Generative AI and NLP in Python Practice Exam

Generative AI and NLP in Python Practice Exam

About the Generative AI and NLP in Python Exam

The Generative AI and NLP in Python  Exam focuses on evaluating the knowledge and skills required to work with Generative AI and Natural Language Processing (NLP) using Python. It tests the ability to build, train, and implement AI models that can generate text, process and analyze natural language, and use NLP techniques for a range of applications.

Knowledge Evaluated

The exam covers foundational concepts in NLP, including tokenization, part-of-speech tagging, named entity recognition, and sentiment analysis. It also includes more advanced topics like language modeling, text generation, and transformer models such as GPT and BERT.

• The exam will assess practical skills in utilizing Python libraries and frameworks like NLTK, spaCy, TensorFlow, and Hugging Face for NLP and generative tasks.
• Candidates will be expected to demonstrate the ability to process large text datasets, build language models, and generate meaningful text outputs based on different NLP tasks.
• This exam is ideal for developers, data scientists, and AI professionals who want to deepen their understanding of NLP and generative AI techniques in Python.

Skills Required

• A strong foundation in Python programming, including the ability to write efficient, readable code and utilize Python libraries effectively.
• Understanding text preprocessing techniques such as tokenization, stemming, and lemmatization.
• Familiarity with part-of-speech tagging, named entity recognition (NER), and sentiment analysis.
• Knowledge of text vectorization methods such as Bag-of-Words, TF-IDF, and word embeddings (Word2Vec, GloVe).
• Understanding of transformers and contextual embeddings such as BERT and GPT.
• Understanding of language models, including both traditional models (e.g., n-grams) and modern deep learning models (e.g., RNNs, LSTMs, transformers).
• Ability to build, train, and fine-tune generative models like GPT-2/3, BERT, and other transformer-based models.
• Proficiency in libraries such as TensorFlow, PyTorch, and Hugging Face Transformers for building and deploying NLP models.
• Knowledge of training and evaluation metrics for language models.
• Experience with manipulating and preprocessing large text datasets using libraries like Pandas and Numpy.
• Ability to handle real-world, unstructured text data and prepare it for model training.
• Knowledge of evaluating NLP models using metrics like perplexity, BLEU score, and ROUGE score.
• Understanding model performance improvement techniques, including hyperparameter tuning and fine-tuning.
• Building real-world NLP applications such as chatbots, text summarization systems, language translation, and text generation.
• Familiarity with deploying NLP models into production using cloud platforms or APIs.

Who should take the Exam?

The exam is ideal for:

• Data Scientists and Machine Learning Engineers
• AI Researchers and Practitioners
• Software Engineers Interested in AI
• Students and Recent Graduates in AI/ML Fields
• AI Enthusiasts and Innovators
• Tech Entrepreneurs and Product Managers

Course Outline

The Generative AI and NLP in Python Exam covers the following topics - 

Domain 1 - Course Introduction

• Course Scope Overview
• Instructor Introduction
• How to Navigate the Course
• Accessing Course Materials (Coding)
• System Setup Guide (101)
• System Setup for Coding

Domain 2 - Introduction to Natural Language Processing (NLP)

• NLP Overview
• Basics of Word Embeddings
• Sentiment Analysis: One-Hot Encoding (OHE) Introduction
• Sentiment OHE Implementation (Coding)
• Word Embeddings with Neural Networks (NN)
• GloVe for Word Embedding (Coding)
• GloVe: Identifying Closest Words (Coding)
• GloVe: Word Analogy (Coding)
• GloVe: Word Clustering (101)
• GloVe Word Implementation (Coding)
• Sentiment Analysis with Embedding (101)
• Sentiment Analysis with Embedding (Coding)
• Introduction to Transformers (101)

