Friday, August 22, 2025

Introduction to Object Orientation Encapsulation Inheritance Polymorphism Abstraction Classes & Objects Python

๐Ÿ“˜ Part 1: Introduction to Object Orientation

  • Object Orientation = A way of designing programs around objects (real-world things).

  • Each object has:

    • Properties (attributes/data) → e.g., a Car has color, model

    • Methods (functions/behavior) → e.g., a Car can start(), stop()

Why use Object Orientation?

  • Models the real world better

  • Easy to reuse and extend code

  • Easier maintenance

  • Saves development time


๐Ÿ“˜ Part 2: Main Principles of OOP

  1. Encapsulation → hiding details

    • Example: You just call tv.turnOn(), you don’t care how it works inside.

  2. Inheritance → child class gets features of parent class

    • Example: Dog and Cat inherit from Animal.

  3. Polymorphism → same function, different behavior

    • Example: draw() works differently for Circle and Square.

  4. Abstraction → show only important things, hide details

    • Example: A car’s steering wheel hides the complex engine mechanism.

  5. Classes & Objects

    • Class = blueprint (like a recipe)

    • Object = actual thing created from blueprint (like a cake baked from recipe)


๐Ÿ“˜ Part 3: Python Basics Refresher

✅ Data Structures

Structure Written With Ordered? Changeable? Allows Duplicates? Example
Tuple () Yes ❌ No ✅ Yes (1, 2, "Hi")
List [] Yes ✅ Yes ✅ Yes [1,2,2,3]
Set {} No ✅ Yes ❌ No {1,2,3}
Dictionary {key:val} Yes ✅ Yes Keys ❌ dupes, Values ✅ {"A":10, "B":20}

✅ Variables

  • Must start with a letter or _

  • Cannot start with number

  • Case-sensitive (Age ≠ age)

  • Use meaningful names (student_age ✔ vs x ✘)


✅ Operators

  • Arithmetic: + - * / % ** //

  • Comparison: == != > < >= <=

  • Logical: and, or, not

  • Assignment: =, +=, -=

  • Membership: in, not in

  • Identity: is, is not


✅ Indentation in Python

  • Python uses spaces instead of { }.

  • Example:

if age >= 18:
    print("Adult")
else:
    print("Child")

๐Ÿ“˜ Part 4: Control Flow

  1. Sequence → run line by line

  2. Selection (if/else) → decision-making

  3. Repetition (loops) → repeat tasks

    • for loop → repeat fixed times

    • while loop → repeat until condition false

    • break → exit loop early

    • continue → skip one loop step


๐Ÿ“˜ Part 5: Functions

  • A function = block of code that does something specific.

def greet(name):
    return "Hello " + name

print(greet("Faaz"))
  • Functions improve reusability and organization.


๐Ÿ“˜ Part 6: Sorting & Searching

  • Bubble Sort → repeatedly swap until sorted (slow for big data).

  • Selection Sort → pick smallest, put in front.

  • Insertion Sort → place each new item in correct place.

  • Merge & Quick Sort → faster, used in real life.


๐Ÿ“˜ Part 7: Advanced Math

  • Factorial5! = 5×4×3×2×1 = 120

  • Permutations (order matters) → number of ways to arrange items

  • Combinations (order doesn’t matter) → number of ways to choose items


๐Ÿ“˜ Part 8: OOP in Python

class Dog:
    def __init__(self, name):  # constructor
        self.name = name       # attribute

    def bark(self):            # method
        print(self.name + " says Woof!")

dog1 = Dog("Buddy")  # create object
dog1.bark()          # Output: Buddy says Woof!
  • self → refers to the object itself (must always be used inside class).

  • Method vs Function

    • Function → independent

    • Method → belongs to a class/object


๐Ÿ“˜ Part 9: Flowcharts & Problem Solving

  • Flowcharts help visualize logic.

  • Example: A game where user inputs a number, computer generates random number → compare → check win/loss.



๐Ÿ”น Cohesion (togetherness inside a class/module)

  • Meaning: How strongly related the functions inside one class are.

  • High Cohesion = everything in the class is focused on one clear job.

  • Low Cohesion = class does many unrelated jobs → confusing & hard to maintain.

✅ Example:

# High Cohesion (Payment related only)
class PaymentProcessor:
    def validate_payment(self): pass
    def process_payment(self): pass
    def send_receipt(self): pass

This class only handles payment-related workhigh cohesion.

# Low Cohesion (Mixing many jobs)
class Utility:
    def send_email(self): pass
    def calculate_salary(self): pass
    def draw_graph(self): pass

Here, one class is trying to do too many different thingslow cohesion.


๐Ÿ”น Coupling (connections between classes/modules)

  • Meaning: How much one class depends on another class.

  • Tightly Coupled = classes know too much about each other → hard to change one without breaking the other.

  • Loosely Coupled = classes depend only on essential information → flexible and easy to maintain.

✅ Example:

# Loose Coupling (better)
class PaymentProcessor:
    def process(self, payment_method):
        payment_method.pay()

class CreditCard:
    def pay(self): print("Paying with Credit Card")

class PayPal:
    def pay(self): print("Paying with PayPal")

processor = PaymentProcessor()
processor.process(PayPal())  # Can easily swap methods

Here, PaymentProcessor only needs to know that payment_method has a pay() function.
It doesn’t care how PayPal or CreditCard works inside → loosely coupled.

# Tight Coupling (bad)
class PaymentProcessor:
    def pay_with_creditcard(self): pass
    def pay_with_paypal(self): pass

If you add a new method (e.g., Bitcoin), you must modify the class.
This makes the system hard to extend → tightly coupled.


๐Ÿ”น Rule of Thumb

๐Ÿ‘‰ Good software should be Highly Cohesive + Loosely Coupled:

  • Each class does one job well (high cohesion).

  • Classes know only what they must about each other (loose coupling).

That’s how professional software is easier to read, test, reuse, and maintain.




๐Ÿ”น What is Abstraction?

Abstraction = show only important details and hide the unnecessary ones.
๐Ÿ‘‰ Example: When you drive a car, you just use the steering wheel & pedals.
You don’t see the engine’s wires or fuel injection system — that’s hidden.


