6 Days of Prompt Engineering, Generative AI and Data Science
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Welcome to the6 Days of Prompt Engineering, Generative AI, and Data Science Course
Get hands-on with Prompt Engineering, Generative AI, and Data Science in just five 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 10 hours and 40 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:
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Prompt Engineering Fundamentals: Understand transformers, attention mechanisms, and how to structure prompts for optimal performance.
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Generative AI Workflows: Master tools like Google Colab, Jupyter Notebook, LM Studio, and learn how to fine-tune system messages and model parameters.
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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).
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Machine Learning with XGBoost & Random Forest: Explore advanced ML topics, including parameter tuning, SHAP values, and real-world approaches to customer satisfaction modeling.
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AI Agents with CrewAI: Dive into the next wave of AI automation (coming in Q1 2025).
COURSE BREAKDOWN
Introduction
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Meet your instructor, download course materials, set up your environment (Google Colab, Jupyter Notebook, RStudio).
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Preview the core projects we’ll tackle.
Day 1 – Basics of Prompt Engineering
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Learn about transformers, attention, and chain-of-thought prompting.
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Experiment with LM Studio to practice explicit instructions, one-shot, and few-shot techniques.
Day 2 – System Messages & LLM Parameters
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Tokenization, system messages, and parameter tuning.
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Break the system message (on purpose) to see how LLMs respond, then learn how to guide them back.
Section 4 & 5: Days 3 & 4 – Coming in Q1 2025
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Day 3: Prompt Engineering for better reasoning.
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Day 4: Cyber Security–focused prompts.
Day 5 – OpenAI API for Text Generation
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Integrate the OpenAI API in Python.
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Adjust temperature, handle few-shot learning, and refine your text generation workflow.
Day 6 – CAPSTONE PROJECT: OpenAI API
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Build a “Rock-Paper-Scissors” AI.
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Create new strategies, test temperature parameters, and see how GPT adapts.
Days 7 & 8 – Coming in Q1 2025
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Day 7: Explore OpenAI API for Images.
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Day 8: Random Forest for customer satisfaction.
Day 9 – XGBoost
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Discover XGBoost in both Python and R.
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Handle data processing, parameter tuning, cross-validation, and SHAP values for model interpretation.
Day 10 – AI Agents with CrewAI
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Coming in Q1 2025—learn to build AI agents that automate tasks and collaborate efficiently.
WHY ENROLL NOW?
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Lifetime Updates: You get all future course modules automatically, including advanced sections scheduled for 2025.
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Practical Projects: Apply what you learn in real-world scenarios (Rock-Paper-Scissors AI, XGBoost for customer satisfaction).
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Structured Curriculum: Each day is designed to build on the previous one, speeding up your learning and progress.
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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!
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1Introduction to 5 Projects in Prompt Engineering, Generative AI and Data ScienceVídeo Aula
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2[ACTION] Download the Course MaterialsTexto
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3About me, DiogoVídeo Aula
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4Unlimited Updates 2025Vídeo Aula
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5Unlimited Updates FormTexto
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6Setting Up Google ColabVídeo Aula
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7Setting Up Jupyter NotebookVídeo Aula
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8Installing R and RStudioVídeo Aula
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9Game Plan for Basics of Prompt EngineeringVídeo Aula
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10Understanding TransformersVídeo Aula
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11Attention Mechanisms in NLPVídeo Aula
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12Prompt Engineering TechniquesVídeo Aula
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13Setting Up the LM StudioVídeo Aula
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14LM Studio - Explicit Instructions and One-ShotVídeo Aula
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15LM Studio - Few-ShotVídeo Aula
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16LLMs are Few-Shot LearnersVídeo Aula
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17LM Studio - Chain of ThoughtsVídeo Aula
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18Scientific Research on Chain of ThoughtsVídeo Aula
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19Key Learnings and Outcomes: Prompt Engineering BasicsVídeo Aula
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20Game Plan for Prompt Engineering with System Message and LLM ParametersVídeo Aula
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21TokenizationVídeo Aula
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22OpenAI TokenizerVídeo Aula
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23Rock-Paper-Scissors, Dices and StrawberriesVídeo Aula
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24System MessageVídeo Aula
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25LM Studio - System MessageVídeo Aula
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26CHALLENGE: Breaking the System Message Part 1Texto
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27LM Studio - Breaking the System Message Part 1Vídeo Aula
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28CHALLENGE - Breaking the System Message Part 2Texto
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29LM Studio - Breaking the System Message Part 2Vídeo Aula
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30CHALLENGE - Breaking the System Message Part 3Texto
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31Understanding Generation Model ParametersVídeo Aula
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32LM Studio - ParametersVídeo Aula
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33Key Learnings and Outcomes: System Message and LLM ParametersVídeo Aula
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34Your feedback is valuableTexto
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35Game Plan for Reasoning and HallucinationsVídeo Aula
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36Meta PromptingVídeo Aula
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37Analogical Reasoning PromptingVídeo Aula
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38Rephrase and RespondVídeo Aula
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39According-to PromptingVídeo Aula
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40Multi-Persona CollaborationVídeo Aula
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41Emotion PromptingVídeo Aula
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42Key Learnings and Outcomes: Reasoning and Hallucinations for LLMsVídeo Aula
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44Game Plan for OpenAI APIVídeo Aula
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45OpenAI API for TextVídeo Aula
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46Python - Setting Up OpenAI API KeyVídeo Aula
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47Python - OpenAI API SetupVídeo Aula
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48Python - Generating Text with OpenAI APIVídeo Aula
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49Python - OpenAI API ParametersVídeo Aula
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50Python - OpenAI API with Few-ShotVídeo Aula
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51Key Learning and Outcomes: OpenAI API for TextVídeo Aula
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52Project Introduction: Can GPT play rock paper scissors?Vídeo Aula
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53Python - OpenAI API SetupVídeo Aula
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54Python - Random Playing StrategyVídeo Aula
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55Python - Iteratively Improving the System PromptVídeo Aula
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56Python - Testing Temperature ParametersVídeo Aula
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57Python - New Strategy: Change if DefeatVídeo Aula
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58Python - Building Functions to Play GameVídeo Aula
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59Python - New Strategy: The AnalystVídeo Aula
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60Python - Testing StrategiesVídeo Aula
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