Python to AI Agent Mastery

Store and manipulate data using variables, strings, numbers, and booleans in Python.

Direct program execution using if statements, comparisons, and logical operators.

Repeat actions efficiently with for and while loops to process data and automate tasks.

Create reusable code blocks with functions, parameters, return values, and local scope.

Organize and access collections of data using lists for sequences and dictionaries for key-value pairs.

Anticipate and manage runtime issues using try-except blocks to write resilient code.

Apply core Python concepts to create an interactive command-line calculator that handles user input and basic operations.

Evaluate comparison and logical operators to form conditional expressions.

Write basic conditional blocks to execute code based on true conditions.

Implement alternative execution paths for false conditions.

Structure multiple sequential conditions for mutually exclusive cases.

Construct indefinite loops controlled by dynamic condition updates.

Iterate over sequences and ranges to process collections efficiently.

Build an interactive game combining conditionals, loops, and user input validation.

Use lists to store sequences and iterate through them with for loops to process data.

Create and access key-value pairs in dictionaries to map relationships and look up data efficiently.

Work with lists of dictionaries and dictionaries containing lists to model complex real-world data.

Apply sets to eliminate duplicates and perform membership tests for efficient data filtering.

Use tuples to represent fixed collections and understand when immutability improves data safety.

Write concise list comprehensions to transform and filter data in a single readable line.

Implement a contact manager using nested dictionaries and lists to store, search, and update person records.

Write reusable Python functions with parameters and return values to eliminate repetitive code.

Distinguish between local and global variable scope to prevent naming conflicts inside functions.

Implement functions with default parameter values and keyword arguments for flexible interfaces.

Organize related functions into importable Python modules with a clear public API.

Leverage essential standard library modules like `random`, `datetime`, and `json` for common programming tasks.

Document functions with docstrings and annotate signatures with type hints for maintainable agent code.

Build a reusable utility module containing helper functions for file I/O, data validation, and logging to support future AI agent projects.

Read text files using context managers to safely handle file resources and process content line by line.

Write and append data to text files using appropriate modes to persist program output and logs.

Implement exception handling for common file operation errors to build robust file processing routines.

Parse and write CSV files using the csv module to handle structured tabular data for AI datasets.

Serialize and deserialize JSON data to exchange configuration and structured data with AI APIs.

Construct and manipulate filesystem paths using pathlib for cross-platform compatibility in project workflows.

Create a reusable data loader module that reads, validates, and prepares local datasets for AI agent training tasks.

Identify common Python exception types and interpret traceback messages to locate the source of runtime errors.

Implement try/except structures to catch specific exceptions and prevent program crashes during unexpected input or failures.

Use else for exception-free execution paths and finally for guaranteed cleanup actions like closing files or network connections.

Raise built-in or custom exceptions with meaningful messages to signal invalid states in functions and validate inputs early.

Define domain-specific exception hierarchies to distinguish agent-related failures such as tool errors, planning loops, or memory corruption.

Configure the logging module to record exceptions with timestamps, severity levels, and context for debugging autonomous agent runs.

Build a fault-tolerant agent execution loop that catches tool failures, logs context, retries with backoff, and gracefully degrades instead of crashing.

Use pip to install, upgrade, and uninstall third-party packages while managing dependencies with requirements.txt files.

Create and activate isolated Python environments using venv to keep project dependencies separate and reproducible.

Send GET and POST requests with the requests library, handle headers, query parameters, and JSON payloads, and check response status codes.

Parse JSON responses into Python objects, serialize dictionaries to JSON, and validate data structures for API integration.

Load API keys and secrets from .env files using python-dotenv to keep credentials out of source code.

Perform concurrent API requests with aiohttp and asyncio to reduce latency when calling multiple endpoints.

Assemble a reusable toolkit module that wraps authenticated API calls, error handling, and rate limiting for an AI agent project.

Inspect request and response components using browser developer tools and Python's http.client to understand the client-server communication cycle.

Execute GET requests with query parameters, headers, and timeouts using the requests library and parse JSON responses into Python dictionaries.

Interact with a public REST API by performing CRUD operations (GET, POST, PUT, DELETE) and handling resource identifiers and status codes.

Implement API key, Bearer token, and Basic Auth authentication schemes to securely access protected endpoints.

Build resilient API clients that handle HTTP errors, network timeouts, and rate limits using retries with exponential backoff.

Refactor synchronous API calls to asynchronous patterns using aiohttp and asyncio to improve throughput for multiple concurrent requests.

Integrate a Large Language Model API (e.g., OpenAI) to build a command-line agent that maintains conversation context and streams responses.

Identify the core components of the Transformer model, including self-attention and feed-forward layers, to explain how LLMs process sequential data.

Implement Byte Pair Encoding (BPE) tokenization using the Hugging Face `tokenizers` library to prepare raw text for LLM input.

Contrast causal language modeling with masked language modeling by calculating loss functions on sample token sequences.

Load a pretrained Hugging Face model with `AutoModelForCausalLM` and generate text completions using greedy decoding and sampling strategies.

Design zero-shot and few-shot prompt templates for classification and generation tasks to steer model behavior without weight updates.

Apply Low-Rank Adaptation (LoRA) to a small open-source LLM using the PEFT library to adapt it for a specific instruction-following dataset.

Fine-tune a quantized LLM on a curated dataset using LoRA, evaluate outputs qualitatively against the base model, and save the adapter weights for agent integration.

Write precise prompts that guide AI responses effectively

Provide relevant background information to improve AI understanding

Define AI personas to shape tone and expertise in responses

Structure prompts to achieve desired output formats like JSON or lists

Improve prompts through testing and adjusting based on AI outputs

Include sample inputs and outputs to demonstrate expected behavior

Design and test a complete prompt system for a practical AI agent task

Compare ReAct, Plan-and-Execute, and Reflection patterns by running minimal reference implementations to identify their distinct control-flow signatures.

Implement the base Agent, Tool, and Memory interfaces that will underpin every subsequent build, enforcing a clean contract between reasoning and execution.

Construct a dynamic tool registry with automatic schema validation and error handling, then register a web-search and a calculator tool as proof-of-concept.

Build short-term conversation buffers and long-term vector-backed stores, then measure retrieval latency and relevance on a multi-turn dialogue.

Develop a pluggable planner that decomposes high-level goals into ordered tool calls, supporting both single-shot and iterative replanning strategies.

Assemble the orchestration loop that routes LLM decisions through the tool registry, updates memory, and enforces step budgets and safety guards.

Wire all components into a working Research Assistant agent that answers multi-hop questions by browsing, calculating, and synthesizing cited responses.

Define what an AI agent is and identify its core components in simple terms

Explain how agents exchange messages using basic protocols and implement a ping-pong message loop

Model a shared world state that multiple agents can observe and modify safely

Design two agents that coordinate to achieve a common goal through basic signaling

Implement rules for agents to detect and resolve conflicting actions in a shared space

Create agents that propose, accept, or reject offers to allocate limited resources

Build a small simulation where multiple agents collaborate to complete a task like gathering or cleaning