๐Ÿ”น Types of Abstraction in OOP

1. Classification Abstraction

  • Meaning: Grouping similar objects into classes.

  • Analogy: Labrador, Poodle, Bulldog → all grouped as Dog.

  • Focus: Looks at similarities.

class Dog:
    def __init__(self, breed):
        self.breed = breed

dog1 = Dog("Labrador")
dog2 = Dog("Poodle")

Here, Dog is a general class for all breeds.


2. Generalization Abstraction

  • Meaning: Creating parent-child relationships (inheritance).

  • Analogy: "Mammal" is a general class → Dog, Cat, Horse are children.

  • Focus: Top-down → from general → specific.

class Animal:   # Parent
    def breathe(self): print("Breathing")

class Dog(Animal):   # Child
    def bark(self): print("Woof!")

d = Dog()
d.breathe()  # Inherited
d.bark()

Here, Dog inherits general features of Animal.


3. Aggregation Abstraction (weak “has-a” relation)

  • Meaning: One object contains another, but parts can live independently.

  • Analogy: A Car has a Radio. The Radio can exist without the Car.

class Radio:
    def play(self): print("Playing music")

class Car:
    def __init__(self, radio):
        self.radio = radio   # "has-a" relation

my_radio = Radio()
my_car = Car(my_radio)
my_car.radio.play()

4. Composition Abstraction (strong “part-of” relation)

  • Meaning: Stronger form of aggregation → parts cannot exist independently.

  • Analogy: A House has Rooms. Rooms do not exist outside the house.

class Room:
    def __init__(self, name):
        self.name = name

class House:
    def __init__(self):
        self.rooms = [Room("Living"), Room("Bedroom")]

h = House()
for r in h.rooms:
    print(r.name)

Here, Room objects exist only because the House exists.


5. Behavioral Abstraction

  • Meaning: Hiding implementation details of methods, only showing what they do.

  • Analogy: You click “Print” → you don’t know if it’s laser/inkjet, but you get output.

  • Focus: Method signatures, not how they work inside.

class Printer:
    def print_document(self):   # behavior is abstract
        raise NotImplementedError("Subclasses must implement")

class LaserPrinter(Printer):
    def print_document(self):
        print("Printing with laser...")

class InkjetPrinter(Printer):
    def print_document(self):
        print("Printing with inkjet...")

p = InkjetPrinter()
p.print_document()

Here, print_document() hides details, but each printer implements it differently.


๐Ÿ”น Summary

Type Focus Example
Classification Grouping similar objects Dogs → Dog class
Generalization Parent-child hierarchy Animal → Dog, Cat
Aggregation Weak "has-a" Car has a Radio
Composition Strong "part-of" House has Rooms
Behavioral Hide method details Printer with different implementations

.


๐Ÿ”น Classification

  • Meaning: Grouping similar objects into classes based on common properties.

  • Approach: Bottom-Up → start from specific objects → group them.

  • Question it answers: “What category does this object belong to?”

✅ Example:

  • Labrador, Poodle, Bulldog → all grouped as Dog.

  • Cat, Tiger, Lion → all grouped as Cat family.

# Classification Example
class Dog:
    def __init__(self, breed):
        self.breed = breed

dog1 = Dog("Labrador")
dog2 = Dog("Poodle")
print(dog1.breed)   # Labrador

Here, classification means: we group similar objects → all are Dogs.


๐Ÿ”น Generalization

  • Meaning: Create hierarchies where specific classes inherit from general ones.

  • Approach: Top-Down → start from general concept → create specialized ones.

  • Question it answers: “What is the more general concept that covers this?”

✅ Example:

  • “Dog”, “Cat”, “Horse” → all are generalized as Animal.

# Generalization Example
class Animal:  # General (parent)
    def breathe(self): print("Breathing")

class Dog(Animal):  # Specific (child)
    def bark(self): print("Woof!")

class Cat(Animal):  # Specific (child)
    def meow(self): print("Meow!")

d = Dog()
d.breathe()   # Inherited from Animal
d.bark()

Here, generalization means: we create a general Animal class → and let Dog & Cat inherit from it.


๐Ÿ”น Key Differences (Side-by-Side)

Feature Classification Generalization
Definition Grouping similar objects into categories Building parent-child relationships
Direction Bottom-Up (from specific → category) Top-Down (from general → specialized)
Focus Similarities among objects Hierarchies & inheritance
Example Labrador + Poodle + Bulldog = Dog class Dog, Cat, Horse → inherit from Animal class
Question “What category is this object in?” “What general concept does this belong to?”

๐Ÿ”น Real-Life Analogy

  • Classification:

    • You look at fruits → Apple, Banana, Orange → group them as Fruits.

  • Generalization:

    • You start with “Plant” → then generalize into “Fruit” and “Vegetable”.


In short:

  • Classification organizes objects into groups.

  • Generalization creates parent-child hierarchies among those groups.


๐Ÿƒ Flashcards for OOP with Python 


๐Ÿ”น Beginner (Basics)

Q1: What is Object Orientation?
A1: A way of designing programs around objects (real-world entities) that have attributes (data) and methods (behavior).

Q2: What is a Class?
A2: A blueprint/template for creating objects.

Q3: What is an Object?
A3: An instance of a class (real thing created from the blueprint).

Q4: What are the 4 main principles of OOP?
A4: Encapsulation, Inheritance, Polymorphism, Abstraction.

Q5: What is the difference between Attributes and Methods?
A5: Attributes = variables (data of object). Methods = functions (behavior of object).


๐Ÿ”น Encapsulation & Abstraction

Q6: What is Encapsulation?
A6: Hiding internal details of a class and controlling access using getters & setters.

Q7: Example of Encapsulation in Python?
A7:

class Bank:
    def __init__(self, balance):
        self.__balance = balance
    def get_balance(self):
        return self.__balance

Q8: What is Abstraction?
A8: Showing only necessary features, hiding the implementation details.

Q9: Types of Abstraction?
A9: Classification, Generalization, Aggregation, Composition, Behavioral.

Q10: Example of Abstraction in real life?
A10: Driving a car → you use the steering wheel (interface), but don’t see engine details.


๐Ÿ”น Inheritance & Polymorphism

Q11: What is Inheritance?
A11: When a class (child) inherits attributes & methods from another class (parent).

