Data Science Applied Complete by Yates Norman (2 results)

Taschenbuch. Condition: Neu. 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.…

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