10 Days: Prompt Engineering, Generative AI and Data Science

About 10 Days: Prompt Engineering, Generative AI and Data Science

Welcome to the 10 Days of Prompt Engineering, Generative AI, and Data Science Course

Get hands-on with Prompt Engineering, Generative AI, and Data Science in just 10 days. I’m Diogo, and I’ve structured this course to take you from basics to advanced topics quickly. We’ll cover live sessions, hands-on labs, and real-world projects—all in

14 hours and 30 minutes

of published video content. You’ll also receive lifetime updates so your learning never goes stale.

You will build a portfolio of project on topics like:

Prompt Engineering Fundamentals:

Understand transformers, attention mechanisms, and how to structure prompts for optimal performance.

Generative AI Workflows:

Master tools like

Google Colab

,

Jupyter Notebook

,

LM Studio

, and learn how to fine-tune system messages and model parameters.

OpenAI API for Text & Images:

Integrate the OpenAI API into Python projects, explore parameters for better text generation, and tap into image generation (coming soon).

Machine Learning with XGBoost & Random Forest:

Explore advanced ML topics, including parameter tuning, SHAP values, and real-world approaches to customer satisfaction modeling.

AI Agents with CrewAI:

Dive into the next wave of AI automation (coming in Q1 2025).

COURSE BREAKDOWN

Introduction

Meet your instructor, download course materials, set up your environment (Google Colab, Jupyter Notebook, RStudio).

Preview the core projects we’ll tackle.

Day 1 – Basics of Prompt Engineering

Learn about transformers, attention, and chain-of-thought prompting.

Experiment with

LM Studio

to practice explicit instructions, one-shot, and few-shot techniques.

Day 2 – System Messages & LLM Parameters

Tokenization, system messages, and parameter tuning.

Break the system message (on purpose) to see how LLMs respond, then learn how to guide them back.

Days 3 - Prompt Engineering for better reasoning

Proven ways to improve the reasoning in LLMs.

Overcoming LLM Hallucinations

Day 4 –Reasoning LLMs - Coming in Q1 2025

How Reasoning Works in LLMs

Prompt Injection for LLMs like the O1.

A hot take on whether LLMs can reason or not.

Day 5 – OpenAI API for Text Generation

Integrate the OpenAI API in Python.

Adjust temperature, handle few-shot learning, and refine your text generation workflow.

Day 6 – CAPSTONE PROJECT: OpenAI API

Build a “Rock-Paper-Scissors” AI.

Create new strategies, test temperature parameters, and see how GPT adapts.

Days 7 - OpenAI API for Images

Fee images via links and encoded to the Multimodal LLM

Add Web-browsing capabilities to the LLM

Day 8 – Random Forest for Customer Satisfaction

End-to-end project on gathering actionable insights on customer satisfaction.

Guide on how to build a great chart.

Day 9 – XGBoost

Discover XGBoost in both

Python

and

R

.

Handle data processing, parameter tuning, cross-validation, and SHAP values for model interpretation.

Day 10 – AI Agents with CrewAI

Coming in Q2 2025—learn to build AI agents that automate tasks and collaborate efficiently.

WHY ENROLL NOW?

Lifetime Updates:

You get all future course modules automatically, including advanced sections scheduled for 2025.

Practical Projects:

Apply what you learn in real-world scenarios (Rock-Paper-Scissors AI, XGBoost for customer satisfaction).

Structured Curriculum:

Each day is designed to build on the previous one, speeding up your learning and progress.

Community & Feedback:

Engage in discussions, get direct feedback, and influence new content updates.

Ready to accelerate your Prompt Engineering, Generative AI, and Data Science skills?

Sign up now and gain immediate access to all published content, including the future modules. Let’s start building the future of AI together!