Q12: What is Polymorphism?
A12: Same method name, different behavior depending on object.

Q13: Python Example of Polymorphism?
A13:

class Dog:
    def speak(self): print("Woof!")
class Cat:
    def speak(self): print("Meow!")
for animal in [Dog(), Cat()]:
    animal.speak()

๐Ÿ”น Cohesion & Coupling

Q14: What is High Cohesion?
A14: A class does only one job well (focused).

Q15: What is Low Coupling?
A15: Classes are independent and interact only through simple interfaces.

Q16: Ideal design principle?
A16: High Cohesion + Low Coupling → modular, reusable code.


๐Ÿ”น Python Basics from PDF

Q17: List vs Tuple vs Set vs Dictionary?
A17:

  • List → ordered, mutable, allows duplicates

  • Tuple → ordered, immutable

  • Set → unordered, unique values only

  • Dictionary → key-value pairs

Q18: What is Indentation in Python?
A18: Spaces at the start of a line that define blocks of code (instead of {}).

Q19: What does break and continue do in loops?
A19: break exits loop, continue skips current iteration.


๐Ÿ”น Control Flow

Q20: Difference between Sequence, Selection, and Repetition?
A20:

  • Sequence → step-by-step execution

  • Selection → decision-making (if/else)

  • Repetition → looping (for, while)

Q21: Deterministic vs Non-Deterministic Loops?
A21:

  • Deterministic → fixed iterations (e.g., for i in range(5))

  • Non-Deterministic → unknown iterations (e.g., while user_input != "stop")


๐Ÿ”น Functions & Recursion

Q22: What is a Function?
A22: A reusable block of code with name, parameters, and optional return value.

Q23: Difference between Function and Method?
A23:

  • Function = standalone block

  • Method = belongs to a class/object

Q24: What is Recursion?
A24: A function calling itself until a base case is reached.

Q25: Recursion vs Iteration?
A25:

  • Recursion → uses call stack, may risk stack overflow

  • Iteration → uses loops, more memory efficient


๐Ÿ”น Algorithms

Q26: What is Bubble Sort?
A26: Repeatedly swap adjacent elements if out of order.

Q27: What is Selection Sort?
A27: Repeatedly find the minimum element and place it in order.

Q28: What is Insertion Sort?
A28: Insert each element into the correct position in sorted part.

Q29: What is Merge Sort?
A29: Divide & conquer → split list, sort halves, merge results.

Q30: What is Quick Sort?
A30: Choose a pivot, partition list, sort sublists recursively.


๐Ÿ”น Math & Probability

Q31: What is Factorial?
A31: n! = n × (n-1) × ... × 1

Q32: What is a Permutation?
A32: Selection where order mattersnPk = n! / (n-k)!

Q33: What is a Combination?
A33: Selection where order does NOT matternCk = n! / (k!(n-k)!)


๐Ÿ”น Advanced OOP in Python

Q34: What is self in Python classes?
A34: Refers to the current object instance (needed for attributes & methods).

Q35: Why use __ (double underscore) before variable names?
A35: To make them private → accessed only through getters/setters.

Q36: What is the difference between Public, Protected, and Private attributes in Python?
A36:

  • Public → normal (name) → accessible anywhere

  • Protected → _name → “internal use only” (by convention)

  • Private → __name → hidden, only accessible inside class


๐Ÿ”น Flowcharts & Problem Solving

Q37: Why use Flowcharts for algorithms?
A37: Visual clarity, easier debugging, error detection, and communication.

Q38: Example: Game logic flashcard
Q38A: User inputs number → computer generates random → compare → if 2+ matches in 3 tries → Win.



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Thursday, August 21, 2025

E-Prescription Generator system healthcare providers to electronically create, transmit, and manage prescriptions, replacing traditional paper-based prescriptions BIT BSc Software Web Development Tamil

 


✨๐Ÿ’Š The Future of Prescriptions is Digital! ๐Ÿ’Š✨

Say goodbye to messy handwriting and paper slips!
An E-Prescription Generator System is changing healthcare by making prescriptions smarter, safer, and faster ๐Ÿš€

Electronic Generation & Transmission – Doctors can send prescriptions directly to pharmacies with just a click!
Integrated with Patient Records – Access to medical history & allergies ensures safer prescribing ๐Ÿงพ
Improved Accuracy & Safety – No more errors from illegible handwriting, plus automatic checks for drug interactions ⚡
Faster & Efficient – Save time for doctors, pharmacies, and patients ⏱️
Patient Friendly – Get prescriptions via QR code or token & choose your pharmacy ๐Ÿฅ
Compliance & Reporting – Stay updated with healthcare regulations ๐Ÿ“Š

๐ŸŒ Examples Worldwide:
๐Ÿ‡ฌ๐Ÿ‡ง Electronic Prescription Service (UK)
๐Ÿ‡ฆ๐Ÿ‡บ Active Script List (Australia)
๐Ÿ‡ฌ๐Ÿ‡ง signatureRx (UK)

๐Ÿ‘‰ Whether integrated with EHRs or as stand-alone systems, e-prescriptions are shaping the future of healthcare ๐Ÿ’ก

๐Ÿ’ก E-Prescription = Accuracy + Safety + Speed + Patient Control ๐Ÿ’ก

Would you trust an E-Prescription system for your medicines? ๐Ÿค”๐Ÿ’ฌ

#Healthcare #DigitalHealth #Eprescription #FutureOfMedicine #PatientSafety




๐Ÿ“Œ Modules of an E-Prescription Generator System

1. ๐Ÿ–ฅ️ Electronic Generation & Transmission

  • Doctors create prescriptions digitally.

  • Prescriptions are transmitted directly to pharmacies.

  • Eliminates manual writing & reduces handwriting errors.


2. ๐Ÿ“‚ Integration with Electronic Health Records (EHR)

  • Connects seamlessly with EHR platforms.

  • Provides access to patient history, allergies, medications.

  • Supports safer and more informed prescribing.


3. ✅ Accuracy & Patient Safety

  • Prevents errors caused by illegible handwriting.

  • Automatic checks for:

    • Drug interactions ๐Ÿ’Š

    • Allergy conflicts ⚠️

  • Improves overall patient safety.


