Building Gen AI App 12+ Hands-on Projects with Gemini Pro

Start programming in VS Code by creating and activating a conda environment. Install ipykernel to run notebooks and explore Python 3.12 features.

Explore Python syntax and semantics, covering single and multi line comments, case sensitivity, indentation, line continuation, and variable assignment with type inference and dynamic typing.

Explore variables in Python by declaring and assigning values, mastering naming conventions, and understanding types, type checking, conversion, dynamic typing, and basic examples.

Explore the fundamentals of Python data types—integers, floating point numbers, strings, booleans—and learn type conversion and handling common errors with practical examples.

Explore Python operators across arithmetic, comparison, and logical categories with practical examples, including a calculator project and results for +, -, *, /, //, %, **, and comparisons.

Explore Python control flow with conditional statements, including if, elif, else and nested forms, with real-world examples like leap year, and practical error prevention and best practices.

Master Python loops with for and while constructs, range, and loop controls like break, continue, and pass, plus nested loops and practical examples from strings to prime numbers.

Master Python lists: create, access, modify, slice, and iterate. Learn list methods (append, insert, remove, pop, index, count, sort, reverse, clear) and list comprehension, including nested forms.

Explore practical, real-world uses of Python lists, from to-do lists and inventory management to organizing student grades and collecting user feedback, highlighting append, remove, and membership checks.

Explore Python tuples by learning to create, access, and manipulate immutable ordered collections, including packing and unpacking, star unpacking, and nested tuples.

Explore Python dictionaries, from creating and accessing key value pairs to updating, deleting, and iterating; learn nested dictionaries, dictionary comprehension, and merging techniques.

Learn how to define and call Python functions, handle parameters including default, positional and keyword arguments, and use return statements and docstrings to organize reusable code for data science projects.

Explore practical Python functions with real-world examples: temperature conversion, password strength checks, shopping cart totals, palindrome, factorial recursion, word frequency in files, and email validation.

Explore lambda functions in Python: create anonymous one-expression functions with the lambda keyword, use them with multiple arguments, and apply them via map for operations like squaring numbers.

The map function applies a given function to all items in an input iterable and returns a map object (an iterator), enabling efficient data transformation in Python.

Master Python's filter function to extract items from iterables using a condition, with lambda-based examples on even numbers, greater than five, and age-based dictionary filtering for data cleaning.

Learn how Python modules and packages organize and reuse code, import and use libraries like math and NumPy, install packages, and create custom packages with __init__.py and subpackages.

Explore the Python standard library, from array and math to os, json, csv, datetime, time, and re, with practical examples like file copy, json serialization, and CSV handling.

Explore Python file operations by creating, writing, reading, and appending text and binary files; learn with open modes, reading whole or line by line, and seeking to reset the cursor.

Learn to work with file paths using the OS module: create directories, list files, join paths, and check for files or directories, then derive absolute and relative paths.

Explore Python exception handling with try, except, else, and finally blocks to gracefully manage errors, such as name errors and zero division, including practical file handling examples.

Explore object oriented programming in Python by modeling real world entities with classes and objects, using constructors and instance variables, and implementing methods like deposit, withdraw, and get balance.

Explore Python inheritance, single and multiple, with car and Tesla examples showing super calls and shared attributes, plus animal and pet models illustrating combined initialization and speak behavior.

Learn how polymorphism in Python enables a single method to behave differently across classes through method overriding and abstract base classes, illustrated with animal, shape, and vehicle examples.

Explore encapsulation in Python and abstraction in OOP, learning to bundle data and methods, hide complex details, and implement getter and setter methods for private attributes.

Explore how abstraction hides complex implementation details and shows only the necessary features, illustrated by a vehicle base class with an abstract start engine method and washing machine examples.

Explore Python magic methods, or dunder methods, the double underscore features that define object behavior for built in operations and show how to override __init__ and __str__.

Explore operator overloading in Python by implementing magic methods like __add__, __sub__, and __mul__ within a vector class to customize arithmetic and comparisons.

Develop a custom exception in Python, with a generic error class and a DOB exception; use try/except to validate age 20–30 in an exam form, displaying clear messages.

Explore Google's Gemini models, including ultra, pro, nano, and flash, and their architectures, capabilities, and long-context multimodal applications; learn to obtain api keys for on-device ai projects.

Create free api keys to access Google Gemini models via AI Studio, then set up a Google Cloud project and store keys as environment variables for end-to-end use.

Master the Google Gemini api in python. Configure api keys in Google Colab and explore Gemini models for text and image tasks.

Discover how generative AI sits as a subset of deep learning, alongside discriminative approaches. Learn to generate text, images, and music from unstructured data and large language models like ChatGPT.