Chapter 1: Python & AI Introduction

AI Creator's Launchpad

Before you teach AI to think, you must learn to think like a coder. Master the foundations of the $2 Trillion economy.

$2T

AI Market by 2030

90%

AI-Assisted Content

100%

Executable Intelligence

Python: The Language of Creation

Python powers 90% of today's AI research. It's simple, powerful, and universal. Master these 4 blocks to build anything.

1. Variables

Containers for your data.

2. Functions

Reusable mini-programs.

3. Loops

Automate repetitive tasks.

4. Lists & Dicts

Structured data storage.

# Click a concept to see it in action ai_model = "GPT-4o"

Output: Ready...

The Connective Tissue (APIs)

APIs are the bridges. They let your Python code talk to world-class models like DALL-E and Gemini.

Your App

Python Script

API Request

AI Model

OpenAI / Anthropic

Mini Projects

Select Your Mini Project

Hands-on builders to internalize every Python + AI concept.

45-60 mins each Earn HI Coins Ready-to-run code Jump to Labs

Communication

Tone Adjuster

Paste an email draft and let AI rewrite it for professional or friendly tones.

Prompt frameworks
Voice consistency

Education

Explain Like I'm 5

Simplify quantum physics or AI concepts using natural storytelling.

System prompts
JSON formatting

Content

Social Generator

Generate 3 platform-ready posts with hooks, CTAs, and trending tags.

Tone tokens
Hook builder

Marketing

Product Writer

Turn feature lists into persuasive marketing descriptions instantly.

Feature mapping
Persona targeting

Lab 1: Python Foundations

Building the core blocks of your AI applications

1.1 Variables & Types

Variables are labels for boxes holding information. We use int, float, str, and bool to store different data shapes.

In [1]:

my_age = 30
pi_value = 3.14159
user_name = "Alex"
is_learning = True

print(f"Name: {user_name} ({type(user_name)})")
print(f"Age: {my_age}, Learn: {is_learning}")

1.2 Lists: Ordered Items

A list is an ordered, changeable collection. Access items by index (starts at 0).

In [2]:

fruits = ["apple", "banana", "cherry"]
print("Initial:", fruits)
fruits.append("orange")
print("First Item:", fruits[0])
fruits.remove("banana")
print("Modified:", fruits)

Lab 2: Top 15 Interview Problems

A curated guide to mastering technical logic

Q1: Reverse a String

Using Python's slice syntax [::-1] to efficiently return a reversed string.

In [1]:

def reverse_string(s):
    return s[::-1]

print(f"'hello' reversed: {reverse_string('hello')}")

Q2: Palindrome Check

Checking if a string reads the same forwards and backwards after cleaning non-alphanumeric characters.

In [2]:

def is_palindrome(s):
    cleaned = "".join(c.lower() for c in s if c.isalnum())
    return cleaned == cleaned[::-1]

print(f"Is 'A man, a plan, a canal: Panama' a palindrome? {is_palindrome('A man, a plan, a canal: Panama')}")

Q3: Check for Anagrams

Two strings are anagrams if their sorted characters match. We ignore case and spaces.

In [3]:

def are_anagrams(s1, s2):
    return sorted(s1.lower().replace(" ","")) == sorted(s2.lower().replace(" ",""))

print(f"Are 'silent' and 'listen' anagrams? {are_anagrams('silent', 'listen')}")

Q4: Find Missing Number

Calculating the expected sum using n * (n + 1) // 2 and subtracting the actual sum.

In [4]:

def find_missing(nums):
    n = len(nums)
    expected = n * (n + 1) // 2
    return expected - sum(nums)

print(f"Missing in [3, 0, 1]: {find_missing([3, 0, 1])}")

Q5: Find Duplicates in List

Using a Set to track seen items for O(n) lookups.

In [5]:

def find_duplicates(nums):
    seen = set()
    dupes = set()
    for n in nums:
        if n in seen: dupes.add(n)
        else: seen.add(n)
    return list(dupes)

print(f"Duplicates: {find_duplicates([1, 2, 3, 2, 1, 5])}")

Q6: Two-Sum Problem

Finding indices of two numbers adding to target using a dictionary lookup for the complement.

In [6]:

def two_sum(nums, target):
    seen = {}
    for i, num in enumerate(nums):
        comp = target - num
        if comp in seen: return [seen[comp], i]
        seen[num] = i
    return []

print(f"Indices for Target 9: {two_sum([2, 7, 11, 15], 9)}")

Q7: Merge Two Sorted Lists

Using two pointers to merge without a full re-sort for efficiency.