4. ⚡ Efficiency Module

  • Faster communication between doctors & pharmacies.

  • Reduces unnecessary phone calls.

  • Speeds up prescription fulfillment for patients.


5. ๐Ÿ‘จ‍⚕️ Patient Access & Control

  • Patients can receive prescriptions via:

    • QR codes

    • Digital tokens

  • Patients choose which pharmacy to fill their medication.


6. ๐Ÿ“Š Compliance & Reporting

  • Helps healthcare facilities meet legal regulations.

  • Generates reports for audits, tracking & analytics.

  • Ensures transparency & accountability.


๐Ÿ“Œ Types of E-Prescribing Systems

  1. Integrated with EHRs

    • Part of a larger hospital/clinic system.

    • Provides a complete patient health overview.

    • Best for large healthcare providers.

  2. Stand-alone Systems

    • Independent software.

    • Easier & cheaper to set up.

    • Suitable for smaller clinics but less integration.


๐Ÿ“Œ Examples of E-Prescribing Systems

  • EPS (UK) → Sends prescriptions directly to nominated pharmacies.

  • ASL (Australia) → Patients manage prescriptions via a digital list.

  • MediSecure (Australia) → Previously used national system.

  • SignatureRx (UK) → Offers private electronic prescription services.


⭐ Summary

An E-Prescription Generator System:
๐Ÿ‘‰ Improves accuracy
๐Ÿ‘‰ Ensures safety ⚕️
๐Ÿ‘‰ Boosts efficiency
๐Ÿ‘‰ Empowers patients ๐Ÿ‘ฉ‍⚕️๐Ÿ‘จ‍⚕️
๐Ÿ‘‰ Supports compliance & reporting ๐Ÿ“Š


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๐Ÿ”„ Flow of an E-Prescription Generator System

  1. Doctor Login & Patient Selection

    • Doctor securely logs into the system.

    • Selects patient profile from EHR (Electronic Health Records).

  2. Review Patient Medical History

    • Doctor checks allergies, past medications, and conditions.

    • System provides alerts for possible risks.

  3. Prescription Creation

    • Doctor enters prescription digitally.

    • System auto-suggests correct dosage, alternatives, and safety checks.

  4. Accuracy & Safety Check

    • System scans for:

      • Drug interactions ๐Ÿ’Š

      • Allergy conflicts ⚠️

      • Duplicate medicines

    • If issues found → system warns doctor.

  5. Electronic Transmission

    • Once approved, prescription is sent directly to the patient’s chosen pharmacy.

    • Copy stored in patient’s EHR for record keeping.

  6. Pharmacy Processing

    • Pharmacy receives digital prescription.

    • Prepares medicine and notifies patient when ready.

  7. Patient Access

    • Patient gets prescription via:

      • QR code ๐Ÿ“ฑ

      • Digital token or app

    • Patient can choose which pharmacy to use.

  8. Compliance & Reporting

    • System logs prescription data.

    • Generates reports for audits, regulations, and analytics.


๐Ÿ‘‰ So the flow is:
Doctor → EHR Check → Create Prescription → Safety Check → Send to Pharmacy → Patient Access → Compliance Reports


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๐Ÿค– What is a Chatbot in Healthcare?
A healthcare chatbot is an AI-powered digital assistant that helps patients and doctors with instant responses, appointment scheduling, symptom checks, medical knowledge access, and 24/7 support. ๐ŸŒ

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Building a healthcare chatbot can cost from $500 (basic) to $50,000+ (enterprise-grade with AI & ML), depending on complexity.

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✔ Provide instant responses
✔ Reduce patient wait time
✔ Offer symptom checkers & FAQs
✔ Improve patient satisfaction
✔ Lower hospital readmission rates

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Friday, August 15, 2025

Python SECTION 2 – Control Flow Conditionals – if, elif, else Loops Comprehensions

SECTION 2 – Control Flow (Weeks 2–3)


2.1 Conditionals – if, elif, else

๐Ÿ“š What: Conditionals let your program make decisions.
๐Ÿง  AI/ML Relevance:

  • Check accuracy thresholds before deployment

  • Decide model tuning steps based on loss value

  • Perform different preprocessing for text vs image datasets

๐Ÿ’ป Example: Accuracy Evaluation

accuracy = 0.88

if accuracy > 0.9:
    print("Excellent model")
elif accuracy > 0.8:
    print("Good model, but can improve")
else:
    print("Needs more training")

Nested Conditions

๐Ÿ“š What: Condition inside another condition.
๐Ÿง  AI/ML Relevance:

  • Check multiple model performance metrics

  • Apply rules for different dataset conditions

๐Ÿ’ป Example: Accuracy + Loss Check

accuracy = 0.92
loss = 0.15

if accuracy > 0.9:
    if loss < 0.2:
        print("Ready for deployment")
    else:
        print("Accuracy is fine, but reduce loss")

๐Ÿ‹ Exercise:
Write a conditional that:

  1. Checks if accuracy > 0.85

  2. If true, checks if loss < 0.15 → print "Excellent"

  3. Else print "Needs work"


2.2 Loops


For Loops

๐Ÿ“š What: Repeat code for a fixed number of iterations or over a collection.
๐Ÿง  AI/ML Relevance:

  • Iterate over datasets

  • Process batches in training

  • Apply transformations to every data point

๐Ÿ’ป Example: Processing Dataset Batches

batches = ["Batch 1", "Batch 2", "Batch 3"]

for batch in batches:
    print(f"Processing {batch}")

While Loops

๐Ÿ“š What: Repeat until a condition becomes false.
๐Ÿง  AI/ML Relevance:

  • Continue training until loss is below a threshold

  • Keep collecting data until dataset size is reached

๐Ÿ’ป Example: Training Until Loss Threshold

loss = 1.0
while loss > 0.1:
    print(f"Training... current loss = {loss}")
    loss -= 0.2
print("Training stopped: Loss threshold met")

Break & Continue

๐Ÿ“š What:

  • break → Stop the loop early

  • continue → Skip the rest of the current iteration

๐Ÿ’ป Example: Stop Training Early

losses = [0.5, 0.3, 0.15, 0.09]

for loss in losses:
    if loss < 0.1:
        print("Loss is low enough, stopping early")
        break
    print(f"Current loss: {loss}")

๐Ÿ’ป Example: Skip Bad Data

data_points = [1, None, 3, None, 5]

for point in data_points:
    if point is None:
        continue
    print(f"Processing: {point}")

range()

๐Ÿ“š What: Generates a sequence of numbers for looping.

