What do you Learn?
Acquaintance Centro Overview
This advanced 6-month AI training course with a focus on prompt engineering provides a comprehensive curriculum to equip students with the knowledge and skills required to excel in AI development, particularly in the context of NLP and responsible AI. It includes hands-on projects, ethical considerations, and advanced topics to ensure students are well-prepared for the evolving AI landscape.
01.
Introduction to AI and Prompt Engineering
1.1. AI Fundamentals
- Understanding AI, ML, and Deep Learning
- Python Programming for AI
- Introduction to Jupyter Notebooks
- Installing and Setting Up Deep Learning Frameworks (TensorFlow, PyTorch)
1.2. Natural Language Processing (NLP) Basics
- Tokenization and Text Preprocessing
- NLP Libraries (NLTK, spaCy)
- Word Embeddings (Word2Vec, GloVe)
1.3. Prompt Engineering Basics
- Introduction to Prompt Engineering
- Designing Effective Prompts for AI Systems
- Data Collection and Annotation for Prompt Engineering
02.
Advanced NLP Techniques
2.1. Sequence-to-Sequence Models
- Recurrent Neural Networks (RNNs)
- Long Short-Term Memory (LSTM) Networks
- Encoder-Decoder Architectures
2.2. Transformer Models
- Attention Mechanisms
- Introduction to Transformers
- Pretrained Language Models (BERT, GPT)
2.3. Fine-Tuning Pre-trained Models
- Transfer Learning for NLP Tasks
- Fine-Tuning BERT and GPT Models
- Hands-on Projects: Sentiment Analysis, Text Generation
03.
Prompt Engineering for NLP Tasks
3.1. Prompt Engineering for Text Classification
- Building Prompt Templates
- Fine-Tuning Language Models for Classification Tasks
- Bias Mitigation in Text Classification
3.2. Prompt Engineering for Text Generation
- Generating Specific Text Outputs
- Controlling Language Model Creativity
- Hands-on Projects: Custom Text Generation
04.
Advanced Machine Learning Techniques
4.1. Reinforcement Learning (RL)
- Introduction to Reinforcement Learning
- RL Algorithms (Q-Learning, DDPG)
- Applications in AI
4.2. Generative Adversarial Networks (GANs)
- Understanding GANs
- Training and Generating with GANs
- Applications in Image and Text Generation
05.
Ethics and Responsible AI
5.1. Bias and Fairness in AI
- Bias in AI Models
- Fairness Metrics
- Mitigating Bias in Prompt Engineering
5.2. Ethical AI and AI Safety
- Ethical Considerations in AI
- AI Safety Principles
- Case Studies: Ethical AI Failures
06.
Capstone Projects and Advanced Topics
6.1. Capstone Project Development
- Acquaintees work on real-world AI projects with prompt engineering elements
- Mentoring and Guidance from Specialized Instructors
6.2. Advanced Topics in Prompt Engineering
- Tokenization and Text Preprocessing
- NLP Libraries (NLTK, spaCy)
- Word Embeddings (Word2Vec, GloVe)
6.3. Final Projects Presentation and Evaluation
- Acquaintees present and evaluate their capstone projects
- Feedback and Assessment
6.4. Course Conclusion and Future Directions
- Review of Key Concepts
- Future Directions in AI and Prompt Engineering
- Experience Certification
- Placement Training and Interview grooming