AI Lesson & Submodule
Pandas for ML workflows
DataFrames, filtering, grouping, selecting feature/target columns, and exploratory data analysis.
Why This Matters
Data preparation consumes 80% of ML workflows. Pandas makes querying and preprocessing datasets highly efficient.
What You Will Learn
- •Loading data into Pandas DataFrames
- •Filtering, querying, and grouping datasets
- •Isolating feature columns vs target columns
- •Handling missing data inside DataFrames
- •Basic exploratory data analysis and statistics
Concepts Covered
DataFrameSeriesFeature ColumnTarget Column
Mapped Foundation Project: NumPy Vector Playground
Execute matrix arithmetic, broadcasting dot products, and shape transformations.
Architecture Preview
System flow diagrams outlining client nodes, processing engines, and backing databases for NumPy Vector Playground.
Client WorkspaceInference GatewayNumPy Vector Playground Logic Engine
Tech Stack Planned
PythonNumPy
GitHub: Coming SoonLive Demo: Coming Soon
Coming SoonTechnical Interview Value
- ?Manipulate large datasets, group features, and clean target columns in memory