Module 3 Lesson 2. Training Data

Lesson Overview
The single most important factor in machine learning success is data. A simple algorithm trained on great data will usually beat a complex algorithm trained on poor data.

What Training Data Is
Training data is the set of historical examples used to teach the model. For a spam filter, training data is thousands of emails already labelled "spam" or "not spam". For a sales forecast, it's years of past sales with the conditions at the time.

Data Collection
Common sources include:
Internal systems (CRM, ERP, point-of-sale).
Sensors and IoT devices.
Public datasets (open data portals, Kaggle).
Web data (with permission and within terms of use).
User-generated content (with consent).

What Good Training Data Looks Like
| Quality | Why It Matters | |---|---| | Accurate | Wrong labels teach the model the wrong thing. | | Complete | Missing values can bias predictions. | | Consistent | "USA" and "United States" must be unified. | | Representative | Must reflect the real population, not just one segment. | | Timely | Old data may no longer reflect current patterns. | | Sufficient | Most models need thousands or millions of examples. |

Structured vs Unstructured Data
Structured — tables of numbers, dates, categories (sales records, inventory).
Unstructured — text, images, audio, video (emails, photos, voice notes).
Modern AI (especially deep learning) can work with both, but structured data is generally faster and cheaper to use.

Try This
List three examples of structured data in a typical business (e.g. customer table, sales table, attendance log) and three examples of unstructured data (emails, customer reviews, support call recordings).

Видео Module 3 Lesson 2. Training Data канала Softclue Global Technologies