Can I use this skill for LLM development?

Absolutely, it covers OpenAI API integration, LangChain workflows, prompt engineering, and the creation of AI agents.

Does this skill support deep learning frameworks?

Yes, it includes implementation patterns and training loops specifically designed for PyTorch, including CNN and SimpleNN architectures.

Is it suitable for beginners in data science?

Yes, it includes a structured 5-step learning roadmap and fundamental guides for essential libraries like NumPy and Pandas.

How does it help with model production?

The skill provides MLOps guidance, including model versioning with MLflow, serving models with FastAPI, and monitoring for data drift.

What machine learning libraries are covered?

The skill focuses primarily on Scikit-learn for classical ML tasks and PyTorch for advanced deep learning projects.

Data, AI & Machine Learning Roadmap

Name: Data, AI & Machine Learning Roadmap
Author: pluginagentmarketplace

bypluginagentmarketplace

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Data Science & ML

Provides comprehensive guidance and implementation patterns for data science, machine learning engineering, and AI application development.

About

This skill serves as an exhaustive reference for developers looking to master the end-to-end lifecycle of intelligent systems, from initial data manipulation with NumPy and Pandas to deploying production-grade AI models. It offers specific implementation snippets for classical machine learning, deep learning with PyTorch, and modern AI engineering using LLMs and agents. Whether you are building automated data pipelines, fine-tuning neural networks, or architecting LLM-powered applications with LangChain, this skill provides the frameworks, best practices, and MLOps strategies required to move from experimentation to scalable production.

Key Features

End-to-end data manipulation and visualization techniques using NumPy, Pandas, and Matplotlib
Deep learning architectures and training loops using PyTorch
AI engineering guides for LLM integration, prompt engineering, and agentic workflows
1 GitHub stars
Implementation patterns for supervised and unsupervised machine learning with Scikit-learn
MLOps best practices for model versioning with MLflow and serving with FastAPI

Use Cases

Establishing an MLOps pipeline to monitor data drift and version models in production environments
Developing AI agents that utilize external tools and chain-of-thought reasoning for complex tasks
Building and deploying a classification model for business intelligence and forecasting

About

Key Features

End-to-end data manipulation and visualization techniques using NumPy, Pandas, and Matplotlib
Deep learning architectures and training loops using PyTorch
AI engineering guides for LLM integration, prompt engineering, and agentic workflows
1 GitHub stars
Implementation patterns for supervised and unsupervised machine learning with Scikit-learn
MLOps best practices for model versioning with MLflow and serving with FastAPI

Use Cases

Establishing an MLOps pipeline to monitor data drift and version models in production environments
Developing AI agents that utilize external tools and chain-of-thought reasoning for complex tasks
Building and deploying a classification model for business intelligence and forecasting