AI for Everyone

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.


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