๐Ÿ’ป Example: 5 Epochs Training

for epoch in range(1, 6):
    print(f"Training Epoch {epoch}")

Loop Over Iterables

๐Ÿ“š What: Loop directly over lists, dicts, sets, tuples.
๐Ÿ’ป Example: Looping Over Dictionary of Model Parameters

params = {"learning_rate": 0.001, "batch_size": 32, "epochs": 10}

for key, value in params.items():
    print(f"{key}: {value}")

๐Ÿ‹ Exercise:
Loop over a dictionary containing "model", "accuracy", and "loss" and print them in key: value format.


2.3 Comprehensions

๐Ÿ“š What: Shorter, cleaner way to create lists, sets, dicts from loops.
๐Ÿง  AI/ML Relevance: Quickly transform datasets without writing long loops.


List Comprehension

๐Ÿ’ป Example: Squaring All Loss Values

losses = [0.5, 0.3, 0.15]
squared_losses = [loss**2 for loss in losses]
print(squared_losses)

Tuple Comprehension (Actually Generators in Python)

๐Ÿ’ป Example: Generate Accuracy Values on the Fly

accuracies = (round(acc, 2) for acc in [0.82, 0.91, 0.88])
for acc in accuracies:
    print(acc)

Dict Comprehension

๐Ÿ’ป Example: Mapping Model Names to Accuracies

models = ["Model_A", "Model_B", "Model_C"]
accs = [0.85, 0.90, 0.88]

model_performance = {model: acc for model, acc in zip(models, accs)}
print(model_performance)

Set Comprehension

๐Ÿ’ป Example: Unique Label Extraction

labels = ["cat", "dog", "cat", "bird", "dog"]
unique_labels = {label for label in labels}
print(unique_labels)

๐Ÿ‹ Final Exercise for Section 2:
Given:

models = ["A", "B", "C"]
accuracies = [0.9, 0.85, 0.92]
  1. Create a dictionary mapping each model to its accuracy (dict comprehension)

  2. Create a list of only the accuracies above 0.88 (list comprehension)

  3. Loop over the dictionary and print "Model X has accuracy Y"




Python dictionary methods in action.

# Create a dictionary for months
months = {
    "January": 31,
    "February": 28,
    "March": 31,
    "April": 30,
    "May": 31,
    "June": 30
}

print("Original dictionary:", months)

1. Access values

print("Days in February:", months["February"])
print("Days in May (get method):", months.get("May"))
print("Non-existing month with default:", months.get("December", "Not Found"))

2. Add or update

months["July"] = 31  # Add
months["February"] = 29  # Update (Leap year)
print("After add/update:", months)

3. Remove — pop()

removed_days = months.pop("June")
print("Removed June (days =", removed_days, "):", months)

4. Remove last item — popitem()

last_item = months.popitem()
print("Removed last item:", last_item)
print("After popitem:", months)

5. Delete — del

del months["April"]
print("After deleting April:", months)

6. Clear dictionary

temp_months = months.copy()
temp_months.clear()
print("After clear:", temp_months)

7. Copy dictionary

months_copy = months.copy()
print("Copied dictionary:", months_copy)

8. Keys, Values, Items

print("Keys:", months.keys())
print("Values:", months.values())
print("Items:", months.items())

9. Loop through dictionary

for month, days in months.items():
    print(f"{month}: {days} days")

10. Check membership

print("Is 'March' in months?", "March" in months)
print("Is 'December' not in months?", "December" not in months)

11. Update with another dictionary

months.update({"August": 31, "September": 30})
print("After update:", months)

12. Dictionary comprehension

month_lengths = {month: len(month) for month in months}
print("Month name lengths:", month_lengths)

Tuple example similar to the list and set ones.
Tuples are ordered, immutable collections — so you can’t change items after creation (no append(), remove(), etc.), but you can still use many useful built-in functions.


# Create a tuple of fruits
fruits = ("apple", "banana", "cherry", "date", "elderberry", "banana")

print("Original tuple:", fruits)

1. Access elements by index

print("First fruit:", fruits[0])
print("Last fruit:", fruits[-1])

2. Slicing

print("First 3 fruits:", fruits[:3])
print("Fruits from index 2 onwards:", fruits[2:])

3. Count — count()

print("Count of 'banana':", fruits.count("banana"))

Counts occurrences of a value.


4. Index — index()

print("Index of 'cherry':", fruits.index("cherry"))

Finds the first occurrence of a value.


5. Length — len()

print("Number of fruits:", len(fruits))

6. Membership test

print("Is 'apple' in tuple?", "apple" in fruits)
print("Is 'mango' not in tuple?", "mango" not in fruits)

7. Concatenation

more_fruits = ("fig", "grape")
combined = fruits + more_fruits
print("Combined tuple:", combined)

8. Repetition

repeat_fruits = ("apple", "banana") * 2
print("Repeated tuple:", repeat_fruits)

9. Min / Max / Sum (works only if all items are same type)

numbers = (5, 2, 9, 1)
print("Min number:", min(numbers))
print("Max number:", max(numbers))
print("Sum:", sum(numbers))

10. Tuple unpacking

a, b, c, *rest = fruits
print("a:", a)
print("b:", b)
print("c:", c)
print("rest:", rest)

11. Convert to list (to modify) and back

temp_list = list(fruits)
temp_list.append("honeydew")
fruits = tuple(temp_list)
print("Modified tuple:", fruits)

Python set functions/methods with examples.
Remember: a set is unordered, does not allow duplicates, and elements must be immutable.


# Create a set of fruits
fruits = {"apple", "banana", "cherry", "date", "elderberry"}

print("Original set:", fruits)

1. Add — add()

fruits.add("fig")
print("After add:", fruits)

Adds a single item.


2. Update — update()

fruits.update(["grape", "honeydew"])
print("After update:", fruits)

Adds multiple items.


3. Remove — remove()

fruits.remove("banana")
print("After remove:", fruits)

Removes an item; error if not found.


