Pre-Work: Introduction to AI Landscape & Python Programming

🗺 Concept Map

How all Pre-Work topics connect together:

🤖 Introduction to AI Landscape

AI vs ML vs Deep Learning vs GenAI vs LLMs

History & Evolution of AI

🛠 Types of AI

ML Learning Paradigms

💼 AI in Business

AI is transforming every major industry. Understanding where AI creates value helps you identify opportunities and frame problems correctly throughout the PGP program.

Industry Applications

Case Studies

💻 Google Colab Setup

Why Use Colab for This Program?

Getting Started with Colab

Essential Colab Code

# Check Python version
import sys
print(sys.version)

# Mount Google Drive to access your files
from google.colab import drive
drive.mount('/content/drive')

# Install a library not in Colab by default
!pip install xgboost

# Upload files from your computer
from google.colab import files
uploaded = files.upload()

# Download a file to your computer
files.download('my_model.pkl')

Keyboard Shortcuts

📄 Variables & Data Types

Python Data Types

# Creating variables — Python infers the type
name = "Vamshi"          # str
age = 25                 # int
gpa = 3.85               # float
is_student = True         # bool
scholarship = None       # NoneType

# Check type with type()
print(type(name))       # <class 'str'>
print(type(age))        # <class 'int'>
print(type(gpa))        # <class 'float'>

# Check type with isinstance()
print(isinstance(age, int))   # True

Type Conversion (Casting)

# Explicit type conversion
x = "42"             # string
y = int(x)            # now integer 42
z = float(x)          # now float 42.0

# Common pitfall — can't convert non-numeric strings
# int("hello")  # ValueError!

# Implicit conversion (Python does it automatically)
result = 5 + 3.0     # int + float = float (8.0)

# Boolean conversions (truthy/falsy)
bool(0)         # False (zero is falsy)
bool(42)        # True  (non-zero is truthy)
bool("")        # False (empty string is falsy)
bool("hello")  # True  (non-empty is truthy)
bool([])        # False (empty list is falsy)
bool(None)      # False (None is falsy)

✍ Operators

Arithmetic Operators

# Arithmetic in action — MSE calculation
actual = 100
predicted = 95
error = actual - predicted      # 5 (residual)
sq_error = error ** 2         # 25 (squared error)

# Floor division for batching
total_samples = 1000
batch_size = 32
num_batches = total_samples // batch_size  # 31
leftover = total_samples % batch_size    # 8

Comparison Operators

Logical Operators

# Practical: Filter conditions for ML data
age = 30
income = 75000
has_degree = True

# Complex condition
is_target = (age >= 25) and (income > 50000) and has_degree
print(is_target)  # True

# Chaining comparisons (Python allows this!)
score = 85
print(70 <= score <= 90)  # True (same as: score >= 70 and score <= 90)

Assignment Operators (Shortcuts)

x = 10
x += 5   # x = x + 5  → 15
x -= 3   # x = x - 3  → 12
x *= 2   # x = x * 2  → 24
x /= 4   # x = x / 4  → 6.0
x **= 2  # x = x ** 2 → 36.0

🔤 Strings

Creating Strings

# Multiple ways to create strings
s1 = "Hello, World!"          # double quotes
s2 = 'Hello, World!'          # single quotes (identical)
s3 = """Multi-line
string spanning
several lines"""                   # triple quotes
s4 = "He said \"hello\""      # escaping quotes
s5 = r"C:\new\folder"         # raw string (no escape)

String Operations

text = "Machine Learning"

# Indexing (0-based)
print(text[0])     # "M"
print(text[-1])    # "g" (last character)

# Slicing [start:stop:step]
print(text[0:7])   # "Machine"
print(text[8:])    # "Learning"
print(text[::-1])  # "gninraeL enihcaM" (reverse)

# Common methods
print(text.upper())         # "MACHINE LEARNING"
print(text.lower())         # "machine learning"
print(text.split())         # ["Machine", "Learning"]
print(text.replace("Machine", "Deep"))  # "Deep Learning"
print(len(text))              # 16
print("Learn" in text)      # True (membership test)

f-Strings (Formatted String Literals)

model_name = "Random Forest"
accuracy = 0.9543
n_trees = 100

# f-string with formatting
print(f"Model: {model_name}")
# "Model: Random Forest"

print(f"Accuracy: {accuracy:.2%}")
# "Accuracy: 95.43%"

print(f"Trees: {n_trees:,} | Acc: {accuracy:.4f}")
# "Trees: 100 | Acc: 0.9543"

# Expressions inside f-strings
print(f"Error rate: {(1 - accuracy) * 100:.2f}%")
# "Error rate: 4.57%"

📦 Data Structures

Data structures determine how you store, organize, and access data. Choosing the right one is fundamental to writing efficient Python code for data science.

