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Concise, clear, and validated revision notes
Kalyan Narayana
🌊 LightGBM: Deep Dive & Best Practices
A validated and novice-friendly guide to LightGBM, detailing its high-performance, leaf-wise tree growth algorithm, and histogram-based techniques. Learn best practices for achieving maximum speed, memory efficiency, and accuracy on large-scale datasets.
🧭 Data Engineering: Quick Guide, References & Best Practices
A validated and novice-friendly guide to Data Engineering - covering essential concepts like ELT/ETL, Data Pipelines, Data Warehousing (e.g., Snowflake, BigQuery), Orchestration (e.g., Airflow), Data governance, and Implementing robust, scalable data architectures.
🌊 HTML5 / CSS3: Deep Dive & Best Practices
Concise, clear, and validated revision notes on HTML5 / CSS3 — practical best practices for beginners and practitioners.
🌊 CatBoost: Deep Dive & Best Practices
A validated and novice-friendly guide covering CatBoost's algorithm, efficient handling of categorical features, and how to tune parameters for superior performance and robustness in production-ready machine learning models.
🧭 Data Pipelines: Quick Guide, References & Best Practices
Data Pipelines — A Quick Guide, List of References and Best Practices for beginners and practitioners.
🧭 REST API: Quick Guide, References & Best Practices
REST API — Quick Guide, References & Best Practices — for beginners and practitioners.
🌊 Random Forest: Deep Dive & Best Practices
Concise, clear, and validated revision notes on Random Forest with Python & Scikit-learn — practical best practices for beginners and practitioners.
🌊 XGBoost: Deep Dive & Best Practices
Concise, clear, and validated revision notes on XGBoost library for Python — practical best practices for beginners and practitioners.
🌊 Statsmodels: Deep Dive & Best Practices
Concise, clear, and validated revision notes on Statsmodels — structured for beginners and practitioners.