Data Science & Machine Learning
Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free For collaborations: @love_data
إظهار المزيد📈 نظرة تحليلية على قناة تيليجرام Data Science & Machine Learning
تُعد قناة Data Science & Machine Learning (@datasciencefun) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 76 441 مشتركاً، محتلاً المرتبة 2 066 في فئة التعليم والمرتبة 4 109 في منطقة الهند.
📊 مؤشرات الجمهور والحراك
منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 76 441 مشتركاً.
بحسب آخر البيانات بتاريخ 13 يوليو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار 721، وفي آخر 24 ساعة بمقدار 38، مع بقاء الوصول العام مرتفعاً.
- حالة التحقق: غير موثّقة
- معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 2.82%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 1.30% من ردود الفعل نسبةً إلى إجمالي المشتركين.
- وصول المنشورات: يحصل كل منشور على متوسط 2 156 مشاهدة. وخلال اليوم الأول يجمع عادةً 992 مشاهدة.
- التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 3.
- الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل learning, accuracy, distribution, panda, dataset.
📝 الوصف وسياسة المحتوى
يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
“Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free
For collaborations: @love_data”
بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 14 يوليو, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التعليم.
if age >= 18:
print("Eligible")
❌ Incorrect Indentation
if age >= 18:
print("Eligible")
Python requires proper indentation.
Correct:
if age >= 18:
print("Eligible")
🔹 11. Real-World Data Science Example
prediction = 0.82
if prediction >= 0.5:
print("Spam Email")
else:
print("Not Spam")
Many Machine Learning classification models use similar logic to convert prediction probabilities into categories.
🎯 Practice Questions
1. Check whether a number is positive or negative.
2. Check whether a person is eligible to vote.
3. Create a grading system using "if...elif...else".
4. Check whether a number is even or odd.
5. Determine the largest of three numbers.
🎯 Key Takeaways
✅ Use "if" to execute code when a condition is True.
✅ Use "else" to execute code when the condition is False.
✅ Use "elif" to check multiple conditions.
✅ Nested "if" statements allow more complex decision-making.
✅ Logical operators (and, or, not) help combine conditions.
✅ The ternary operator provides a concise way to write simple "if...else" statements.
Conditional statements are the foundation of decision-making in Python and are widely used in automation, data analysis, machine learning, and AI applications.
Double Tap ❤️ For Part-5
-----
1.35 ₽ · /balance_helpif condition:
# Code to execute
Example
age = 20
if age >= 18:
print("Eligible to vote")
Output
Eligible to vote
🔹 3. The "if...else" Statement
Use "else" when you want to execute another block if the condition is False.
Example
age = 16
if age >= 18:
print("Eligible to vote")
else:
print("Not eligible to vote")
Output
Not eligible to vote
🔹 4. The "if...elif...else" Statement ⭐
Use "elif" when you need to check multiple conditions.
Example
marks = 85
if marks >= 90:
print("Grade A")
elif marks >= 75:
print("Grade B")
elif marks >= 60:
print("Grade C")
else:
print("Grade D")
Output
Grade B
Python checks conditions from top to bottom and executes the first condition that is True.
🔹 5. Nested "if" Statements
You can place one "if" statement inside another.
Example
age = 25
citizen = True
if age >= 18:
if citizen:
print("Eligible to vote")
Output
Eligible to vote
🔹 6. Using Logical Operators
Conditional statements often use logical operators.
"and"
age = 25
if age >= 18 and age <= 60:
print("Working Age")
"or"
marks = 35
if marks >= 40 or marks == 35:
print("Eligible for Grace Marks")
"not"
is_holiday = False
if not is_holiday:
print("Go to Office")
🔹 7. Checking Multiple Conditions
salary = 60000
experience = 4
if salary > 50000 and experience >= 3:
print("Eligible for Promotion")
else:
print("Not Eligible")
🔹 8. Ternary Operator ⭐
A shorter way to write an "if...else" statement.
