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

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

💻 Example:

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

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

🏋 Exercise: Print "Welcome to AI Learning" on the screen.


1.2 Comments & Indentation

📚 What: Comments help document your code; indentation defines code blocks.
🧠 AI/ML Relevance:

  • Comments explain data cleaning steps.

  • Indentation ensures loops and functions run correctly.

💻 Example:

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

🏋 Exercise: Write a loop that prints "Processing batch X" for batches 1–5.


1.3 Variables & Naming

📚 What: Variables store data in memory.
🧠 AI/ML Relevance: Store dataset paths, hyperparameters, metrics.

💻 Example:

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

🏋 Exercise: Create variables for:

  • AI project name

  • Batch size

  • Dropout rate


1.4 Constants

📚 What: Fixed values you don’t change.
🧠 AI/ML Relevance: Useful for fixed paths, API keys, default configs.

💻 Example:

PI = 3.14159
DEFAULT_EPOCHS = 10

1.5 Data Types

📚 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

💻 Example:

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

🏋 Exercise: Create:

  • List of 3 loss values

  • Dictionary with model name & accuracy


1.6 Type Conversion

📚 What: Changing a variable from one type to another.
🧠 AI/ML Relevance: Convert strings from CSV into numbers for model training.

💻 Example:

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

🏋 Exercise: Convert "32""0.002""True" into int, float, and boolean.


1.7 Operators

📚 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")

🏋 Exercise: Check if accuracy > 0.85 and loss < 0.2.


1.8 Strings & Methods

📚 What: Strings hold text; methods manipulate them.
🧠 AI/ML Relevance: NLP preprocessing (lowercasing, tokenization).

💻 Example:

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

🏋 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)."ß".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

💡 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

💡 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

💡 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




🚀 Kickstart Your AI/ML Journey with Python! 🤖💡

Ever dreamed of creating your own AI applications but didn’t know where to start?
Here’s your chance to dive into fun & practical beginner projects — perfect for learning real-world machine learning skills! 🖥✨

🔥 Beginner-Friendly AI/ML Projects You Can Build:
🎯 Classification:

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

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

  • 📧 Spam Email Detection – Teach AI to spot unwanted emails using NLP.

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

📊 Regression:

  • 🏠 House Price Prediction – Estimate property values with regression models.

  • 🍷 Wine Quality Prediction – Predict wine ratings from chemical properties.

💬 Other Cool Projects:

  • 🤝 Simple Chatbot – Build your first conversational AI.

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

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

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

💡 Whether you’re a student, developer, or tech enthusiast, these projects will give you hands-on experience to stand out in the AI/ML world.

📞 Call / WhatsApp: 0777337279
🎓 Learn from Sri Lanka’s best AI/ML project training programs — Online & In-Person!

Stop scrolling. Start building your AI future today! 🚀



Sunday, August 3, 2025

Hotel Management System with all features download source code BIT BSc UoM UCSC SLIIT Colombo Moratuwa PHP Python mySQL

 


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🚀 *Streamline your pharmacy. Boost efficiency. Improve patient care.*
### 🏥 **What is a PMS?**
A **Pharmacy Management System** is a smart software solution designed to **automate and optimize** the operations of your pharmacy. From managing stock to processing prescriptions, it’s your all-in-one pharmacy assistant.
💡 **Why it matters?** It saves time, reduces errors, and improves patient safety.
---
## ⚙ **Core Functions**
🔹 **Inventory Management** – Track medicines, manage reorders, avoid stockouts & overstocking.
🔹 **Prescription Management** – Process, verify, manage refills, and handle e-prescriptions with ease.
🔹 **Patient Management** – Store medical history, allergies, and medication lists for personalized care.
🔹 **POS System** – Handle sales, track revenue, and integrate with accounting tools.
🔹 **Reporting & Analytics** – Make informed decisions with real-time data insights.
🔹 **EHR Integration** – Access patient health records directly in the system.
---
## 🌟 **Benefits**
✅ **Increased Efficiency** – Automation reduces manual work & speeds up service.
✅ **Improved Patient Safety** – Accurate dosage & allergy tracking prevent medication errors.
✅ **Enhanced Patient Care** – Better communication & personalized service.
✅ **Reduced Costs** – Smarter inventory means less waste.
✅ **Better Decisions** – Use reports to spot trends and grow your pharmacy.
✅ **Regulatory Compliance** – Stay in line with healthcare standards.
---
## 🏆 **Popular PMS Solutions**
💻 **EnterpriseRx** – Powerful, clinical-grade pharmacy software.
💻 **Acnoo Pharmacy** – Web-based, with Laravel admin panel.
💻 **PioneerRx, QS/1, McKesson** – Trusted names in pharmacy tech.
---
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💊 **ඖෂධාගාර කළමනාකරණ පද්ධතිය (PMS)** 💊
ඔබගේ ඖෂධාගාර කාර්යයන් **ස්වයංක්රීය කර**, කාලය බේරගන්න, දෝෂ අඩු කරන්න, සහ රෝගීන්ට වඩා හොඳ සේවාව ලබා දෙන්න.
🔹 ගබඩා කළමනාකරණය
🔹 පිටපත් කළමනාකරණය
🔹 රෝගීන්ගේ තොරතුරු කළමනාකරණය
🔹 POS පද්ධතිය
🔹 වාර්තා සහ විශ්ලේෂණය
🔹 EHR ඒකාබද්ධ කිරීම
✅ කාර්යක්ෂමතාව වැඩි කිරීම
✅ රෝගී ආරක්ෂාව වැඩි කිරීම
✅ පිරිවැය අඩු කිරීම
---
💊 **மருந்தகம் மேலாண்மை அமைப்பு (PMS)** 💊
உங்கள் மருந்தகத்தை **தானியங்கி செய்து**, நேரத்தை மிச்சப்படுத்தி, பிழைகளை குறைத்து, நோயாளி சேவையை மேம்படுத்துங்கள்.
🔹 பங்கு மேலாண்மை
🔹 மருந்து 처방 மேலாண்மை
🔹 நோயாளர் தகவல் மேலாண்மை
🔹 விற்பனை (POS) அமைப்பு
🔹 அறிக்கைகள் & பகுப்பாய்வு
🔹 EHR ஒருங்கிணைப்பு
✅ செயல்திறன் உயர்வு
✅ நோயாளி பாதுகாப்பு மேம்பாடு
✅ செலவு குறைப்பு

-------------------------------------------------------------------------------------

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🚀✨ The Future of Learning is Here – Technology-Driven Education! ✨🚀

Education is no longer confined to chalkboards and textbooks – it’s now powered by technology that makes learning more engaging, accessible, and personalized than ever before! 💡📚

Here’s why tech-powered classrooms are transforming education:
Enhanced Learning – Interactive simulations, games, and multimedia bring lessons to life.
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21st-Century Skills – Critical thinking, problem-solving & digital literacy for the future workforce.

🌐 From interactive whiteboards to virtual reality, educational apps, and online libraries, technology is reshaping classrooms into vibrant hubs of creativity and innovation.

⚡ But let’s not forget: challenges like the digital divide, teacher training, and cost of infrastructure need to be addressed to ensure no learner is left behind.

💡 With the right tools and support, every student can thrive in a tech-driven world!

📍 At Presidency School Bangalore North, we’re committed to empowering students with the best of technology and education combined.

👉 What do you think? Should technology become a core part of every classroom? Share your thoughts below! 👇💬

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