNN architecture and components
- Image classification projects
- Transfer learning (VGG, ResNet, Inception)
- Object detection (YOLO, SSD, R-CNN)
- Image segmentation
- Facial recognition systems
Weeks 11–13Recurrent Neural Networks
- RNN architecture and use cases
- Long short-term memory (LSTM)
- Gated recurrent units (GRU)
- Sequence-to-sequence models
- Time series prediction
- Text generation
Weeks 14–17Natural Language Processing
- Text preprocessing and tokenization
- Word embeddings (Word2Vec, GloVe)
- Sentiment analysis
- Named entity recognition (NER)
- Transformer architecture
- BERT and GPT models
- Question answering systems
- Chatbot development
Weeks 18–20Advanced Computer Vision
- Image processing with OpenCV
- Advanced object detection
- Pose estimation
- Video analysis
- Medical image analysis
- Autonomous vehicle vision
Weeks 21–22Generative AI
- Generative adversarial networks (GANs)
- Variational autoencoders (VAE)
- Style transfer
- Image generation
- Deepfake technology
- AI art and creative applications
Week 23Reinforcement Learning
- RL fundamentals
- Q-learning and deep Q-networks
- Policy gradient methods
- Game-playing AI
- Real-world RL applications
Week 24AI Deployment and MLOps
- Model optimization
- Flask/FastAPI for model serving
- Docker containerization
- Cloud deployment (AWS/Azure)
- Model monitoring
- CI/CD for ML models
Weeks 25–26Capstone Projects
- Industry-level AI project
- End-to-end implementation
- Portfolio development
- Final presentation
Prerequisites
- Strong Python programming skills
- Understanding of machine learning basics
- Linear algebra and calculus fundamentals
- A data science foundation is recommended
Career paths
- AI Engineer
- Deep Learning Specialist
- Computer Vision Engineer
- NLP Engineer
- Research Scientist
- AI Architect
- MLOps Engineer
Tools and frameworks
- Python, NumPy, Pandas
- TensorFlow and Keras
- PyTorch
- OpenCV
- Hugging Face Transformers
- NLTK, spaCy
- Docker, Kubernetes
- AWS SageMaker, Google Colab
Portfolio projects
- Image classification system
- Object detection application
- Chatbot with NLP
- Sentiment analysis tool
- Face recognition system
- Recommendation engine
- Deployed deep learning model