What are the main principles of writing unit tests?
Answer:
A good unit test reads like a specification: a clear name, minimal unnecessary preparation, a clear reason for failure. It should be fast and not access the network, database, or file system. If a dependency is unavoidable, it is replaced with a stub or mocks, checking either the result or the interaction contract, but not both at once unnecessarily.
tags: #interview
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Why shouldn't the
is operator be used to compare strings and numbers?Answer:
For comparing content, you need to use ==, otherwise the result may be unpredictable and depend on the interpreter's implementation.
tags: #interview
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Forwarded from Learn Python Hub
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
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⚡️ Want to build your own personal JARVIS, but Clawdbot seems too complicated to deploy and understand?
Try - nanobot: an ultra-lightweight version of Clawdbot (99% simpler), which sets up a personal AI assistant in less than a minute.
⚡️ The basic functionality is just ~4,000 lines of Python - compared to over 400k lines in Clawdbot.
Key features of nanobot:
🪶 Ultra-lightweight - ~4,000 lines of code, just the core without overload.
🔬 Convenient for research - clean, understandable code, easy to modify and expand.
⚡️ Fast - minimal size = quick start, fewer resources, rapid iterations.
💎 Simple to use - one launch, and the assistant is already working.
What nanobot can do:
📈 24/7 real-time market analysis - monitoring and insights.
🚀 Full-stack software engineer - assistance in development from idea to production.
📅 Smart routine manager - helps organize the day and tasks.
📚 Personal knowledge assistant - storage, search, and work with information.
If you want your own AI agent without a monstrous infrastructure - this is exactly the start you need.
🔗 Open Source: https://github.com/HKUDS/nanobot
🔗Video: https://www.youtube.com/shorts/Wx2RBCnl5nU
#Clawdbot #AIAssistant #Agents
Try - nanobot: an ultra-lightweight version of Clawdbot (99% simpler), which sets up a personal AI assistant in less than a minute.
⚡️ The basic functionality is just ~4,000 lines of Python - compared to over 400k lines in Clawdbot.
Key features of nanobot:
🪶 Ultra-lightweight - ~4,000 lines of code, just the core without overload.
🔬 Convenient for research - clean, understandable code, easy to modify and expand.
⚡️ Fast - minimal size = quick start, fewer resources, rapid iterations.
💎 Simple to use - one launch, and the assistant is already working.
What nanobot can do:
📈 24/7 real-time market analysis - monitoring and insights.
🚀 Full-stack software engineer - assistance in development from idea to production.
📅 Smart routine manager - helps organize the day and tasks.
📚 Personal knowledge assistant - storage, search, and work with information.
If you want your own AI agent without a monstrous infrastructure - this is exactly the start you need.
🔗 Open Source: https://github.com/HKUDS/nanobot
🔗Video: https://www.youtube.com/shorts/Wx2RBCnl5nU
#Clawdbot #AIAssistant #Agents
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Forwarded from Machine Learning
🚀 Machine Learning Workflow: Step-by-Step Breakdown
Understanding the ML pipeline is essential to build scalable, production-grade models.
👉 Initial Dataset
Start with raw data. Apply cleaning, curation, and drop irrelevant or redundant features.
Example: Drop constant features or remove columns with 90% missing values.
👉 Exploratory Data Analysis (EDA)
Use mean, median, standard deviation, correlation, and missing value checks.
Techniques like PCA and LDA help with dimensionality reduction.
Example: Use PCA to reduce 50 features down to 10 while retaining 95% variance.
👉 Input Variables
Structured table with features like ID, Age, Income, Loan Status, etc.
Ensure numeric encoding and feature engineering are complete before training.
👉 Processed Dataset
Split the data into training (70%) and testing (30%) sets.
Example: Stratified sampling ensures target distribution consistency.
👉 Learning Algorithms
Apply algorithms like SVM, Logistic Regression, KNN, Decision Trees, or Ensemble models like Random Forest and Gradient Boosting.
Example: Use Random Forest to capture non-linear interactions in tabular data.
👉 Hyperparameter Optimization
Tune parameters using Grid Search or Random Search for better performance.
Example: Optimize max_depth and n_estimators in Gradient Boosting.
👉 Feature Selection
Use model-based importance ranking (e.g., from Random Forest) to remove noisy or irrelevant features.
Example: Drop features with zero importance to reduce overfitting.
👉 Model Training and Validation
Use cross-validation to evaluate generalization. Train final model on full training set.
Example: 5-fold cross-validation for reliable performance metrics.
👉 Model Evaluation
Use task-specific metrics:
- Classification – MCC, Sensitivity, Specificity, Accuracy
- Regression – RMSE, R², MSE
Example: For imbalanced classes, prefer MCC over simple accuracy.
