The Complete Generative AI and Python Programming Bootcamp

Explore Python installation options, from online IDEs like Google Colab to local IDLE, PyCharm, VSCode, and Jupyter Notebook, and learn how to run Python code across environments.

Run Python code in Google Colab, a free, no-setup cloud notebook with code cells and markdown. Explore Colab as a hosted alternative to Jupyter Notebook and share notebooks.

Install Python on Windows via python.org, add Python to path, and verify with the command prompt. Practice best practices and stay updated on the latest version as you start coding.

Install and configure PyCharm community edition on Windows, explore its code editor and debugging tools, and learn essential shortcuts, updates, and git integration for Python projects.

Create and run Python scripts in PyCharm, using virtual environments, .py files, and built-in run and execute selection features. Customize shortcuts and explore development with PyCharm for Python projects.

Master Python variables as labeled memory boxes that store data for calculations, printing, and debugging; learn multiple assignments, best practices, and indentation to avoid errors.

Master Python naming conventions from pep8, using snake_case, valid characters, and avoiding reserved words, built-ins, and leading underscores, and learn that constants use uppercase by convention.

Learn python comments with hash, including single line, inline, and block styles; avoid triple quotes for comments, explain why, use docstrings. Apply best practices with ctrl+/ or cmd+/.

Explore Python's built-in data types, including integers, floats, booleans, strings, lists, tuples, sets, dictionaries, and None, with notes on mutability and how syntax defines each type.

Explore static versus dynamic typing in Python, compare compile-time safety with runtime flexibility, and learn how type annotations offer safeguards without sacrificing Python’s flexibility.

Explore Python's basic arithmetic operators—addition, subtraction, multiplication, division (float results), floor division, exponentiation, and modulus—along with operator precedence and readability tips using underscores.

Explore assignment, comparison, and identity operators in Python. Understand mutability versus immutability and how memory addresses shown by id reveal when is and is not refer to the same object.

Master Python strings by declaring with single or double quotes, capturing user input, printing, slicing, combining, converting data types, and applying string methods, escape sequences, and multi-line strings.

Learn how to gather user input in Python using the input function, understand data types and convert strings to int or float for calculations, and build interactive programs.

Learn to work with Python strings through indexing, slicing, concatenation, and repetition; note strings are immutable, and use len and negative indices to reverse with a negative step.

Explore how Python string methods operate on string objects, including upper and lower case conversion, non-destructive behavior, strip, replace, and count, plus a case-insensitive count using lower.

Master split and join for tokenizing and recombining text, then apply remove prefix and remove suffix (Python 3.9+), with safe, non-destructive string handling for URLs and filenames.

Learn to format strings with f-strings in Python, embedding model name and version and expressions like token cost, and format four decimal places for clean, dynamic outputs.

Discover how python 3.8 f-strings with equals aid debugging by showing the variable name and its value, and use format specifiers to display results with three decimals.

Explore lists in Python, a versatile data structure, including creation, indexing (zero-based and negative), mutability, append, len, and nested lists for matrices.

Learn to concatenate and extend lists in Python with plus, plus equals, and extend, differentiate append from extend, and master slicing with start, end, and step for data manipulation.

Inspect list operations through iteration and for loops, using in and not in to check elements; compare copying versus referencing, and avoid modifying lists while iterating.

Explore list methods in Python, including append, extend, insert, clear, pop, and remove, and learn how to remove all occurrences with a loop for data science and generative AI projects.

discover how to use list methods count, reverse, sort, and sorted to add, remove, and organize data in Python, with in-place and built-in options for ascending or descending order.

Master list comprehension in Python to create, transform, and filter lists quickly and readably, with examples like doubling values and formatting names, for cleaner AI-ready code.

Explore Python tuples, an immutable, ordered data type, and learn creation, indexing, unpacking, and nested structures for reliable, memory-efficient data and data integrity.

Master tuple operations in Python by concatenating and repeating tuples, slicing, counting, iterating, and sorting, while noting immutability, memory efficiency, and avoiding unnecessary list conversions.

Discover how Python sets provide an unordered collection of unique immutable elements. Create sets with curly braces, convert lists or tuples, and explore frozen sets for immutability.