Syntax
value_if_true if condition else value_if_false
Example
age = 20
status = "Adult" if age >= 18 else "Minor"
print(status)
Output
Adult
🔹 9. Real-World Example
temperature = 38
if temperature > 35:
print("It's a hot day.")
elif temperature >= 20:
print("Weather is pleasant.")
else:
print("It's cold.")
🔹 10. Common Mistakes
❌ Missing Colon
if age >= 18
print("Eligible")
This gives a SyntaxError.
Correct:num1 = int(input("Enter first number: "))
num2 = int(input("Enter second number: "))
print(num1 + num2)
Output:
30
🔹 12. Real-World Example
salary = float(input("Enter your monthly salary: "))
annual_salary = salary * 12
print(f"Your annual salary is {annual_salary}")
🎯 Practice Questions
1. Take your name as input and print a welcome message.
2. Take two integers as input and print their sum.
3. Take a student's marks as input and print them using an f-string.
4. Take the radius of a circle as input and calculate the area.
5. Take your birth year as input and calculate your approximate age.
🎯 Key Takeaways
✅ Use print() to display output
✅ Use input() to accept user input
✅ input() always returns a string
✅ Convert input using int() or float() when needed
✅ Use f-strings for clean and readable output formatting
Double Tap ❤️ For Moreprint("Hello, Data Science!")
Output
Hello, Data Science!
🔹 3. Printing Variables
You can print variables along with text.
name = "Deepak"
print(name)
Output:
Deepak
Or:
name = "Deepak"
print("Welcome", name)
Output:
Welcome Deepak
🔹 4. Taking User Input
Python uses the input() function to receive input from users.
name = input("Enter your name: ")
print("Hello", name)
Example Output:
Enter your name: Deepak
Hello Deepak
🔹 5. Important Note ⭐
The input() function always returns a string, even if the user enters a number.
age = input("Enter age: ")
print(type(age))
Output:
<class 'str'>
🔹 6. Converting Input to Integer
To perform mathematical operations, convert the input using int().
age = int(input("Enter your age: "))
print(age + 5)
Example:
Enter your age: 25
30
🔹 7. Taking Decimal Input
Use float() for decimal numbers.
price = float(input("Enter price: "))
print(price)
🔹 8. Taking Multiple Inputs
You can take multiple inputs in a single line.
name, city = input("Enter your name and city: ").split()
print(name)
print(city)
Example Input:
Deepak Mumbai
Output:
Deepak
Mumbai
🔹 9. Formatting Output
Using f-Strings ⭐ Recommended
name = "Deepak"
age = 25
print(f"My name is {name} and I am {age} years old.")
Output:
My name is Deepak and I am 25 years old.
Using .format()
name = "Deepak"
print("Welcome {}".format(name))
🔹 10. Example Program
name = input("Enter your name: ")
age = int(input("Enter your age: "))
print(f"Hello {name}")
print(f"Next year you will be {age + 1} years old.")
Example Output:
Enter your name: Deepak
Enter your age: 25
Hello Deepak
Next year you will be 26 years old.
🔹 11. Common Mistake
num1 = input("Enter first number: ")
num2 = input("Enter second number: ")
print(num1 + num2)
Input:
10
20
Output:
1020
Why?
Because both values are strings.
Correct way:result = 10 + 5 * 2
print(result)
Output:
20
Multiplication is performed before addition.
Use parentheses to change the order.
result = (10 + 5) * 2
print(result)
Output:
30
🔹 10. Real-World Example
salary = 60000
bonus = 5000
total_salary = salary + bonus
is_high_salary = total_salary > 50000
print(total_salary)
print(is_high_salary)
Output:
65000
True
🎯 Key Takeaways
✅ Operators perform calculations and comparisons.
✅ Arithmetic operators are used for mathematical operations.
✅ Comparison operators return True or False.
✅ Logical operators help combine multiple conditions.
✅ Membership operators check if a value exists in a sequence.
✅ Identity operators check whether two variables refer to the same object.
Double Tap ❤️ For Morea = 10
b = 5
print(a + b)
Output:
15
Here, "+" is an operator that adds two numbers.