In [7]:

def merge_lists(l1, l2):
    res = []
    i = j = 0
    while i < len(l1) and j < len(l2):
        if l1[i] < l2[j]: res.append(l1[i]); i += 1
        else: res.append(l2[j]); j += 1
    res.extend(l1[i:]); res.extend(l2[j:])
    return res

print(f"Merged: {merge_lists([1, 2, 4], [1, 3, 4])}")

Q8: FizzBuzz Logic

Testing modulo logic: Multiples of 3, 5, or both.

In [8]:

def fizzbuzz(n):
    res = []
    for i in range(1, n+1):
        if i % 15 == 0: res.append("FizzBuzz")
        elif i % 3 == 0: res.append("Fizz")
        elif i % 5 == 0: res.append("Buzz")
        else: res.append(str(i))
    return res

print(fizzbuzz(15))

Q9: Fibonacci Sequence

Comparing iterative vs recursive approaches for generating the n-th number.

In [9]:

def fib(n):
    a, b = 0, 1
    for _ in range(n): a, b = b, a + b
    return a

print(f"10th Fibonacci: {fib(10)}")

Q10: Prime Number Check

Checking divisors up to the square root of n for optimized performance.

In [10]:

import math
def is_prime(n):
    if n < 2: return False
    for i in range(2, int(math.sqrt(n)) + 1):
        if n % i == 0: return False
    return True

print(f"Is 29 Prime? {is_prime(29)}")

Q11: Group Anagrams

Grouping words that share characters by using sorted strings as dictionary keys.

In [11]:

def group_anagrams(strs):
    m = {}
    for s in strs:
        key = "".join(sorted(s))
        if key not in m: m[key] = []
        m[key].append(s)
    return list(m.values())

print(group_anagrams(["eat", "tea", "tan", "ate"]))

Q12: Implement a Stack (LIFO)

Designing a class to support push, pop, and peek using an internal list.

In [12]:

class Stack:
    def __init__(self): self.items = []
    def push(self, i): self.items.append(i)
    def pop(self): return self.items.pop() if self.items else None
    def peek(self): return self.items[-1] if self.items else None

s = Stack()
s.push(10); s.push(20)
print(f"Peek: {s.peek()}, Popped: {s.pop()}")

Q13: List Comprehensions

Creating new lists from existing ones in a single line using [expression for item in list if condition].

In [13]:

nums = [1, 2, 3, 4, 5, 6]
squares_of_evens = [n*n for n in nums if n % 2 == 0]
print(f"Result: {squares_of_evens}")

Q14: Dynamic Arguments

Using *args for extra positional items and **kwargs for extra named parameters.

In [14]:

def demo(*args, **kwargs):
    print(f"Positional: {args}")
    print(f"Keyword: {kwargs}")

demo(1, 2, name="Alice", age=30)

Q15: Simple OOP - Car Class

Defining a constructor and class methods to manage object state.

In [15]:

class Car:
    def __init__(self, make, model, year):
        self.make, self.model, self.year = make, model, year
    def describe(self):
        return f"{self.year} {self.make} {self.model}"

my_car = Car("Toyota", "Camry", 2020)
print(my_car.describe())

Logic Challenges

Warehouse Security Problem

Determining minimum guards needed by calculating overlapping shift depth.

Challenge [1]:

def min_guards(shifts):
    events = []
    for s in shifts:
        events.append((s['start'], 1))
        events.append((s['end'], -1))
    events.sort(key=lambda x: (x[0], x[1]))
    mx, curr = 0, 0
    for _, delta in events:
        curr += delta
        mx = max(mx, curr)
    return mx

shifts = [{"start": 100, "end": 400}, {"start": 300, "end": 600}]
print(f"Guards required: {min_guards(shifts)}")

Sliding Window Processor

Calculating moving averages and max subarray sums using class-based logic.

Challenge [2]:

class WindowProcessor:
    def __init__(self, data, size):
        self._data, self.s = data, size
    def get_moving_average(self):
        res = []
        for i in range(len(self._data) - self.s + 1):
            res.append(sum(self._data[i:i+self.s])/self.s)
        return res

proc = WindowProcessor([1, 3, 2, 6, -1, 4, 1, 8, 2], 5)
print(f"Moving Averages: {proc.get_moving_average()}")