Domain 3 - Applying Huggingface for Pre-Trained Models

• Huggingface Overview (101)
• Using Pipelines for General Text Classification (101)
• Pipelines for Text Classification (Coding)
• Named Entity Recognition (NER) Overview (101)
• Named Entity Recognition (NER) (Coding)
• Question Answering System (101)
• Question Answering (Coding)
• Text Summarization (101)
• Text Summarization (Coding)
• Translation with Huggingface (101)
• Translation (Coding)
• Fill-Mask Method (101)
• Fill-Mask (Coding)
• Zero-Shot Text Classification (101)
• Zero-Shot Text Classification (Coding)

Domain 4 - Model Fine-Tuning

• Overview of Fine-Tuning (101)
• Exploratory Data Analysis (Coding)
• Simple Model Development (Coding)
• Fine-Tuning Models (101)
• Huggingface Trainer for Model Training (101)
• Fine-Tuning Model (Coding)
• Saving and Loading Models with Huggingface (Coding)

Domain 5 - Vector Databases

• Vector Databases Overview (101)
• Tokenization Basics (101)
• Practical Tokenization (Coding)
• Building a Bible Vector Database (Full Picture)
• Data Preparation for Bible Vector DB (Coding)
• Database Management for Bible Vector DB (Coding)
• Exercises: Movies Vector Database (Coding)
• Data Prep for Movies Vector DB (Coding)
• Setup and Query Function for Movies Vector DB (Coding)
• Multimodal Vector Databases Overview (101)
• Multimodal Vector DB Setup (Coding)
• Querying Multimodal Vector DB (Coding)

Domain 6 - Working with OpenAI API

• OpenAI API Introduction (101)
• Obtaining Your API Key (Coding)
• Working with Python Package for OpenAI (101)
• Implementing Python Package for OpenAI (Coding)
• REST APIs and OpenAI WebUI (Coding)
• Understanding API Costs (101)

Domain 7 - Prompt Engineering

• Introduction to Prompt Engineering (101)
• Crafting Clear Instructions (Coding)
• Using Personas in Prompts (Coding)
• Defining Delimiters in Prompts (Coding)
• Dividing Tasks into Sub-Tasks (Coding)
• Providing Examples in Prompts (Coding)
• Controlling Output in Prompts (Coding)

Domain 8 - Advanced Prompt Engineering

• Advanced Prompt Techniques (101)
• Few-Shot Prompting (101)
• Chain-of-Thought Process (101)
• Example of Chain-of-Thought (Coding)
• Self-Consistency in Chain-of-Thought (101)
• Self-Consistency Example (Coding)
• Prompt Chaining (101)
• Example of Prompt Chaining (Coding)
• Reflection Techniques (101)
• Tree-of-Thought Methodology (101)
• Self-Feedback and Critique (101)
• Self-Critique Techniques (Coding)

Domain 9 - Retrieval-Augmented Generation (RAG)

• Introduction to RAG (101)
• RAG Coding: Final Results (Coding)
• Handling Vector Databases in RAG (Coding)
• Managing Large Language Models (LLMs) in RAG (Coding)
• Putting RAG Concepts Together (Coding)

Domain 10 - Capstone Project: Chatbot Development

• Overview of Climate Change Chatbot Webapp (101)
• Data Preparation for Chatbot (Coding)
• Implementing Vector Database for Chatbot (Coding)
• Integrating RAG in the Chatbot (Coding)
• Building the Final Chatbot Web Application (Coding)

Domain 11 - Open Source Large Language Models (LLMs)

• Open Source LLMs Overview (101)
• Implementing Open Source LLMs (Coding)

Domain 12 - Data Augmentation

• Introduction to Data Augmentation (101)
• Back-Translation Method (Coding)
• Synonym Replacement for Data Augmentation (Coding)
• Random Cropping for Data Augmentation (Coding)
• Contextual Augmentation Techniques (Coding)
• Word Embedding for Data Augmentation (Coding)
• Using Fill-Mask for Data Augmentation (Coding)