Data Science & Applied AI : The Complete 14-Week Self-Paced Program: From Python Foundations to Building LLM-Powered Applications
Language: English
Published by Amazon Digital Services LLC - Kdp Mai 2026, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
US$ 50.43
Quantity: 2 available
Add to basketItem description from seller
Neuware - Are you serious about breaking into data science or AI - but tired of scattered tutorials, half-finished courses, and 'learn Python in 24 hours' promises This book gives you something different: a complete, structured, 14-week university-level curriculum - from Python fundamentals to building and deploying LLM-powered AI applications - without a $60,000 master's program.Modeled on graduate-level coursework. Designed for self-directed learners.Every week is structured like a university class: - Clear learning objectives (what you will actually be able to do)- Curated readings from leading textbooks and free online resources- A real, graded-style assignment that produces a portfolio artifact- The tools and libraries professionals use on the jobNo filler. No hand-holding. Just the program.WHAT YOU WILL COVER: Phase 1 - Foundations (Weeks 1-3): Python, NumPy, mathematics for ML (linear algebra, calculus, probability), and exploratory data analysis with Pandas.Phase 2 - Data Engineering and Visualization (Weeks 4-5): SQL through window functions, ETL pipeline design, data cleaning, and interactive dashboards with Plotly and Streamlit.Phase 3 - Machine Learning (Weeks 6-9): Supervised learning, feature engineering, model interpretation with SHAP, clustering, and dimensionality reduction.Phase 4 - Deep Learning (Weeks 10-11): Neural networks from scratch, backpropagation, PyTorch, CNNs, RNNs, and transfer learning.Phase 5 - Applied AI (Weeks 12-13): How LLMs work, prompt engineering, retrieval-augmented generation (RAG), agentic AI, and production AI applications.Phase 6 - Capstone (Week 14): A GitHub repository, technical research report, live deployed demo, and recorded presentation.WHO THIS IS FOR: - Career changers wanting a structured path into data science or AI- Software engineers moving into ML and AI roles- Analysts who want to go deeper into modeling and AI- Recent graduates wanting a rigorous supplement to their degree- Self-taught programmers tired of jumping between resourcesPrerequisites: Basic programming experience, high school algebra, willingness to do the work. No prior data science knowledge required.BY THE END OF WEEK 14, YOU WILL: - Build and deploy production-ready ML models end-to-end- Design and fine-tune deep learning architectures- Build LLM-powered applications with RAG, agents, and tool use- Communicate findings through professional data visualizations- Present a complete capstone portfolio project to a technical audienceStop collecting courses. Start finishing one.…
Seller Inventory # 9798198199460
- Title
- Data Science & Applied AI : The Complete 14-Week Self-Paced Program: From Python Foundations to Building LLM-Powered Applications
- Author
- Norman Yates
- Publisher
- Amazon Digital Services LLC - Kdp Mai 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798198199460
- Item weight
- 150 grams
- Dimensions
- 229x152x6 mm
Are you serious about breaking into data science or AI — but tired of scattered tutorials, half-finished courses, and "learn Python in 24 hours" promises?
This book gives you something different: a complete, structured, 14-week university-level curriculum — from Python fundamentals to building and deploying LLM-powered AI applications — without a $60,000 master's program.
Modeled on graduate-level coursework. Designed for self-directed learners.
Every week is structured like a university class:
- Clear learning objectives (what you will actually be able to do)
- Curated readings from leading textbooks and free online resources
- A real, graded-style assignment that produces a portfolio artifact
- The tools and libraries professionals use on the job
No filler. No hand-holding. Just the program.
WHAT YOU WILL COVER:
Phase 1 — Foundations (Weeks 1–3): Python, NumPy, mathematics for ML (linear algebra, calculus, probability), and exploratory data analysis with Pandas.
Phase 2 — Data Engineering and Visualization (Weeks 4–5): SQL through window functions, ETL pipeline design, data cleaning, and interactive dashboards with Plotly and Streamlit.
Phase 3 — Machine Learning (Weeks 6–9): Supervised learning, feature engineering, model interpretation with SHAP, clustering, and dimensionality reduction.
Phase 4 — Deep Learning (Weeks 10–11): Neural networks from scratch, backpropagation, PyTorch, CNNs, RNNs, and transfer learning.
Phase 5 — Applied AI (Weeks 12–13): How LLMs work, prompt engineering, retrieval-augmented generation (RAG), agentic AI, and production AI applications.
Phase 6 — Capstone (Week 14): A GitHub repository, technical research report, live deployed demo, and recorded presentation.
WHO THIS IS FOR:
- Career changers wanting a structured path into data science or AI
- Software engineers moving into ML and AI roles
- Analysts who want to go deeper into modeling and AI
- Recent graduates wanting a rigorous supplement to their degree
- Self-taught programmers tired of jumping between resources
Prerequisites: Basic programming experience, high school algebra, willingness to do the work. No prior data science knowledge required.
BY THE END OF WEEK 14, YOU WILL:
- Build and deploy production-ready ML models end-to-end
- Design and fine-tune deep learning architectures
- Build LLM-powered applications with RAG, agents, and tool use
- Communicate findings through professional data visualizations
- Present a complete capstone portfolio project to a technical audience
Stop collecting courses. Start finishing one.
"Synopsis" may belong to another edition of this title.
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