4. Discard — discard()

fruits.discard("kiwi")  # No error if not found
print("After discard (kiwi):", fruits)

Removes item without error.


5. Pop — pop()

removed = fruits.pop()
print("Popped item:", removed)
print("After pop:", fruits)

Removes a random item (since sets are unordered).


6. Clear — clear()

temp_set = fruits.copy()
temp_set.clear()
print("After clear:", temp_set)

Removes all items.


7. Copy — copy()

fruits_copy = fruits.copy()
print("Copied set:", fruits_copy)

Shallow copy.


8. Union — union() / |

set_a = {"apple", "banana", "mango"}
set_b = {"banana", "kiwi", "orange"}
print("Union:", set_a.union(set_b))
print("Union (|):", set_a | set_b)

Combines sets, removes duplicates.


9. Intersection — intersection() / &

print("Intersection:", set_a.intersection(set_b))
print("Intersection (&):", set_a & set_b)

Items common to both sets.


10. Difference — difference() / -

print("Difference:", set_a.difference(set_b))
print("Difference (-):", set_a - set_b)

Items in the first set but not the second.


11. Symmetric Difference — symmetric_difference() / ^

print("Symmetric Difference:", set_a.symmetric_difference(set_b))
print("Symmetric Difference (^):", set_a ^ set_b)

Items in either set but not both.


12. isdisjoint()

print("Is disjoint?:", {"pineapple"} .isdisjoint({"mango"}))

True if no elements in common.


13. issubset() / issuperset()

print("Is subset?:", {"apple", "banana"}.issubset(set_a))
print("Is superset?:", set_a.issuperset({"banana"}))

14. len()

print("Number of fruits:", len(fruits))

Counts items in the set.


 Python list function / method with examples so you have a ready reference.


# Create a list of fruits
fruits = ["apple", "banana", "cherry", "date", "elderberry"]

print("Original list:", fruits)

1. Append — append()

fruits.append("fig")
print("After append:", fruits)

Adds an item to the end.


2. Insert — insert()

fruits.insert(2, "blueberry")
print("After insert:", fruits)

Inserts at a specific index.


3. Extend — extend()

fruits.extend(["grape", "honeydew"])
print("After extend:", fruits)

Adds multiple items.


4. Remove — remove()

fruits.remove("banana")
print("After remove:", fruits)

Removes first occurrence of a value.


5. Pop — pop()

popped = fruits.pop(3)
print("Popped item:", popped)
print("After pop:", fruits)

Removes by index (last item if no index given).


6. Clear — clear()

temp_list = fruits.copy()
temp_list.clear()
print("After clear:", temp_list)

Removes all items.


7. Index — index()

print("Index of cherry:", fruits.index("cherry"))

Finds the first occurrence of a value.


8. Count — count()

print("Count of 'apple':", fruits.count("apple"))

Counts how many times a value appears.


9. Sort — sort()

fruits.sort()
print("Sorted list:", fruits)

fruits.sort(reverse=True)
print("Reverse sorted list:", fruits)

Sorts in place (alphabetically by default).


10. Reverse — reverse()

fruits.reverse()
print("After reverse:", fruits)

Reverses order.


11. Copy — copy()

new_fruits = fruits.copy()
print("Copied list:", new_fruits)

Shallow copy of the list.


12. Slicing

print("First 3 fruits:", fruits[:3])
print("Last 2 fruits:", fruits[-2:])

Access sublists.


13. List Comprehension

long_names = [f for f in fruits if len(f) > 5]
print("Fruits with names longer than 5 letters:", long_names)

Create lists with conditions.


14. Length — len()

print("Number of fruits:", len(fruits))

Gets number of items.


15. Min / Max

print("Min fruit (alphabetically):", min(fruits))
print("Max fruit (alphabetically):", max(fruits))

import sys

# Join all arguments into one string

full_text = " ".join(sys.argv[1:])

# Example: full_text = "Arguments passed is <123> from command"

# Extract the part between < and >

start = full_text.find("<") + 1

end = full_text.find(">")

number = full_text[start:end]

print(number)


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Tuesday, August 12, 2025

SECTION 1 – Python Foundations Basics & Syntax Comments & Indentation Variables & Naming Constants Data Types Type Conversion Operators Strings & Methods

 


SECTION 1 – Python Foundations

1.1 Basics & Syntax

๐Ÿ“š What: The rules for writing Python code.
๐Ÿง  AI/ML Relevance: Every AI/ML model in Python — whether it’s Scikit-learn, TensorFlow, or PyTorch — uses this basic syntax.

๐Ÿ’ป Example:

# Single-line comment
print("Hello AI")  # Output: Hello AI

"""
Multi-line comment:
This program prints Hello AI
"""

๐Ÿ‹ Exercise: Print "Welcome to AI Learning" on the screen.


1.2 Comments & Indentation

๐Ÿ“š What: Comments help document your code; indentation defines code blocks.
๐Ÿง  AI/ML Relevance:

  • Comments explain data cleaning steps.

  • Indentation ensures loops and functions run correctly.

๐Ÿ’ป Example:

# Loop through training epochs
for epoch in range(3):
    print(f"Epoch {epoch+1} started")
    print("Training complete for this epoch")

๐Ÿ‹ Exercise: Write a loop that prints "Processing batch X" for batches 1–5.


1.3 Variables & Naming

๐Ÿ“š What: Variables store data in memory.
๐Ÿง  AI/ML Relevance: Store dataset paths, hyperparameters, metrics.

๐Ÿ’ป Example:

dataset_path = "data/iris.csv"   # string
learning_rate = 0.001            # float
epochs = 50                      # int
use_gpu = True                   # boolean

๐Ÿ‹ Exercise: Create variables for:

  • AI project name

  • Batch size

  • Dropout rate


1.4 Constants

๐Ÿ“š What: Fixed values you don’t change.
๐Ÿง  AI/ML Relevance: Useful for fixed paths, API keys, default configs.

๐Ÿ’ป Example:

PI = 3.14159
DEFAULT_EPOCHS = 10

1.5 Data Types

๐Ÿ“š What: Different kinds of values Python can store.

