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
نمایش بیشتر📈 تحلیل کانال تلگرام Data Science & Machine Learning
کانال Data Science & Machine Learning (@datasciencefun) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 76 441 مشترک است و جایگاه 2 066 را در دسته آموزش و رتبه 4 109 را در منطقه الهند دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 76 441 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 13 ژوئیه, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 721 و در ۲۴ ساعت گذشته برابر 38 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 2.82% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 1.30% واکنش نسبت به کل مشترکان کسب میکند.
- دسترسی پستها: هر پست به طور میانگین 2 156 بازدید دریافت میکند. در اولین روز معمولاً 992 بازدید جمعآوری میشود.
- واکنشها و تعامل: مخاطبان بهطور فعال حمایت میکنند؛ میانگین واکنش به هر پست 3 است.
- علایق موضوعی: محتوا بر موضوعات کلیدی مانند learning, accuracy, distribution, panda, dataset تمرکز دارد.
📝 توضیح و سیاست محتوایی
نویسنده این فضا را محل بیان دیدگاههای شخصی توصیف میکند:
“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”
به لطف بهروزرسانیهای پرتکرار (آخرین داده در تاریخ 14 ژوئیه, 2026)، کانال همواره بهروز و دارای دسترسی بالاست. تحلیلها نشان میدهد مخاطبان بهطور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته آموزش تبدیل کردهاند.
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
