English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 29m | 203 MB
AI is changing the world around us, and completely revolutionizing the way we work. Data professionals now have access to tools that provide superpowers to work smarter and faster than ever before. In this course, the Maven Analytics team guides you through the world of deep learning and generative AI and shows you how to leverage Python and ChatGPT together to work more efficiently and make smarter, data-driven decisions.
Explore the rapid rise of large language models like ChatGPT and Google Gemini, and get up and running with free tools that will take your skills to the next level. You’ll also dive into the art of prompt engineering, review tips and best practices for generating consistent and accurate model outputs, and find out how to address common limitations and pitfalls to be aware of. From there, Chris Bruehl takes you through some of the most powerful and practical ChatGPT use cases for data science and analytics, with demos in Python.
Table of Contents
Introduction
1 The future is now Intro to AI for data analytics
2 Setting expectations
How AI Is Revolutionizing Data Analysis
3 Why AI is a game changer for data analysis
4 AI use cases for data analytics
A Crash Course in AI, LLMs, and ChatGPT
5 The AI landscape
6 Generative AI and large language models
7 The road to ChatGPT
8 Generative AI tools
9 Warning Pitfalls of ChatGPT
10 Accessing ChatGPT and Google Gemini
Prompt Engineering Tips
11 Intro to prompt engineering
12 Prompt tip Be clear and specific
13 Prompt tip Provide context
14 Prompt tip Establish roles
15 Prompt tip Set the tone
ChatGPT for Python
16 Intro to ChatGPT for Python
17 Explain Python code
18 Add comments to Python code
19 Optimize Python code
20 Troubleshoot Python errors
21 Generate Python code from scratch
22 Visualize data with Python
23 Research Python libraries
24 Scrape web data with Python
25 Interpret Python machine learning models
26 ChatGPT extension for Jupyter notebooks
Conclusion
27 Key takeaways and next steps
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