🔥
Yangi kurs — 2026-yil

Python va
Sun’iy Intellekt

Python va Sun’iy Intellekt (AI) yo‘nalishini professional darajada o‘zbek tilida o‘rganing. 18 ta modul, 50+ amaliy mashq, real loyihalar va portfolio bilan.

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project.py — Sardor Egamberdiyev

Amalyotga asoslangan

Amalyotga asoslangan solo(yolg'iz) o'ziz uchun o'quv dasturi

🇺🇿

O'zbek tilida

Barcha darslar o‘zbek tilida, sodda va tushunarli tarzda tushuntirilgan

💻

Amaliy loyihalar

Har bir modul real loyihalar va amaliy mashqlar asosida chuqur mustahkamlanadi

📦

To'liq kod

Har bir misol uchun to‘liq ishlaydigan va bevosita nusxalab qo‘llash mumkin bo‘lgan kodlar taqdim etiladi

🚀

0 dan PRO gacha

Python asoslaridan boshlab loyihani deploy qilishgacha bo‘lgan to‘liq o‘quv yo‘l xaritasi

🔄

Doimiy yangilanish

Kurs materiallari muntazam yangilanib, eng so‘nggi talqin asosida taqdim etiladi

🤖

Sun’iy intellekt amaliyoti

Machine Learning, Deep Learning va AI modellarini yaratish, o‘qitish hamda real loyihalarda qo‘llash

📁

Portfolio loyihalar

Kurs davomida GitHub uchun tayyor real loyihalar va ishga topshirish mumkin bo‘lgan portfolio

💬

Qo'llab-quvvatlash

Telegram orqali o'quvchilar bilan to‘g‘ridan-to‘g‘ri muloqot qilish imkoniyati taqdim etiladi.

12 ta PYTHON modul — 6 ta Suniy Intellekt(AI)

Har bir modul bosqichma-bosqich, amaliy misollar bilan

MODUL 01
🐍

Python asoslari

Python — yuqori darajadagi, o‘qilishi oson va ko‘p sohalarda ishlatiladigan dasturlash tili.

  • print, input, o‘zgaruvchi
  • Data types
  • String metodlar
  • Operatorlar
  • Shart operatorlari
  • Sikllar
  • range, len
  • Basic debug
MODUL 02
📦

Collectionlar (List, Tuple, Set, Dict)

Python’da ma’lumotlarni saqlash va boshqarish uchun ishlatiladigan asosiy kolleksiya turlari bilan ishlash.

  • list, tuple, set, dict
  • indexing va slicing
  • list metodlari
  • dict metodlari
  • set operatsiyalari
  • nested structures
  • Mini-loyihalar
MODUL 03
⚙️

Funksiyalar va Clean Code

Funksiyalar yozish, kodni toza tashkil qilish va Python’dagi kuchli funksional imkoniyatlardan foydalanish.

  • def va return
  • default args, *args, **kwargs
  • scope (global/local)
  • lambda, map, filter, any, all
  • enumerate, zip
  • modul yozish va import
  • Mini-loyihalar
MODUL 04
📁

Fayl bilan ishlash va Exception

Python’da fayl o‘qish, yozish va xatolarni boshqarish (try/except) mexanizmini o‘rganamiz.

  • open(..., 'r/w/a')
  • with operatori
  • encoding='utf-8'
  • try/except/else/finally
  • Custom exception
  • Mini-loyihalar
MODUL 05
🧱

OOP (Object-Oriented Programming)

Class va objectlar yordamida dastur tuzilishini toza va kengaytiriladigan qilib yozishni o‘rganamiz.

  • class/object, __init__
  • method va attribute
  • inheritance
  • polymorphism
  • encapsulation (property)
  • dataclass
  • composition
  • Mini-loyihalar
MODUL 06
🧰

Standard Library va foydali skilllar

Python standart kutubxonalari: vaqt, fayl yo‘llari, json/csv, regex, collections, typing va logging bilan amaliy ishlash.

  • datetime
  • random
  • math
  • pathlib, os
  • json
  • csv
  • re (regex basics)
  • collections (Counter, defaultdict)
  • typing (type hints)
  • logging
  • Mini-loyihalar
MODUL 07
🔧

Git + GitHub

Git orqali versiya nazorati, branch/merge, .gitignore va GitHub’da repo/README bilan portfolio yig‘ish.

