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30 Real-World AI & Data Science Projects

From Beginner to Job-Ready Portfolio β€’ Kalyanjit Hatibaruah
OFFICIAL LEARNING PLATFORM β€’ 2026 EDITION

30 Real-World AI & Data Science Projects

From Beginner to Job-Ready Portfolio

Authored by Kalyanjit Hatibaruah β€’ Published by Flugelsoft Labs

A hands-on, industry-grounded learning pathway taking students, engineers, and career changers from programming fundamentals to deploying 30 intelligent, production-ready applications across 30 distinct industries.

πŸ“– Explore Book Curriculum
30 Real Industries
47 Full Chapters
30 1-Click Repositories
150+ Interview Q&As
9 16:9 Infographics
Book Master Framework
Modern Technological Framework: AI & Data Science Pathway From Beginner Foundations to Job-Ready Portfolio by Kalyanjit Hatibaruah
LEARNER VALUE PROPOSITION

Exclusive Benefits for Registered Learners

Once registered and approved by the administrator, learners gain instant, unrestricted access to the complete digital suite:

πŸ“–

Unabridged 47-Chapter Manuscript

Read the entire book online with full code explanations, KaTeX mathematical formulas, and step-by-step algorithms across all 6 Parts.

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30 1-Click Deployable Codebases

Access 30 standalone GitHub repositories with pre-configured 1-click deploy buttons for Streamlit Community Cloud and GitHub Codespaces.

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Interactive Algorithmic Simulators

Test machine learning models directly in the browserβ€”including student academic risk, property valuation, anomaly detection, and telecom churn.

πŸ’Ό

150+ Interview Defense Bank

Master technical, algorithmic, and system design questions asked by hiring managers with model answers formatted in the STAR-D structure.

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Interactive 90-Day Career Roadmap

Track your milestone progress day-by-day from initial Python data wrangling to pinning 4 flagship GitHub projects and interview readiness.

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16:9 Technological Infographics

High-resolution widescreen architectural frameworks illustrating the modern Python data stack, ML pipelines, and GenAI RAG architectures.

PROGRESSIVE LEARNING TAXONOMY

The 6-Part Curriculum Architecture

Designed to accommodate students and professionals from any academic background:

PART I β€’ Chapters 1–3

Understanding AI & Data Science

Demystifying AI in plain English, understanding industry value chains, and cultivating an engineer's problem-solving mindset without intimidation.

PART II β€’ Chapters 4–8

Programming & Data Science Foundations

Hands-on Python core, numerical arrays with NumPy, tabular wrangling with pandas, EDA, SQL databases, and machine learning principles.

PART III β€’ Projects 01–10

Level 1: Beginner Projects

Data cleaning, KPI engines, regression estimators, and interactive dashboards across Education, Retail, Real Estate, Banking, Agriculture, and Environment.

PART IV β€’ Projects 11–20

Level 2: Intermediate Projects

Supervised machine learning, classification, NLP text processing, and forecasting across Healthcare, Predictive Maintenance, Logistics, Telecom, and Energy.

PART V β€’ Projects 21–30

Level 3: Advanced & Generative AI

Retrieval-Augmented Generation (RAG), vector embeddings, semi-structured document OCR, and automated agents in LegalTech, EdTech, Precision Farming, and Maritime.

PART VI β€’ Chapters 31–33 & Appendices

Career Launch & Interview Mastery

Constructing recruiter-grade GitHub portfolios, 150+ STAR-D interview defense questions, the 90-day learning roadmap, and formula cheat sheets.

Community Access & Verification Policy

To preserve academic rigor and prevent automated scraping, each new registration requires a one-time approval by Kalyanjit Hatibaruah (Admin). Once you sign in with your Google account, your profile is queued for approval.