Build AI Tools: GPT Projects, RAG++ & Web App

Get an idea of what NLP is as well as the job and salary options. In addition, learn to differentiate between NLP, Data Science, Machine Learning & A.I.

Learn the basics of NLP, get an overview of the logical workflow - a big picture understanding. Starting with word embeddings, cosine similarity, and an introduction to attention—key concepts that lay the foundation for understanding transformers like GPT.

A warm welcome to the math behind GPT! This brief intro encourages beginners, reassuring them that we'll use easy-to-follow explanations, visuals, and hands-on examples to make the concepts accessible and enjoyable.

Get a high-level overview of how GPT works! Imagine GPT as an orchestra, with each math concept as an instrument. Learn how all components come together in harmony to generate meaningful language.

Understand multi-head attention, embeddings, and gradient descent with engaging analogies. Learn how each plays a unique role, like conductors, musical notes, and practice sessions, to bring GPT's symphony together.

Discover how word embeddings relate like friendships through cosine similarity, and how self-attention adds depth, deciding who to listen to in a conversation based on shared context.

Learn about high vs low attention in GPT using a sandwich analogy. Understand why some ingredients are key, while others receive less focus, depending on their importance in creating the final result.

Understand self-attention through an engaging library analogy. Learn how Query (Q), Key (K), and Value (V) work together like finding the best book in a library based on your question and book details.

Explore the scaling factor in self-attention using a classroom analogy. Learn how dividing the attention helps stabilize learning—like a teacher ensuring no student gets overwhelmed by too much information.

Understand how self-attention helps GPT decide word relevance using a story analogy. Learn how queries, keys, values, and Softmax work together to highlight key ideas, like deciding which parts of a sentence are crucial to continue a narrative.

Learn how GPT uses self-attention and probabilistic modeling to generate vivid next words, such as "sizzling grill" and "flavor." See how context influences the continuation of sentences in a natural, sensory-driven way.

Learn how to create and configure a new Colab notebook. This foundational step ensures you're ready to write and execute Python code, connect to the cloud environment, and start building your projects.

Learn how to open .IPYNB files in Google Colab and locate the provided course resource folders. This step ensures you have easy access to all necessary materials for seamless coding practice.

Learn how to customize your Google Colab environment by adjusting settings like dark vs. light mode and other preferences. Personalize Colab for comfort and efficiency during your coding sessions.

Learn how to install the OpenAI library and import essential Python libraries. This step is needed for setting up your environment to interact with GPT-4 and start building AI-powered projects effectively.

Learn how to obtain your OpenAI API key. This key is essential for authenticating and accessing GPT-4 services, enabling you to create and interact with AI-powered applications like the recipe generator.

Learn how to create a list of ingredients for the recipe generator project. This step will help you provide input to GPT-4, enabling the model to generate creative recipes based on your chosen ingredients.

Learn how to generate three random ingredients from your list. This step will provide diverse inputs for GPT-4, encouraging creative and unique recipe outputs as you interact with the OpenAI API.

Learn how to define a Python function to generate creative recipes using GPT-4. See how to prompt the AI like a helpful chef and use OpenAI's API to turn ingredients into delicious dishes.

Learn how to call the promptGPT function to generate a recipe from selected ingredients using GPT-4. See how to display the AI's creative output in an engaging way for your culinary adventure.

Overview of the interactive storytelling project with GPT-4. Discover how you’ll guide the main character, Elara through adventures, explore creative generation, and see how math concepts like Attention are applied in storytelling

Quickly set up your interactive storytelling project by installing required libraries and inserting your OpenAI API key in Google Colab. A simple guide to get you ready for adventure!"

Build a function that generates creative story content using GPT-4. We'll guide you through setting up AI roles, user prompts, and extracting story responses—all within Google Colab.

Kick off your interactive storytelling adventure! Learn how to set up an engaging story prompt, guide GPT-4 using system requirements, and watch as the AI continues your narrative creatively

Learn how to split a growing story into chapters, just like a series of episodes. Understand how GPT-4 maintains consistency and engagement by leveraging attention across these dynamic story segments

Discover how to split a long story into manageable chapters, like a Netflix series. Learn how GPT-4 keeps each chapter coherent and consistent using attention, creating an engaging, interactive narrative

Add interactivity to your story! Use Python to prompt users for input to decide Elara's next move—whether she goes left, right, or climbs a tree. Engage users and make your storytelling dynamic

See how GPT-4 uses user input to generate the next part of the interactive story. Whether Elara ventures into a dark cave or climbs a mysterious tree, watch how the story dynamically evolves

Learn how to add each newly generated part of the story as a new chapter, keeping the adventure organized and easy to follow. Keep track of Elara's journey chapter by chapter.

