Data Science & Applied AI: The Complete 14-Week Self-Paced Program: From Python Foundations to Building LLM-Powered Applications - Softcover
Synopsis
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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Data Science & Applied AI: The Complete 14-Week Self-Paced Program: From Python Foundations to Building LLM-Powered Applications
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Data Science and Applied AI
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Data Science & Applied AI (Paperback)
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Paperback. Condition: new. Paperback. 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 resourcesA real, graded-style assignment that produces a portfolio artifactThe 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 AISoftware engineers moving into ML and AI rolesAnalysts who want to go deeper into modeling and AIRecent graduates wanting a rigorous supplement to their degreeSelf-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-endDesign and fine-tune deep learning architecturesBuild LLM-powered applications with RAG, agents, and tool useCommunicate findings through professional data visualizationsPresent a complete capstone portfolio project to a technical audienceStop collecting courses. Start finishing one. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798198199460
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