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Free udemy courses

NumPy Cheat Sheet: Data Analysis in Python

This #Python cheat sheet is a quick reference for #NumPy beginners.

Learn more:
https://www.datacamp.com/cheat-sheet/numpy-cheat-sheet-data-analysis-in-python

https://news.1rj.ru/str/DataAnalyticsX

🤖 Automating Research with NotebookLM

Notebooklm-py is an unofficial library for working with Google NotebookLM, allowing you to automate research processes, generate content, and integrate AI agents. It's suitable for prototypes and personal projects, using Python or the command line.

🚀 Key features:
- Integration with AI agents and Claude Code
- Automating research with source importing
- Generating podcasts, videos, and educational materials
- Support for working via the Python API and CLI
- Use with unofficial Google APIs

📌 GitHub: https://github.com/teng-lin/notebooklm-py

https://news.1rj.ru/str/DataAnalyticsX

🚀 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

Effective Pandas 2: Opinionated Patterns for Data Manipulation

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