Скачать или смотреть Data Basics for AI: Features, Labels, Datasets & Train/Test Split Explained

🎓 Welcome to the Learn AI Playlist! In this video, we dive deep into one of the most fundamental aspects of artificial intelligence and machine learning: data. If you're just starting out on your AI learning journey, understanding the core building blocks like features, labels, datasets, and data splits (train/test/validation) is absolutely essential.

🔍 What You'll Learn in This Video:

What are features and how they help models learn patterns

What are labels and why they are critical for supervised learning

The difference between features and labels

What is a dataset and how it's structured

Why we split data into training, testing, and validation sets

How proper data splitting avoids overfitting and ensures generalization

Real-world examples and visual explanations to help concepts stick

📈 Whether you're learning to build your first machine learning model or just trying to understand how data fuels AI, this beginner-friendly breakdown will set you on the right path.

💡 Why This Video Matters for You:
Understanding data structure is the first real step to building your own AI models. Data preparation and proper splitting are often overlooked, but they make the difference between a successful model and a misleading one.

✅ If you want to:

Learn AI from scratch

Start a career in data science or ML

Build reliable, high-performing models

Avoid beginner mistakes like data leakage or improper splitting

…this video is made for you.

📚 Watch Next in the Playlist:

What is Machine Learning? (Intro to AI)

Types of Machine Learning: Supervised vs Unsupervised

Data Preprocessing: Cleaning and Normalizing Data

🧠 Helpful Terms Explained:

Features: The input variables used by the model to make predictions (e.g., age, salary)

Labels: The target variable you're trying to predict (e.g., will a customer churn?)

Training Data: The portion of data used to train the model

Validation Data: Used during training to fine-tune the model

Test Data: Used after training to evaluate final model performance

🔗 Resources & Tools:

Google Colab: https://colab.research.google.com

scikit-learn Documentation: https://scikit-learn.org

Kaggle Datasets: https://www.kaggle.com/datasets

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#LearnAI #MachineLearning #AIForBeginners #ArtificialIntelligence #DataScience #ML #AITutorial #TrainingData #TestData #ValidationData #Datasets #DataSplitting #DataPreparation

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