Decide if you want to keep the adventure going! In this step, you'll choose whether to continue Elara's journey, adding more excitement to the story. Join me and my co-author, Harley, for more fun!"

Add various user-driven actions like exploring, interacting with characters, or making bold decisions. This will make Elara's story more engaging and dynamic as you follow along

Watch how your choices influence Elara's journey. We'll review the generated story output based on the decisions made, showcasing the power of interactive storytelling with GPT-4.

Import and utilize essential libraries like Pandas, SpaCy, and Hugging Face to scrape, analyze, and visualize financial data using sentiment analysis and entity recognition.

Understand how to define a Python function using requests and BeautifulSoup to retrieve financial news headlines from Google, focusing on targeted company-specific queries.

Learn how to use the get_news function to retrieve and display financial news headlines for multiple companies, preparing the data for further sentiment analysis and risk assessment.

Understand how pre-trained models like DistilBERT are fine-tuned for sentiment analysis. Learn to use Hugging Face's sentiment analysis pipeline to evaluate financial news for risk assessment.

Apply Hugging Face's sentiment analysis pipeline with DistilBERT to evaluate financial news sentiment. Understand how fine-tuned models classify positive or negative sentiment for risk analysis.

Learn to convert sentiment analysis results into pandas DataFrames for better organization and easier visualization. Explore financial sentiment insights for multiple companies.

Learn how to use SpaCy's pre-trained model to perform Named Entity Recognition (NER). Identify companies, locations, and events in financial news to uncover valuable insights for decision-making.

Apply NER to financial news for Tesla, NIO, Rivian, and more. Convert results into readable DataFrames, enabling easy analysis of key entities like organizations and events.

Learn how to build a risk assessment function that combines sentiment analysis results and named entity recognition to calculate a company’s risk score. Understand the role of critical keywords and sentiment labels in determining risk.

Learn how to evaluate financial risk scores for multiple companies by combining sentiment analysis and named entity recognition data. Interpret the results to identify high-risk entities.

Set up your coding environment by importing essential libraries and tools. Learn to install and configure OpenAI for GPT-4, and prepare for integrating APIs like OpenWeatherMap and Google Places. ?

Learn to set up API keys for OpenWeatherMap, Google Places, and OpenAI. Understand their role in accessing weather, location, and GPT data while securely integrating them into your Colab project. ? ?

Learn to collect user inputs like destination, dates, activities, and budget using Python. These preferences will personalize your RAG++-powered travel itinerary for a unique user experience. ? ✈️

Learn to fetch real-time weather data using the OpenWeatherMap API, handle API responses, and extract meaningful insights like temperature and conditions to tailor user travel activities effectively. ? ?

Learn to fetch popular tourist spots using the Google Places API, handle activity-based searches, and extract key details like names, ratings, and addresses to enhance personalized travel plans. ?️ ?

This is where we put it all together. We use GPT to generate a natural language itinerary using the gathered data. Imagine GPT-4 is your creative writing buddy. You give it a prompt, and it comes up with a cool story idea.

Learn the basics of Python by understanding variables and lists. Discover how to store and organize data effectively for your programming projects.

Master the art of creating variables in Python. Learn how to assign, store, and manage data to bring your code to life.

Learn how to create and use lists in Python. Discover how to store multiple values, access them, and manipulate data efficiently.

Learn how to write if statements with multiple conditions in Python. Master combining logic to make your programs smarter and more efficient.

Learn the basics of functions in Python. Understand how to create reusable blocks of code to make your programming more efficient.

Dive deeper into Python functions. Learn how to use parameters and return values to create powerful and flexible code structures.

Understand key Python terminology. Learn the differences between scripts, modules, packages, and libraries to navigate Python programming with confidence.

Learn what a module is in Python and how it helps you organize and reuse code efficiently. Explore its role in building structured and scalable projects

Discover how to create your own Python module. Learn to structure your code for better reusability and maintainability in larger projects.