Machine Learning with Python
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho
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Channel Machine Learning with Python (@codeprogrammer) in the English language segment is an active participant. Currently, the community unites 68 102 subscribers, ranking 2 372 in the Education category and 4 808 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 68 102 subscribers.
According to the latest data from 27 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 112 over the last 30 days and by 8 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 4.52%. Within the first 24 hours after publication, content typically collects 1.90% reactions from the total number of subscribers.
- Post reach: On average, each post receives 3 077 views. Within the first day, a publication typically gains 1 291 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 5.
- Thematic interests: Content is focused on key topics such as insidead, learning, degree, evaluation, algorithm.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Thanks to the high frequency of updates (latest data received on 28 August, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.
https://anthropic.skilljar.com/claude-with-the-anthropic-api▶️ Introduction to Model Context Protocol (MCP)
https://anthropic.skilljar.com/introduction-to-model-context-protocol▶️ Claude in Amazon Bedrock
https://anthropic.skilljar.com/claude-in-amazon-bedrock▶️ Claude in Google Cloud (Vertex AI)
https://anthropic.skilljar.com/claude-with-google-vertex▶️ Advanced MCP
https://anthropic.skilljar.com/model-context-protocol-advanced-topics▶️ Claude Code in Practice
https://anthropic.skilljar.com/claude-code-in-actiontags: #courses #ai ➡ https://t.me/CodeProgrammer
if obj == None, use if obj is None
In Python, when you write:
obj == None
you're not directly checking if obj is the value None. Instead, you're asking if the object is equal to None.
Yes, in many cases, the result will be the same as for the code:
obj is None
But the behavior of these two variants is different, and this difference is important.
When you use:
obj == None
Python calls the __eq__ method on the object. That is, the object itself decides what it means to be "equal to None". And this method can be overridden.
If obj is an instance of a class in which __eq__ is implemented so that when compared with None, it returns True (even if the object is not actually None), then obj == None may mistakenly give True.
Example:
class Weird:
def __eq__(self, other):
return True # Always asserts that it's equal
obj = Weird()
print(obj == None) # True
print(obj is None) # False
Here, it can be seen that obj == None returns True due to the custom behaeqf the __eq__ operator in the class.
Therefore, when using obj == None, the result is not always predictable.
On the other hand, when you write:
obj is None
you're using the is operator, which cannot be overridden. This means that the result will always be the same and predictable.
The is operator checks the identity of objects, that is, whether two references point to the same object. Since None is a singleton (the only instance), obj is None is the correct and most efficient way to perform such a check.
❤️ Therefore, it is always recommended, and this is best practice, to use obj is None instead of obj == None for predictability and efficiency.
👉 https://t.me/DataScienceQ