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Foundations of Artificial Intelligence and Data Literacy

AI Concepts, Terminology, and the Modern Landscape

Data Basics: Types, Sources, and Quality

Collecting and Preparing Data for AI Tasks

Data Collection Planning and Governance

Data Cleaning and Preprocessing Essentials

Applying Basic Statistics and Feature Engineering

Descriptive Statistics and Data Exploration

Feature Engineering for Reliable Models

Modeling, Evaluation, and Reliability

Beginner-Friendly Models and Training Workflow

Evaluation Metrics and Error Analysis

Quizzes: Check Your Understanding

Quiz: Foundations and Terminology

Quiz: Data Collection and Quality

Quiz: Cleaning, Preprocessing, and Splits

Quiz: Statistics, Features, and Drift

Quiz: Modeling and Evaluation Metrics

Wrap-Up and Learner Feedback

Capstone Review: From Problem Framing to Reliable Results

Course Survey: Feedback and Next Steps

Build a rock-solid AI foundation without getting lost in math or jargon. AI Foundations: Data Collection, Preparation, and Evaluation for Beginners teaches you the most important skill in AI: working with data the right way. Through clear explanations, hands-on practices, and beginner-friendly tools, you’ll learn how to define a problem, plan and collect data ethically, clean and prepare datasets, engineer meaningful features, and evaluate simple models with confidence. You’ll understand key AI concepts (AI vs. ML vs. deep learning, supervised vs. unsupervised vs. reinforcement learning) and the typical project lifecycle—from problem framing to monitoring. You’ll also master practical steps like handling missing values, avoiding data leakage, splitting data correctly, and choosing the right metrics for your task. Along the way, you’ll connect data quality to real business impact and learn to spot pitfalls like bias, overfitting, and drift. By the end, you’ll complete a mini end-to-end project and walk away with a reusable checklist, best practices for documentation and reproducibility, and the confidence to contribute to beginner AI projects responsibly and effectively.

AI Foundations: Data Collection, Preparation, and Evaluation for Beginners

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            <p>AI Foundations: Data Collection, Preparation, and Evaluation for Beginners is designed to translate your familiarity with large language models into a broader, methodical understanding of how AI systems are built and trusted. The course opens by grounding key terminology—AI, machine learning, deep learning, and generative AI—while clarifying learning paradigms and the end-to-end project lifecycle from problem framing through deployment and monitoring. You will examine common pitfalls that derail beginner projects, such as data leakage and overfitting, and connect these risks to ethical responsibilities around bias, fairness, privacy, and transparency. We also establish data literacy essentials: distinguishing data types and modalities, identifying credible sources with proper permissions, and tying data quality dimensions directly to model reliability and business impact.</p>

<p>Building on these foundations, you will plan data collection with rigor—translating objectives into label definitions, designing sampling strategies for representativeness, and implementing lightweight governance with documentation, versioning, lineage, and consent. You will practice cleaning and preprocessing tasks that prevent silent errors: handling missingness and outliers, encoding categorical variables, normalizing features, and creating leakage-free train/validation/test splits. With basic statistics and exploratory analysis, you will validate assumptions, surface drift, and form hypotheses before modeling. The course then introduces beginner-friendly models and a reproducible training workflow, including baselines, simple hyperparameter tuning, cross-validation, and structured experiment tracking. Evaluation is handled with intent: selecting task-appropriate metrics, interpreting confusion matrices, calibrating thresholds, and analyzing errors by segment to align results with real-world constraints. Quizzes reinforce learning throughout, and a final capstone walkthrough provides a practical checklist to carry forward—ensuring future projects are grounded in data quality, clear documentation, ethical practice, and evaluation rigor.</p>
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