💡 This workflow ensures models are robust, interpretable, and ready for deployment in real-world applications.
https://news.1rj.ru/str/DataScienceM
Understanding the ML pipeline is essential to build scalable, production-grade models.
👉 Initial Dataset
Start with raw data. Apply cleaning, curation, and drop irrelevant or redundant features.
Example: Drop constant features or remove columns with 90% missing values.
👉 Exploratory Data Analysis (EDA)
Use mean, median, standard deviation, correlation, and missing value checks.
Techniques like PCA and LDA help with dimensionality reduction.
Example: Use PCA to reduce 50 features down to 10 while retaining 95% variance.
👉 Input Variables
Structured table with features like ID, Age, Income, Loan Status, etc.
Ensure numeric encoding and feature engineering are complete before training.
👉 Processed Dataset
Split the data into training (70%) and testing (30%) sets.
Example: Stratified sampling ensures target distribution consistency.
👉 Learning Algorithms
Apply algorithms like SVM, Logistic Regression, KNN, Decision Trees, or Ensemble models like Random Forest and Gradient Boosting.
Example: Use Random Forest to capture non-linear interactions in tabular data.
👉 Hyperparameter Optimization
Tune parameters using Grid Search or Random Search for better performance.
Example: Optimize max_depth and n_estimators in Gradient Boosting.
👉 Feature Selection
Use model-based importance ranking (e.g., from Random Forest) to remove noisy or irrelevant features.
Example: Drop features with zero importance to reduce overfitting.
👉 Model Training and Validation
Use cross-validation to evaluate generalization. Train final model on full training set.
Example: 5-fold cross-validation for reliable performance metrics.
👉 Model Evaluation
Use task-specific metrics:
- Classification – MCC, Sensitivity, Specificity, Accuracy
- Regression – RMSE, R², MSE
Example: For imbalanced classes, prefer MCC over simple accuracy.
💡 This workflow ensures models are robust, interpretable, and ready for deployment in real-world applications.
https://news.1rj.ru/str/DataScienceM
❤3
How do unit tests differ from integration tests?
Answer:
Integration tests check the joint operation of several system components. They run code with real or almost real dependencies and answer the question of whether the application parts interact correctly with each other. Such tests are slower, more difficult to set up, but they allow to identify problems at the boundaries between modules.
tags: #interview
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PyData Careers
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
Admin: @HusseinSheikho || @Hussein_Sheikho
Admin: @HusseinSheikho || @Hussein_Sheikho
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Forwarded from Machine Learning with Python
A full-fledged educational course has been published on the university's website: 24 lectures, practical tasks, homework assignments, and a collection of materials for self-study.
The program includes modern neural network architectures, generative models, transformers, inference, and other key topics.
A great opportunity to study deep learning based on the structure of a top university, free of charge and without simplifications — let's learn here.
https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/resources/lecture-videos/
tags: #python #deeplearning
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What is Meta in Django and why is it needed?
Answer:
Django uses metaclasses to retrieve information from Meta when creating a model and configure its operation in the ORM and admin interface. There's no need to override the mechanism — it's enough to define the class Meta within the class.
tags: #interview
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PyData Careers
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
Admin: @HusseinSheikho || @Hussein_Sheikho
Admin: @HusseinSheikho || @Hussein_Sheikho
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Forwarded from Machine Learning with Python
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We're sharing a cool resource for learning about neural networks, offering clear, step-by-step instruction with dynamic visualizations and easy-to-understand explanations.
In addition, you'll find many other useful materials on machine learning on the site.
Find and use it — https://mlu-explain.github.io/neural-networks/
tags: #AI #ML #PYTHON
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Forwarded from Udemy Coupons
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Master Python in 2025: Build 101 Projects, Learn Socket Programming , Automation, Data Analysis, OpenCV and OOP....
🏷 Category: development
🌍 Language: English (US)
👥 Students: 6,622 students
⭐️ Rating: 4.3/5.0 (143 reviews)
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Forwarded from Udemy Coupons
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💎 By: https://news.1rj.ru/str/DataScienceC
Python for Beginners & Beyond: Learn to Code with Real-World Projects...
🏷 Category: it-and-software
🌍 Language: English (US)
👥 Students: 15,346 students
⭐️ Rating: 4.2/5.0 (138 reviews)
🏃♂️ Enrollments Left: 983
⏳ Expires In: 0D:4H:4M
💰 Price:
🆔 Coupon: 42CE25692A9A939BF456
⚠️ Please note: A verification layer has been added to prevent bad actors and bots from claiming the courses, so it is important for genuine users to enroll manually to not lose this free opportunity.
💎 By: https://news.1rj.ru/str/DataScienceC