Explore how sets enforce uniqueness to prevent duplicates in synthetic data and identifiers like user IDs, IP addresses, and emails, and how immutable elements are stored, added, removed, and iterated.

Explore Python dictionaries, handling key-value pairs, immutable keys, and safe access with get, while organizing nested configs like hyperparameters for model pipelines.

Explore python dictionary operations, including copying versus assignment, using copy, update, and clear, and test keys, values, and items with in for efficient data management.

Use pop to remove a key and return its value, with optional default to avoid KeyError. Use popitem to remove the last inserted pair, and del to delete a key.

Learn to use dictionary keys as sets to find common keys between configurations, and iterate over keys, values, and items with f-strings to organize data in Python.

Explore Python 3.9's dictionary merge and update operators to combine dictionaries with cleaner syntax. Merging creates a new dictionary, while updating the left-hand dictionary in place.

Master set and dictionary comprehensions in Python to transform and filter data, practice with zip to pair lists, and apply conditional logic for practical data modeling.

Connect Python fundamentals into a solid foundation. Use OpenAI, Landgraff, and Linkchain to build a real world research agent with functions, object oriented programming, data structures, and APIs.

Explore conditional statements and indentation in Python, using if, elif, and else to control flow and define code blocks with consistent four-space indentation.

Explore Python booleans, including statements versus expressions, and boolean variables used in decisions. Learn booleans as integers, truthiness, and how these concepts simplify conditional checks.

Explore Python's logical operators and, or, not, and learn how boolean expressions govern conditions for discounts and prompts. Practice using and or not with variables, parentheses, and short circuit evaluation.

Explore Python's short circuit evaluation with and, or, and not. Learn how boolean values and truthiness guide decision making and optimize program flow.

Explore Python for loops, iterating over iterables to automate tasks, filter streams like tweets, analyze feedback, and monitor sensor data for threshold alerts in real-world apps.

Harness the range function to drive for loops and create sequences, such as assigning participant IDs and iterating over numbers; apply to data analysis and scalable tasks.

Master while loops by keeping code running until a condition stops it, illustrated with a temperature monitor that increments and avoids infinite loops in real time.

Explore Python loop control with continue and pass, using for and while loops to skip iterations, validate inputs, and structure code with placeholders for future logic, including break.

Explore the break statement to exit for and while loops early, improving efficiency as soon as a condition is met, and understand its interaction with the else clause.

Master debugging by stepping through code line by line with breakpoints, the debugger, and watches in PyCharm to understand program flow, evaluate expressions, and inspect variables.

Learn to define reusable Python functions with def, call them, and improve code modularity. Explore docstrings, triple quotes, Pep 257 guidelines, and how to access documentation with help and __doc__.

Learn how Python function arguments work, mastering positional and keyword arguments, their order rules, readability benefits, and safer mappings between parameters and values.

Master default arguments in Python functions to create optional parameters and flexible calls with positional and keyword arguments, while learning the non-default parameter follows default parameter rule.

Learn how positional-only parameters in Python 3.8+ use a forward slash to enforce argument order, with examples of positional-only, positional-or-keyword, and keyword-only parameters, and why this improves clarity and performance.

Explore variable length arguments using *args to create flexible functions that handle any number of inputs, such as a base cost mandatory plus additional item costs or joining hashtags.

Use *args and **kwargs to accept any number of positional or keyword arguments, enabling dynamic data like user info or configurations.

Explore Python dictionary packing and unpacking using keyword arguments, enabling flexible configurations, merging defaults with experiments, and passing dicts as function parameters for readable, maintainable code.

Explore how Python uses built-in, global, and local namespaces to manage names, prevent conflicts, and scope variables across functions and modules.

Master global and local scopes in Python, avoiding common pitfalls like shadowing; learn to use the global keyword to modify globals and prevent shadowing built-in names.

Explore lambda expressions as anonymous, one-line functions for quick calculations and data transformations, and sort data with lambdas as key functions, including defaults.

Master reading and writing text files in Python with the built-in open function, manage file objects, and handle text vs binary modes, utf-8 encoding, and closing files.

Understand absolute and relative file paths in Python, including root paths, relative notation (./ and ../), backslashes, and raw strings.

