How I Built an AI Agent That Automates My Daily Tasks

It seems like you've provided an extensive outline or summary of a comprehensive guide on the prerequisites and basics of building AI applications, particularly focusing on Python libraries, machine learning (ML), deep learning, natural language processing (NLP), and the foundational concepts behind large language models (LLMs) such as ChatGPT. Here's a concise breakdown of key points from your provided text:

1. Python Libraries

• Definition: Pre-built collections of functions, classes, and constants that abstract away complex algorithms.
• Why Use Them?:
  • Save time by leveraging pre-written code.
  • Benefit from optimized, tested implementations.
• Key Libraries:
  • numpy: For numerical operations.
  • pandas: For data manipulation and analysis.

2. Machine Learning (ML)

• Core Idea: Find a mathematical function that fits the given data to make predictions.
• Example: Predicting salary based on experience using linear regression.

3. Deep Learning

• Difference from ML:
  • Works with unstructured data like images, audio, and video.
  • Uses neural networks at its core.

• Real-world Examples:

Read the full article at Towards AI - Medium

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