Udemy

10 Days: Prompt Engineering, Generative AI and Data Science

立即報名
  • 3,209 名學生
  • 更新於 11/2025
4.5
(402 個評分)
CTgoodjobs 嚴選優質課程,為職場人士提升競爭力。透過本站連結購買Udemy課程,本站將獲得推廣佣金,有助未來提供更多實用進修課程資訊給讀者。

課程資料

報名日期
全年招生
課程級別
學習模式
修業期
14 小時 33 分鐘
教學語言
英語
授課導師
Diogo Alves de Resende
評分
4.5
(402 個評分)
3次瀏覽

課程簡介

10 Days: Prompt Engineering, Generative AI and Data Science

All-in-One Prompt Engineering, Generative AI & Data Science Bootcamp: Build a Portfolio with GenAI & AI Agents Projects

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!

課程章節

  • 12 個章節
  • 147 堂課
  • 第 1 章 Introduction
  • 第 2 章 Day 1 - Basics of Prompt Engineering
  • 第 3 章 Day 2 - Prompt Engineering with System Message and LLM Parameters
  • 第 4 章 Day 3 - Prompt Engineering for Better Reasoning LLMs
  • 第 5 章 Day 4 - Reasoning LLMs
  • 第 6 章 Day 5 - OpenAI API for Text Generation
  • 第 7 章 Day 6 - CAPSTONE PROJECT: OpenAI API
  • 第 8 章 Day 7 - OpenAI API for Images
  • 第 9 章 Day 8 - Random Forest for Customer Satisfaction
  • 第 10 章 Day 9 - XGBoost
  • 第 11 章 Day 10 - AI Agents with CrewAI
  • 第 12 章 What's Next?

課程內容

  • Design Powerful Prompts for large language models to boost AI accuracy., Master Generative AI Techniques to create text, images, and more with minimal effort., Optimize System Messages for better control and consistency in AI-driven projects., Leverage Data Science Tools like Python, R, and XGBoost for deeper insights., Integrate OpenAI APIs into real-world applications for text generation and analysis., Fine-Tune Model Parameters to maximize performance and personalize AI outputs., Automate Common Tasks with AI, saving time and resources in business workflows., Apply Prompt Engineering to solve cybersecurity and compliance challenges., Develop Hands-On Projects that showcase AI capabilities to stakeholders., Build AI Agents using CrewAI for advanced task automation (coming soon).


評價

  • A
    Arun Kannan
    5.0

    good for the new learners.

  • D
    DUSTIN SMITH
    1.0

    This guy is not much better at prompt engineering than a standard jr high student. This course is not worth the time

  • B
    Bindhu Madhavi Kandikonda
    5.0

    good

  • r
    rodney dwy
    3.0

    direction for installing collabratory not ideal. where is this python folder located i downloaded the the heros file but no single python file i have XGBoost - Python but is that the correct file to put into colobaratory?

立即關注瀏覽更多

本網站使用Cookies來改善您的瀏覽體驗,請確定您同意及接受我們的私隱政策使用條款才繼續瀏覽。

我已閱讀及同意