Lists

# Creating and manipulating lists
scores = [85, 92, 78, 95, 88]

# Indexing and slicing
print(scores[0])      # 85 (first)
print(scores[-1])     # 88 (last)
print(scores[1:4])   # [92, 78, 95]

# Modifying (lists are mutable!)
scores.append(91)       # Add to end: [85, 92, 78, 95, 88, 91]
scores.insert(0, 100)  # Insert at index 0
scores.remove(78)       # Remove first occurrence of 78
popped = scores.pop()   # Remove and return last item

# Useful operations
print(len(scores))      # Length
print(sorted(scores))   # Returns sorted copy
print(sum(scores))      # Sum all elements
print(max(scores))      # Maximum value
print(min(scores))      # Minimum value

# List comprehension (concise way to create lists)
squares = [x**2 for x in range(1, 6)]  # [1, 4, 9, 16, 25]

Tuples

# Tuples are immutable
coordinates = (37.7749, -122.4194)
rgb_color = (255, 128, 0)
model_config = ("RandomForest", 100, 0.8)

# Tuple unpacking
lat, lon = coordinates
print(f"Lat: {lat}, Lon: {lon}")

# coordinates[0] = 40.0  # TypeError! Tuples are immutable

Dictionaries

# Creating and using dictionaries
model_results = {
    "model": "XGBoost",
    "accuracy": 0.9534,
    "precision": 0.9412,
    "recall": 0.9621,
    "f1_score": 0.9515
}

# Access values
print(model_results["accuracy"])       # 0.9534
print(model_results.get("auc", "N/A"))  # "N/A" (safe access)

# Add/modify
model_results["auc"] = 0.9801          # Add new key
model_results["accuracy"] = 0.9567     # Update existing

# Iterate
for metric, value in model_results.items():
    print(f"{metric}: {value}")

# Useful methods
print(model_results.keys())     # all keys
print(model_results.values())   # all values

Sets

# Sets auto-remove duplicates
features_v1 = {"age", "income", "education", "age"}
print(features_v1)  # {"age", "income", "education"} (no dupe)

features_v2 = {"age", "income", "location", "gender"}

# Set operations
print(features_v1 | features_v2)  # Union: all features
print(features_v1 & features_v2)  # Intersection: common features
print(features_v1 - features_v2)  # Difference: in v1 but not v2

When to Use Which?

🔀 Conditional Statements & Control Flow

if / elif / else

# Basic if/elif/else
accuracy = 0.87

if accuracy >= 0.95:
    print("Excellent model! Ready for production.")
elif accuracy >= 0.85:
    print("Good model. Consider hyperparameter tuning.")
elif accuracy >= 0.70:
    print("Acceptable. Try feature engineering.")
else:
    print("Poor model. Revisit data and approach.")
# Output: "Good model. Consider hyperparameter tuning."

Nested Conditions

# Nested conditions for complex logic
has_gpu = True
dataset_size = 50000
task = "image_classification"

if task == "image_classification":
    if has_gpu and dataset_size > 10000:
        model = "Deep CNN"
    elif dataset_size > 1000:
        model = "Transfer Learning"
    else:
        model = "Simple CNN + Augmentation"
else:
    model = "XGBoost"

print(f"Recommended: {model}")
# Output: "Recommended: Deep CNN"

Ternary Expressions (One-line if)

# Ternary: value_if_true if condition else value_if_false
accuracy = 0.92
status = "Pass" if accuracy >= 0.9 else "Fail"
print(status)  # "Pass"

# Practical: label encoding
label = 1 if prediction > 0.5 else 0

# Practical: handling missing values
value = data.get("score") if data else 0

Common Patterns

# Check for None explicitly
if value is None:
    print("Missing!")

# Use 'in' for membership
if ext in [".csv", ".xlsx"]:
    print("Data file!")

# Chain comparisons
if 0 <= prob <= 1:
    print("Valid")

# Don't use == for None
if value == None:   # works but wrong
    print("Missing!")

# Don't chain OR for each
if ext==".csv" or ext==".xlsx":
    print("Data file!")

# Don't split comparisons
if prob>=0 and prob<=1:
    print("Valid")

⚠ Common Mistakes & Misconceptions

AI Landscape Mistakes

Python Basics Mistakes