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Practical insights for a data-driven approach to model optimizationOn this last a part of my series, I'll share what I even have learned on choosing a model for image classification and easy methods to...

Employ cluster algorithms to handle missing time-series data(Should you haven’t read Part 1 yet, test it out here.)Missing data in time-series evaluation is a recurring problem.As we explored in Part 1, easy imputation techniques...

Coding your road to Data Science: mastering key development skillsData science is undoubtedly one of the fascinating fields today. Following significant breakthroughs in machine learning a couple of decade ago, data science has surged...

We’ll proceed our deal with feature engineering — this stays the core objective of this project.Upon completing all feature engineering tasks, I’ll save the ends in a CSV file as the ultimate deliverable, marking...

Feature engineering techniques for healthcare data evaluation, specializing in real-world challenges and practical solutions.On this project, we dive into feature engineering for medical data, where precision is important. It is a comprehensive project that...

Research and experiments are at the guts of any exercise that involves AI. Constructing LLM applications is not any different. Unlike traditional web apps that follow a pre-decided design that has little to no...

Unlocking the ability of SQL for data-driven decision-making48 min read·10 hours agoHere’s an interesting fact: do you already know when the SQL language was created? When it first appeared? I do! It was in...

Shaping, transposing, joining, and splitting arraysWelcome to Part 3 of Introducing NumPy, a primer for those latest to this essential Python library. Part 1 introduced NumPy arrays and the right way to create them....