🔹 2. Types of Operators in Python
Python has several types of operators:
✅ Arithmetic Operators
✅ Comparison Operators
✅ Assignment Operators
✅ Logical Operators
✅ Membership Operators
✅ Identity Operators
🔹 3. Arithmetic Operators ⭐
Used for mathematical calculations.
Operators:
• ** + Addition**: 10 + 5 = 15
• - Subtraction: 10 - 5 = 5
• ** Multiplication*: 10 * 5 = 50
• / Division: 10 / 5 = 2.0
• // Floor Division: 10 // 3 = 3
• % Modulus (Remainder): 10 % 3 = 1
• ** Exponent: 2 ** 3 = 8
Example:
a = 10
b = 3
print(a + b)
print(a - b)
print(a * b)
print(a / b)
print(a // b)
print(a % b)
print(a ** b)
🔹 4. Comparison Operators ⭐
Used to compare two values. The result is always True or False.
Operators:
• == Equal to
• != Not Equal to
• > Greater than
• < Less than
• >= Greater than or Equal to
• <= Less than or Equal to
Example:
x = 20
y = 10
print(x > y)
print(x == y)
print(x != y)
Output:
True
False
True
🔹 5. Assignment Operators
Used to assign values to variables.
x = 10
x += 5
print(x)
Output:
15
Other assignment operators:
x -= 2
x *= 3
x /= 2
🔹 6. Logical Operators ⭐
Used to combine multiple conditions.
and
Returns True only if both conditions are True.
age = 25
print(age > 18 and age < 30)
Output:
True
or
Returns True if at least one condition is True.
print(age < 18 or age < 30)
Output:
True
not
Reverses the result.
print(not(age > 18))
Output:
False
🔹 7. Membership Operators
Used to check whether a value exists in a sequence.
in
fruits = ["Apple", "Banana", "Mango"]
print("Apple" in fruits)
Output:
True
not in
print("Orange" not in fruits)
Output:
True
🔹 8. Identity Operators
Used to check whether two variables refer to the same object.
is
a = [1, 2]
b = a
print(a is b)
Output:
True
is not
x = [1, 2]
y = [1, 2]
print(x is not y)
Output:
True
🔹 9. Operator Precedence
Python follows the PEMDAS/BODMAS rule while evaluating expressions.
Example:The GigaChat team has released GigaChat 3.5 Ultra as open source—a new 432B model under the MIT license. This is the first open-source hybrid of GatedDeltaNet and MLA scaled to hundreds of billions of parameters, featuring a proprietary training recipe we refined through more than 1,500 experiments. The model has grown in terms of code, mathematics, agent scenarios, and application domains—yet it’s 40% smaller than GigaChat 3.1 Ultra.What’s inside: 🔘A proprietary hybrid MLA + Gated DeltaNet architecture with a dedicated stabilization framework, without which this hybrid setup would not train reliably at this scale; 🔘 Gated Attention: the model can locally down-weight overly strong signals from the attention layer; 🔘GatedNorm: normalization with an explicit gate that controls signal magnitude across features; 🔘Approximately 4x lower KV cache per token: with the same memory budget, the model can support 2.14x longer context and deliver a 20% throughput increase under load; 🔘Two MTP heads, enabling up to 2.2x faster generation; 🔘FP8 across all training stages with no quality degradation compared with bf16, enabled by custom Triton and CUDA kernels; 🔘A new online RL stage after SFT and DPO. Results: 🔘 GigaChat-3.5-Ultra-Base outperforms DeepSeek V3.2 Exp Base and DeepSeek V4 Flash Base on average across a set of general, math, and code benchmarks: 🔘 GigaChat-3.5-Ultra-Instruct is comparable to DeepSeek V3.2 in terms of average score, despite having half the size; 🔘 According to the MiniMax-M2.7 LLM judge, the average win rate against GigaChat 3.1 Ultra is 75.9%, and against GPT-5 is 68.7%.
The entire stack — data (our own LLM-filtered Common Crawl, 600+ programming languages in the code), architecture, training methodology, and infrastructure — was built end-to-end by GigaChat team.➡️ HuggingFace
