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{ "item_title" : "Data Science & Applied AI", "item_author" : [" Norman Yates "], "item_description" : "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.", "item_img_path" : "https://covers1.booksamillion.com/covers/bam/9/79/819/819/9798198199460_b.jpg", "price_data" : { "retail_price" : "34.95", "online_price" : "34.95", "our_price" : "34.95", "club_price" : "34.95", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Data Science & Applied AI|Norman Yates

Data Science & Applied AI : The Complete 14-Week Self-Paced Program: From Python Foundations to Building LLM-Powered Applications

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Overview

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.

This item is Non-Returnable

Details

  • ISBN-13: 9798198199460
  • ISBN-10: 9798198199460
  • Publisher: Independently Published
  • Publish Date: May 2026
  • Dimensions: 9 x 6 x 0.22 inches
  • Shipping Weight: 0.33 pounds
  • Page Count: 104

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