  • git init / add / commit
  • git status / log
  • branch yaratish va ishlash
  • merge va conflict yechish
  • .gitignore
  • GitHub repo yaratish va push
  • README yozish
  • Portfolio talablari
MODUL 08
🧪

Test yozish (pytest)

pytest yordamida test yozish, edge case tekshirish va kerak bo‘lsa mock ishlatish.

  • pytest o'rnatish va ishga tushirish
  • test case va edge case
  • assert
  • exceptions test qilish
  • parametrize
  • fixture basics
  • mock basics (kerak bo'lsa)
  • Mini: kalkulyator/validator testlari
MODUL 09
🗃️

SQL + Database asoslari

SQL so'rovlar, SQLite bilan ishlash va ORM tushunchasini o'rganish.

  • SQL asoslari
  • SELECT, WHERE
  • JOIN
  • GROUP BY
  • SQLite (sqlite3)
  • ORM tushunchasi
  • Mini-loyihalar
MODUL 10
🤖

Telegram Bot (Aiogram 3.x)

Aiogram 3.x yordamida Telegram bot yaratish, handlerlar, keyboard va database integratsiya.

  • Aiogram o'rnatish
  • Bot yaratish (BotFather)
  • Dispatcher va Router
  • Command handler
  • Message handler
  • Inline keyboard
  • FSM (State)
  • Database integratsiya
  • Middleware (basic)
  • Deploy tayyorlash
MODUL 11
🌐

Web (Django + DRF)

Django’da web ilova qurish: models/migrations, auth/forms, templates/static/media va REST API (DRF).

  • Django o'rnatish va project/app
  • URLs + Views
  • Models
  • Migrations
  • Admin panel
  • Auth (login/register/logout)
  • Forms (Form/ModelForm)
  • Templates (inheritance, context)
  • Static & Media
  • DRF: Serializer
  • DRF: ViewSet + Router
  • DRF: Permissions
  • DRF: Pagination
  • Mini: Blog / Shop / CRM
MODUL 12
🐳

Docker (Production Ready Deployment)

Django, Telegram Bot yoki API loyihalarni Docker yordamida konteynerlashtirish va production darajada ishga tushirish.

  • Docker nima?
  • Docker install
  • Docker image vs container
  • Dockerfile yozish
  • requirements.txt bilan build
  • docker build / run
  • docker-compose
  • Environment variables (.env)
  • Volume va persistent data
  • Django + PostgreSQL docker-compose
  • Bot + SQLite docker
  • Production best practices
PYTHON AI (MODUL 01)
📊

Python + Data Foundations

AI/ML uchun kerak bo‘ladigan Python data-asoslari: Numpy, Pandas, vizualizatsiya, Jupyter va CSV/JSON bilan ishlash.

  • Jupyter Notebook (workflow)
  • NumPy: array, vector, matrix
  • Pandas: DataFrame, filtering
  • Pandas: groupby + aggregation
  • Missing values (cleaning)
  • CSV o‘qish/yozish
  • JSON o‘qish/yozish
  • Matplotlib: line/bar/hist basics
  • Seaborn: quick viz basics
  • Mini-loyiha: report pipeline
PYTHON AI (MODUL 02)
🧠

Math + ML Foundations

AI/ML uchun matematika va fundamental ML tushunchalar: linear algebra, probability, loss/gradient, overfitting, bias-variance va train/test split.

  • Linear algebra: vector/matrix
  • Dot product, matrix multiplication
  • Probability basics
  • Loss function (MSE)
  • Gradient tushunchasi
  • Gradient Descent
  • Train/Test split
  • Overfitting / Underfitting
  • Bias-Variance tradeoff
  • Mini: manual linear regression (NumPy)
PYTHON (MODUL 03)
🤖

Machine Learning (Scikit-Learn)

Regression, Classification, Feature Engineering, Model Evaluation va Hyperparameter tuning (GridSearch) bilan real ML pipeline qurish.

  • Linear Regression
  • Ridge Regression
  • Logistic Regression
  • KNN
  • Decision Tree
  • Random Forest
  • Feature Engineering
  • Scaling (StandardScaler)
  • Encoding (OneHotEncoder)
  • Model evaluation (Accuracy, F1, ROC)
  • Cross Validation
  • GridSearchCV
  • Pipeline
  • Mini-loyiha
PYTHON (MODUL 04)
🔥

Deep Learning (PyTorch)

Neural network, activation, backpropagation, CNN asoslari va MNIST digit classifier qurish.

