Explore the Full AI Expert Roadmap: From Data Science to Big Data Engineering
The AI Expert Roadmap on GitHub offers a comprehensive, interactive guide covering data‑science fundamentals, machine‑learning algorithms, deep‑learning techniques, data‑engineering pipelines, and big‑data architectures, with linked resources, up‑to‑date references, and practical tool recommendations for aspiring AI professionals.
Project Overview
AI‑Expert‑Roadmap is an open‑source, interactive learning map for artificial intelligence, hosted at https://github.com/AMAI-GmbH/AI-Expert-Roadmap. Each node links to external definitions, Wikipedia entries, and the latest research papers. The repository is version‑controlled and updated whenever new relevant work appears.
Roadmap Structure
The map is organized into five independent learning tracks, each providing a step‑by‑step progression from fundamentals to advanced topics.
Data Scientist Track
Machine Learning Track
Deep Learning Track
Data Engineer Track
Big Data Engineer Track
1. Data Scientist Track
Core foundations include:
Mathematics: matrix algebra and linear algebra.
Databases and data formats: JSON, XML, CSV.
Regular expressions.
Statistics: probability theory, probability distributions, estimation, hypothesis testing, confidence intervals, law of large numbers, Monte Carlo methods.
Python programming: syntax, virtual‑environment setup, essential libraries (e.g., NumPy, pandas, matplotlib).
Data acquisition: access to curated public datasets via the “Awesome Public Datasets” collection.
Data processing: visualization, exploratory analysis, transformation, and cleaning.
After mastering these basics, learners can branch into machine‑learning or data‑engineering specializations.
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