TypeExampleAI/ML Usage
intepochs = 10Number of iterations
floatlr = 0.01Learning rate
str"Neural Network"Model name
boolTrueFlags
list[0.9, 0.92, 0.95]Accuracy history
tuple(224, 224)Image size
dict{"lr": 0.001, "epochs": 10}Model configs
set{"cat", "dog"}Unique labels

๐Ÿ’ป Example:

accuracy_scores = [0.88, 0.90, 0.92]
model_config = {"layers": 3, "activation": "relu"}

๐Ÿ‹ Exercise: Create:

  • List of 3 loss values

  • Dictionary with model name & accuracy


1.6 Type Conversion

๐Ÿ“š What: Changing a variable from one type to another.
๐Ÿง  AI/ML Relevance: Convert strings from CSV into numbers for model training.

๐Ÿ’ป Example:

epochs = int("50")        # string → int
lr = float("0.01")        # string → float
model_id = str(123)       # int → string

๐Ÿ‹ Exercise: Convert "32""0.002""True" into int, float, and boolean.


1.7 Operators

๐Ÿ“š What: Symbols to perform operations.

Arithmetic:

accuracy = 90
total = 100
print("Accuracy %:", accuracy / total * 100)

Comparison:

if accuracy > 85:
    print("Good model")

Logical:

if accuracy > 85 and total == 100:
    print("Perfect data size")

๐Ÿ‹ Exercise: Check if accuracy > 0.85 and loss < 0.2.


1.8 Strings & Methods

๐Ÿ“š What: Strings hold text; methods manipulate them.
๐Ÿง  AI/ML Relevance: NLP preprocessing (lowercasing, tokenization).

๐Ÿ’ป Example:

text = "  Machine Learning is FUN!  "
print(text.lower())   # lowercase
print(text.strip())   # remove spaces
print(text.replace("FUN", "Powerful"))  # replace word

๐Ÿ‹ Exercise:
From " THIS product is Excellent!!! ":

  1. Lowercase it

  2. Remove spaces

  3. Remove !!!

  4. Replace "product" with "model"


Python 3.12, grouped logically so it’s easy to study.


1. Changing Case

MethodDescriptionExample
str.upper()Converts all characters to uppercase."hello".upper() → "HELLO"
str.lower()Converts all characters to lowercase."Hello".lower() → "hello"
str.title()Capitalizes first letter of each word."hello world".title() → "Hello World"
str.capitalize()Capitalizes first letter, rest lowercase."hELLO".capitalize() → "Hello"
str.casefold()Lowercases more aggressively (for comparisons)."รŸ".casefold() → "ss"
str.swapcase()Swaps uppercase to lowercase and vice versa."HeLLo".swapcase() → "hEllO"

2. Alignment & Padding

MethodDescriptionExample
str.center(width, fillchar)Centers the string."hi".center(6, "-") → "--hi--"
str.ljust(width, fillchar)Left-aligns with padding."hi".ljust(6, ".") → "hi...."
str.rjust(width, fillchar)Right-aligns with padding."hi".rjust(6, ".") → "....hi"
str.zfill(width)Pads with zeros on the left."42".zfill(5) → "00042"

3. Searching & Finding

MethodDescriptionExample
str.find(sub)Returns lowest index of sub or -1."hello".find("l") → 2
str.rfind(sub)Returns highest index of sub or -1."hello".rfind("l") → 3
str.index(sub)Like find() but raises ValueError if not found."hello".index("e") → 1
str.rindex(sub)Like rfind() but raises error if not found."hello".rindex("l") → 3
str.count(sub)Counts non-overlapping occurrences."banana".count("na") → 2
str.startswith(prefix)Checks if string starts with prefix."python".startswith("py") → True
str.endswith(suffix)Checks if string ends with suffix."python".endswith("on") → True

4. Checking String Content

(All return True or False.)

MethodExample
str.isalpha() → "Hello".isalpha() → True
str.isdigit() → "123".isdigit() → True
str.isalnum() → "Hello123".isalnum() → True
str.isdecimal() → "123".isdecimal() → True
str.isnumeric() → "²".isnumeric() → True
str.isspace() → " ".isspace() → True
str.islower() → "hello".islower() → True
str.isupper() → "HELLO".isupper() → True
str.istitle() → "Hello World".istitle() → True
str.isascii() → "A".isascii() → True

5. Modifying & Replacing

MethodDescriptionExample
str.replace(old, new, count)Replaces occurrences of old with new."banana".replace("na", "NA") → "baNANA"
str.strip(chars)Removes leading/trailing whitespace or given chars." hello ".strip() → "hello"
str.lstrip(chars)Removes leading chars."***hi".lstrip("*") → "hi"
str.rstrip(chars)Removes trailing chars."hi***".rstrip("*") → "hi"
str.removeprefix(prefix)Removes prefix if present."Python3".removeprefix("Python") → "3"
str.removesuffix(suffix)Removes suffix if present."file.txt".removesuffix(".txt") → "file"

6. Splitting & Joining

MethodDescriptionExample
str.split(sep)Splits into list by separator."a,b,c".split(",") → ['a', 'b', 'c']
str.rsplit(sep)Splits from right side."a,b,c".rsplit(",", 1) → ['a,b', 'c']
str.splitlines()Splits by line breaks."a\nb".splitlines() → ['a', 'b']
str.join(iterable)Joins elements with string as separator.",".join(['a', 'b']) → "a,b"
str.partition(sep)Splits into 3 parts: before, sep, after."abc:def".partition(":") → ('abc', ':', 'def')
str.rpartition(sep)Like partition() but from right side."abc:def:ghi".rpartition(":") → ('abc:def', ':', 'ghi')

7. Encoding

MethodDescriptionExample
str.encode(encoding)Encodes string into bytes."hello".encode("utf-8") → b'hello'

8. Formatting

MethodDescriptionExample
str.format()Formats placeholders {} with values."Hello {}".format("John") → "Hello John"
str.format_map(mapping)Uses a dict for formatting."{name}".format_map({"name": "John"}) → "John"
str.maketrans()Creates mapping table for translation.table = str.maketrans("abc", "123")
str.translate(table)Applies mapping table."abc".translate(table) → "123"

9. Tabs & Whitespace

MethodExample
str.expandtabs(tabsize) → "a\tb".expandtabs(4) → "a b"

✅ Tip for Learning:
To quickly see all methods in your Python, run:

print(dir(str))

and then use:

help(str.methodname)

to read details.