  • Neural Network tushunchasi
  • Activation functions (ReLU, Sigmoid, Softmax)
  • Loss function
  • Backpropagation
  • Optimizer (SGD, Adam)
  • Training loop
  • CNN basics
  • Model saving/loading
  • Mini-loyiha: MNIST classifier
PYTHON (MODUL 05)
🗣️

NLP + LLM (Embeddings, Prompt, RAG)

Matnni qayta ishlash (NLP), TF-IDF, sentiment analysis, embeddings, OpenAI API, prompt engineering va RAG orqali AI assistant qurish.

  • Text preprocessing
  • TF-IDF
  • Sentiment analysis
  • Embeddings
  • Prompt engineering
  • OpenAI API (chat + embeddings konsepti)
  • RAG (vector search: chunking + retrieval)
  • Mini: PDF Q&A bot yoki Telegram AI assistant
PYTHON (MODUL 06)
🚀

AI Production & Deploy

ML modelni FastAPI orqali API qilish, Docker bilan konteynerlashtirish, versioning, logging, monitoring va HuggingFace deploy.

  • FastAPI bilan model API
  • Request/Response schema (Pydantic)
  • Model loading (pickle/joblib/torch)
  • Docker (Dockerfile, build, run)
  • Model versioning (v1, v2)
  • Logging (production logging)
  • Monitoring basics
  • HuggingFace deploy
  • Basic MLOps tushuncha
  • Mini-loyiha: Model → API → Docker → Deploy

Professional kod namunalari

Real dastrulardan olingan kod namunalar

assistant.py
# CHANGE = your_ai_name

import datetime
import random

import pyautogui
import speech_recognition as sr
import os
import webbrowser
import threading
import time

WAKE_WORDS = ["hey your_ai_name", "heyyour_ai_name", "your_ai_name"]
history = []
scrolling = False
waiting_message_for = None


def speak(text):
    print("your_ai_name:", text)

    text = text.replace(":", " ")
    text = text.replace(".", " . ")
    text = text.replace(",", " , ")

    os.system(f'say -r 185 -v Samantha "{text}"')


def listen(timeout=5):
    r = sr.Recognizer()
    with sr.Microphone() as source:
        print("🎤 Listening...")
        r.adjust_for_ambient_noise(source, duration=0.5)
        audio = r.listen(source, phrase_time_limit=timeout)

    try:
        text = r.recognize_google(audio, language="en-US")
        print("You:", text)
        return text.lower()
    except sr.UnknownValueError:
        return ""
    except sr.RequestError:
        speak("There is a problem with the internet.")
        return ""


def scroll_worker(direction):
    global scrolling

    key = 125 if direction == "down" else 126
    start = time.time()

    while scrolling:
        if time.time() - start > 5:
            scrolling = False
            break

        os.system(f"""
        osascript -e 'tell application "Google Chrome" to activate' \
                  -e 'tell application "System Events" to key code {key}'
        """)
        time.sleep(0.5)


def greet():
    answers = [
        "Hello. I'm listening.",
        "Hi. How can I help you?",
        "Go ahead, I'm listening."
    ]
    return random.choice(answers)


def get_time():
    now = datetime.datetime.now()
    return f"The time is {now.hour} {now.minute}."


def help_menu():
    return "I can greet you, tell the time, and stop when you say exit."


def fallback():
    return "Sorry, I didn't understand."


def detect_intent(text: str):
    if any(w in text for w in ["hello", "hi"]):
        return "greet"

    if any(w in text for w in ["time", "clock", "hour"]):
        return "time"

    if "help" in text:
        return "help"

    if "exit" in text or "stop go" in text:
        return "exit"

    if "youtube" in text:
        return "youtube"

    if "google" in text:
        return "google"

    if "telegram" in text:
        return "telegram"

    if "back" in text:
        return "back"

    if "scroll up" in text:
        return "scroll_up"

    if "scroll down" in text:
        return "scroll_down"

    if "scroll stop" in text or "stop scrolling" in text:
        return "scroll_stop"

    if "send" in text and "message" in text and "to" in text:
        return "send_message"

    return "unknown"


def handle_intent(intent, user_text):
    global scrolling
    global waiting_message_for

    if intent == "greet":
        return greet()

    if intent == "time":
        return get_time()

    if intent == "help":
        return help_menu()

    if intent == "youtube":
        url = "https://youtube.com"
        history.append(url)
        webbrowser.open(url)
        return "Opening YouTube."

