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Data Preparation for Machine Learning:

Welcome!

What is Data Preparation for Machine Learning?

Why is data preparation for machine learning important?

Common misconceptions about data preparation

Step-by-step guide to data preparation for machine learning

Step 1: Collecting data

Step 2: Cleaning data

Step 3: Data transformation

Step 4: Data reduction

Step 5: Data splitting

Why Good Data Makes Better Al

Learning Objectives

What's the Big Idea?

1. Data Collection & Labeling

2. Feature Selection: Picking the Right Inputs

3. Training vs. Testing: How the Model Learns and Gets Checked

4. Activity: Design a Mock Dataset

Why This Matters

Sources

Quick Check: What is data preparation?

Quick Check: Why does quality matter?

Quick Check: Bust the myths

Quick Check: Steps in order

Quick Check: Collecting data ethically

Quick Check: Cleaning vs. transforming

Quick Check: Transform features

Quick Check: Reduce dimensions wisely

Quick Check: Train/validation/test split

Build AI that is accurate, fair, and trustworthy. Ethical Data, Feature Selection, and Training for Better AI is a hands-on course that shows you how high-quality, ethically sourced data leads to better-performing models. You’ll learn the full data preparation pipeline—from collecting and cleaning to transforming, reducing, and splitting—so you can ship models that generalize well and respect user rights. We connect the dots between ethics and engineering: consent, bias mitigation, labeling quality, robust feature selection, and train/test practices that prevent data leakage. You’ll practice designing a mock dataset, choose the right features, and structure training and validation the right way. Whether you work in product, analytics, or ML engineering, this course gives you practical frameworks, checklists, and quick checks to avoid common pitfalls and raise the standard of your AI projects. If you want models that perform reliably in the real world and align with responsible AI principles, this course is your step-by-step guide.

Ethical Data, Feature Selection, and Training for Better AI

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            <p>Artificial intelligence isn’t just about fancy algorithms—it’s about the quality of the data that teaches those algorithms how to think. This course explores how ethical data practices, smart feature selection, and solid training methods make AI more accurate, fair, and useful. You’ll learn why good data matters, how to avoid common mistakes, and what it takes to build models that actually work in the real world. Along the way, we’ll connect technical steps like cleaning, transforming, and splitting data to big-picture ideas like reducing bias and making responsible choices.</p>

<p>We’ll start with data preparation for machine learning: what it is, why it’s important, and myths to avoid. Then we’ll follow a clear, step-by-step process—collecting data (ethically), cleaning messy values, transforming features so models can use them, reducing unnecessary information, and splitting data for fair training and testing. Next, we’ll focus on why good data makes better AI by digging into collection and labeling, picking the right inputs through feature selection, and understanding training versus testing. You’ll even design a mock dataset to see how small choices can change outcomes. By the end, you’ll know how to build AI projects that are not only accurate, but also responsible and trustworthy.</p>
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