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Python for AI/ML – Complete Syllabus AI ML projects for beginners BIT UCSC UoM Colombo Moratuwa

Python for AI/ML – Complete Syllabus



1. Python Foundations (Weeks 1–2)

Before touching AI, get strong in Python basics.

1.1 Basics & Syntax

  • Python installation & setup (Anaconda, venv, Jupyter)

  • Comments, indentation

  • Variables & naming rules

  • Constants

1.2 Data Types & Type Conversion

  • int, float, str, bool, list, tuple, set, dict, NoneType

  • Type casting (int(), float(), str(), bool())

1.3 Operators

  • Arithmetic

  • Comparison

  • Logical

  • Assignment

  • Membership (in, not in)

  • Identity (is, is not)

1.4 Strings

  • String indexing, slicing

  • Common string methods

  • f-strings & formatting

1.5 Input/Output

  • input() & print()

  • Formatting output


2. Control Flow (Weeks 2–3)

2.1 Conditionals

  • if, elif, else

  • Nested conditions

2.2 Loops

  • for loops

  • while loops

  • break & continue

  • range()

  • Loop over iterables

2.3 Comprehensions

  • List comprehensions

  • Tuple comprehensions

  • Dict comprehensions

  • Set comprehensions

๐Ÿ’ก AI/ML relevance: Loops for batch processing, conditions for decision making in models.


3. Functions & Modules (Week 3)

3.1 Functions

  • Defining & calling functions

  • Parameters & arguments

  • Default arguments

  • Return values

  • Scope (local/global)

3.2 Modules & Packages

  • Importing built-in modules (math, random, etc.)

  • Installing & importing external packages (pip install)

  • Creating your own modules


4. Data Structures & File Handling (Week 4)

4.1 Lists, Tuples, Sets, Dictionaries

  • Creation, access, modification

  • Iteration

  • Nesting

4.2 File Handling

  • Reading/writing text files

  • CSV files

  • JSON files

  • Using with context

๐Ÿ’ก AI/ML relevance: Reading datasets, saving model results.


5. Error Handling & Debugging (Week 5)

  • try, except, finally

  • Raising exceptions

  • Common Python errors


6. Python for Data Science (Weeks 6–8)

6.1 NumPy

  • Arrays & indexing

  • Array operations & broadcasting

  • Statistical methods

6.2 Pandas

  • Series & DataFrame

  • Loading CSV, Excel, JSON

  • Selecting, filtering, sorting

  • Grouping & aggregating

  • Handling missing values

6.3 Matplotlib & Seaborn

  • Line, bar, scatter plots

  • Histograms, boxplots

  • Heatmaps

๐Ÿ’ก AI/ML relevance: Data cleaning, preprocessing, visualization.


7. Introduction to AI/ML Concepts (Weeks 9–10)

  • What is AI, ML, Deep Learning

  • Types of ML (supervised, unsupervised, reinforcement)

  • Features, labels, datasets

  • Training & testing

  • Model evaluation metrics


8. Machine Learning with Scikit-learn (Weeks 11–14)

  • Loading datasets (load_iris, load_digits, CSV files)

  • Data preprocessing

  • Train/test split

  • Classification algorithms

  • Regression algorithms

  • Clustering (KMeans)

  • Model evaluation (accuracy, precision, recall, F1-score)

  • Saving/loading models (joblib, pickle)


9. Deep Learning Foundations (Weeks 15–18)

9.1 Neural Networks Basics

  • Perceptron & activation functions

  • Forward & backward propagation

  • Loss functions

9.2 TensorFlow & Keras

  • Creating neural networks

  • Image classification (MNIST, CIFAR-10)

  • Text classification (sentiment analysis)

  • Model tuning & regularization

  • Using GPUs


10. Specialized AI/ML Topics (Weeks 19–24)

  • Natural Language Processing (NLP) – NLTK, spaCy

  • Computer Vision – OpenCV, image preprocessing

  • Time Series Analysis – ARIMA, LSTM

  • Reinforcement Learning – Basics with OpenAI Gym

  • Large Language Models (LLMs) – Hugging Face Transformers


11. AI Project Development (Weeks 25–28)

Real-world projects to combine skills:

  1. House Price Prediction (Regression)

  2. Spam Email Classifier (NLP)

  3. Image Recognition App (Computer Vision)

  4. Stock Price Prediction (Time Series)

  5. Chatbot with AI (LLM Integration)


12. Deployment & Best Practices (Weeks 29–30)

  • Saving & loading models

  • Creating APIs with Flask/FastAPI

  • Deploying to cloud (AWS, Azure, GCP, Hugging Face Spaces)

  • Version control with Git & GitHub

  • Virtual environments for deployment


13. AI Career Skills (Ongoing)

  • Writing clean, documented code

  • Using GitHub portfolio

  • Understanding ML papers

  • Participating in Kaggle competitions

  • Building LinkedIn projects




๐Ÿš€ Kickstart Your AI/ML Journey with Python! ๐Ÿค–๐Ÿ’ก

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๐Ÿ”ฅ Beginner-Friendly AI/ML Projects You Can Build:
๐ŸŽฏ Classification:

  • ๐ŸŒธ Iris Flower Classification – Identify flower species using classic datasets.

  • MNIST Digit Recognition – Classify handwritten digits with ML or neural networks.

  • ๐Ÿ“ง Spam Email Detection – Teach AI to spot unwanted emails using NLP.

  • ๐Ÿšข Titanic Survival Prediction – Predict survival chances using historical data.

๐Ÿ“Š Regression:

  • ๐Ÿ  House Price Prediction – Estimate property values with regression models.

  • ๐Ÿท Wine Quality Prediction – Predict wine ratings from chemical properties.

๐Ÿ’ฌ Other Cool Projects:

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  • ๐ŸŽฌ Recommendation System – Suggest movies or products like Netflix & Amazon.

  • ❤️ Sentiment Analysis – Detect emotions in text reviews or social media posts.

๐Ÿ›  Tools You’ll Learn:
Python, Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn, NLTK, spaCy, TensorFlow/Keras.

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