AI for Everyone

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.

Syllabus:

• 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
• 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
• 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
• 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
• 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
• 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
• and more