Learn how to read files in Python using the read method with a size, and manage the cursor with tell and seek to read specific segments or the entire file.

Discover how the Python with statement uses a context manager to open files and automatically close them, preventing errors from operations on closed files.

Learn how to read a file into a Python list using multiple methods—read and split lines, readlines, list constructor, and iterating over the file—while handling newline characters.

write text files in python with write mode to create or overwrite. append mode adds lines; use r+ for read and write, seek for insertion, and newlines.

Learn to process text files in Python by reading a colon-delimited devices file into a list of lists, excluding the header, to access IP addresses, usernames, and passwords for automation.

Learn to read and write csv files in Python using the built-in csv module, handling headers, iterating rows, and converting values for data analysis in AI and data-driven projects.

Analyze daily AI model usage by processing a CSV of tokens generated with Python, build a token data dictionary, and identify the day with the highest model usage.

Learn how to write csv files in Python using the csv writer, including headers and rows with accuracy, response time, and token counts, with delimiter and newline considerations.

Learn how to work with JSON in Python, sending and receiving structured data via APIs, using key-value pairs, double quotes for validity, and optional pretty printing for readable responses.

Parse JSON from a public API with Python requests and print the first post title. Handle nested data and errors, and note JSON's role in AI workflows.

Learn to call the OpenAI API using Python, handle JSON payloads, securely manage API keys with environment variables, and extract the model's reply from a chat completion response.

Master regular expressions in Python to search, match, and manipulate text, with patterns, quantifiers, and hashtag extraction; see how regex boosts text processing for generative AI and large language models.

Explore anchors and grouping in regular expressions to achieve precise matching and data extraction, using caret and dollar anchors, capturing groups, and patterns for commands and user mentions.

Master range expressions by using square brackets to define character classes, matching letters, digits, underscores, and negated sets with practical examples like username validation.

Master non-greedy quantifiers in regular expressions to capture the smallest match, using patterns like <.*?> to extract the first HTML tag and distinguish from greedy matches.

Learn to clean and anonymize data with Python's re.sub, replacing patterns like emails and dates, using captured groups to reformat text into year-month-day formats.

Learn regex techniques for text processing, including extracting IP addresses from logs, splitting text into sentences, handling multi-line strings, and compiling patterns for faster repeated use.

Explain how Python's compile-time syntax errors differ from runtime exceptions. Demonstrate handling strategies to keep programs running when errors like file not found, zero division, or type errors occur.

Master exception handling in Python using try, except, else, and finally to keep scripts running, handle errors such as division by zero, and release resources.

Discover how to use built-in Python exceptions, write specific except clauses for zero division and type errors, leverage else and finally, and raise custom errors to reveal root causes.

Learn to use Python's subprocess.check_output and exception handling to ping multiple IP addresses from a file, gracefully handling down hosts and continuing the checks.

Explore object oriented programming as a paradigm distinct from procedural styles, focusing on classes, objects, attributes, and methods in Python.

Demonstrate object-oriented programming with the Python turtle library by creating and controlling turtle objects, exploring classes, objects, attributes, methods, and core concepts like abstraction and encapsulation.

Learn to define our own classes and create objects with attributes and methods in Python using the class keyword, Pascal case notation, and docstrings, and perform basic instantiation.

Learn the __init__ method and class constructor, how self links name and year to each object, and how parameters map to attributes and reveal object state via dunder dict.

Explore the Python __del__ destructor, also known as the finalizer, and how it frees resources when an object's lifetime ends. Python's garbage collector largely handles memory, making explicit destructors uncommon.

Define instance attributes with a constructor or set_energy, access via dot notation or getattr, handle missing attributes with a default, and differentiate class attributes like population shared by all robots.

Explore Python's magic methods (dunder) and operator overloading to customize class behavior, including printing objects with __str__ and adding instances via __add__.

Build a practical chatbot class that interfaces with the OpenAI API using object-oriented design, including a configurable model and temperature, to generate conversational responses.

TypedDict enables static type checking for dictionaries with fixed keys and types, demonstrated with a movie example (title, year, rating) and function annotations.

Learn how to define optional keys in a typed dict using total=False, access nested typed dicts like address within user, and safely retrieve values with get to avoid key errors.