Command-Line Pipeline Builder for Spam and News Classification

Added Jun 2025 3 design docs

Before reaching for large neural models, working data scientists establish classical baselines, and the discipline of building a clean pipeline from raw text to evaluated classifier is exactly what most learners skip. Spam filtering and news categorization remain the canonical proving grounds because the tasks are intuitive while the engineering lessons are universal.

The intern builds a Python command-line tool that assembles the full classical NLP workflow. Text preprocessing with NLTK handles cleaning, tokenization, and stopword removal; scikit-learn's TF-IDF vectorizer converts documents into numeric features; and training commands fit Logistic Regression, Naive Bayes, and Support Vector Machine classifiers on labeled datasets. The tool outputs proper evaluation artifacts, accuracy, precision, recall, F1, and confusion matrices on held-out data, and supports running trained pipelines on new documents to produce predictions. Comparing the three algorithms on the same data teaches how model choice interacts with feature representation.

By completing the project, the intern masters the baseline methodology that professional NLP work is measured against, learns to structure a multi-stage pipeline behind a clean CLI, and demonstrates evaluation literacy, knowing not just how to train a classifier but how to prove it works.

Related projects

Word and Sentence Tokenization Command-Line Trainer

Tokenization, splitting text into words and sentences, is the very first operation in every NLP pipeline, and it is deceptively subtle: abbreviations break naive sentence splitting, contractions and hyphenation complicate word boundaries, and different libraries resolve these cases differently. Anyone building on top of NLP tools needs to know what their tokenizer actually does. The intern builds a Python command-line application that tokenizes input text into words and sentences using both NLTK and spaCy. The tool accepts text directly or from files, runs both tokenizers, and presents the results clearly, showing token counts, sentence boundaries, and a comparison view highlighting where the two libraries split the same passage differently. The intern feeds it tricky inputs, abbreviations like Dr., decimal numbers, quotations, and URLs, to surface exactly the edge cases that make tokenization an interesting engineering problem rather than a string split. Through this focused project the intern learns the foundational operation underlying all text processing, gains first hands-on experience with the two dominant Python NLP libraries, and practices building a clean command-line interface with helpful output formatting, producing a small utility that doubles as a study aid for every NLP course exercise that follows.

educationnlpnltkpython+1

PRD · HLD · LLD includedView

TechnologyHard

Crop Disease Classification Service Using Transfer Learning

Crop diseases are often identified too late for effective intervention because farmers and field agronomists rarely have quick access to plant pathology expertise. A photograph of a diseased leaf contains enough signal for accurate diagnosis, but turning that signal into a dependable, scalable service is a genuine engineering challenge for agricultural research groups. The intern builds a full diagnostic platform around transfer learning: pre-trained ResNet and MobileNet backbones are fine-tuned in PyTorch on crop disease imagery, with OpenCV and NumPy handling preprocessing and a data augmentation pipeline improving robustness to lighting and orientation. A Streamlit interface lets users upload leaf photos and receive ranked disease predictions alongside explainable AI visualizations that highlight the image regions driving each classification, while a FastAPI service exposes a batch inference endpoint for research workloads and pandas powers summary reporting. The entire system is containerized with Docker so it can be deployed consistently across lab and cloud environments. The project demonstrates end-to-end applied computer vision: adapting pre-trained networks to a specialist domain, communicating model reasoning to non-technical users, and packaging research code as a deployable, reproducible service.

agricultureeducationcomputer-visiondockerfastapi+8

PRD · HLD · LLD includedView

TechnologyEasy

Lightweight Text Generation API Built With LangServe

The gap between a working LangChain script and a service other software can call is deployment, and LangServe exists to close it: it turns chains into REST APIs with typed endpoints, streaming, and an interactive playground almost for free. Knowing this path from notebook to network service is essential for any developer who wants their AI work used by anyone other than themselves, and this project walks it end to end at a friendly scale. The intern builds a FastAPI project in Python that exposes a text generation chain through LangServe. The intern first constructs the chain itself with LangChain, a prompt template feeding a language model with configurable parameters such as topic and style, then mounts it as a LangServe route, gaining automatically generated endpoints for invocation, batching, and streaming. Around this core the intern adds a health check, request logging, and clear API documentation, then exercises the service from the built-in playground and from an external client script to prove the full round trip works. The project teaches deployment fundamentals for LLM applications: how chains become HTTP services, how FastAPI and LangServe divide responsibilities, what streaming responses look like on the wire, and how to structure a small AI service so that adding the next endpoint is easy, foundations the intern will reuse in every production AI role.

educationfastapigenerative-ailangchain+1

PRD · HLD · LLD includedView