    if intent == "google":
        url = "https://google.com"
        history.append(url)
        webbrowser.open(url)
        return "Opening Google."

    if intent == "telegram":
        url = "https://web.telegram.org"
        history.append(url)
        webbrowser.open(url)
        return "Opening Telegram."

    if intent == "back":
        os.system("""
        osascript -e 'tell application "Google Chrome" to activate' \
                  -e 'tell application "System Events" to keystroke "w" using command down'
        """)
        return "Closing current tab."

    if intent == "scroll_down":
        if not scrolling:
            scrolling = True
            threading.Thread(target=scroll_worker, args=("down",), daemon=True).start()
        return "Scrolling down."

    if intent == "scroll_up":
        if not scrolling:
            scrolling = True
            threading.Thread(target=scroll_worker, args=("up",), daemon=True).start()
        return "Scrolling up."

    if intent == "scroll_stop":
        scrolling = False
        return "Stopped scrolling."

    if intent == "send_message":
        try:
            name = user_text.split("to", 1)[1].strip().split()[0]
            waiting_message_for = name
            return f"What should I write to {name}?"
        except:
            return "Whom should I send it to?"

    return fallback()


def main():
    global waiting_message_for
    speak("your_ai_name listened")

    active = False

    while True:
        msg = listen()

        if not msg:
            continue

        if not active:
            if any(w in msg for w in WAKE_WORDS):
                active = True
                speak("I'm listening")
            continue

        if waiting_message_for:
            name = waiting_message_for
            message = msg

            speak(f"Sending message to {name}")

            os.system("open -a 'Telegram'")
            time.sleep(3)

            pyautogui.hotkey("command", "k")
            time.sleep(1)

            pyautogui.press("enter")
            time.sleep(2)

            for _ in range(8):
                pyautogui.press("tab")
                time.sleep(0.2)

            pyautogui.write(message, interval=0.03)
            time.sleep(0.3)

            pyautogui.press("enter")

            waiting_message_for = None
            speak("Message sent.")
            continue

        intent = detect_intent(msg)

        if intent == "exit":
            speak("Goodbye!")
            active = False
            continue

        speak(handle_intent(intent, msg))


if __name__ == "__main__":
    main()

Voice AI Assistant — Wake word, intents, browser, scrolling, Telegram automation

Speech Recognition + Mac TTS + browser control + pyautogui orqali oddiy voice assistant. Intent detection: time/help/youtube/google/telegram/scroll/send message.

O'quv dasuri muallifi haqida

⭐ Python AI Expert
🐍 Python Expert
🤖 Yahshi Inson

Sardor Egamberdiyev

Software Engineer

Men Python backend va AI yo‘nalishida faoliyat yurituvchi software engineer sifatida production darajadagi tizimlarni ishlab chiqish va qo‘llab-quvvatlash bo‘yicha amaliy tajribaga egaman. Faoliyatim davomida Kun.uz, Payme, Uzcard va Proweb kabi yirik platformalar bilan bog‘liq loyihalarda ishlash orqali real biznes jarayonlari va yuqori yuklama ostidagi tizimlar bilan ishlash tajribasini orttirganman.

2500+
O'quvchilar
300+
Loyihalar
6+
Yillik tajriba
Python OOP Async Programming Aiogram 3.x Telegram Bot API Django Django REST Framework FastAPI Machine Learning Scikit-Learn PyTorch NLP LLM Integration OpenAI API PostgreSQL SQLite Redis Docker Linux Nginx Git GitHub REST API Design JSON / CSV Unit Testing (pytest)
🚀

O'quv dasturi

Ushbu o‘quv dasturini o‘z tajribam va amaliy bilimlarim asosida ishlab chiqdim. Agar bu dastur sizga foydali bo‘lgan bo‘lsa, bundan juda xursand bo‘laman. Savollaringiz yoki takliflaringiz bo‘lsa, bemalol Telegram sahifamga o‘ting va istalgan post ostida izoh qoldiring — albatta javob beraman.