Скачать или смотреть Data Preparation for Machine Learning using AWS SageMaker Canvas Data Wrangler

Did you know 80% of ML project time is spent on Data Preparation?
In this video, we deep dive into Data Preparation for Machine Learning
using AWS SageMaker Data Wrangler!

Whether you're a beginner or an ML practitioner, mastering data
preparation is the KEY to building better models. Garbage in =
Garbage out!
📌 What You'll Learn:
✅ Why Data Preparation Matters (80/20 Rule)
✅ The Complete ML Data Pipeline
✅ Data Collection - Sources & Challenges
✅ Exploratory Data Analysis (EDA) Techniques
✅ Data Preprocessing Techniques
✅ Feature Engineering Fundamentals
✅ What is SageMaker Data Wrangler?
✅ Data Wrangler Key Features (300+ Transformations!)
✅ Data Wrangler Workflow (Import → Transform → Analyze → Export)
✅ Best Practices for Data Preparation

🔗 Useful Links:
► AWS SageMaker Data Wrangler Docs:
https://docs.aws.amazon.com/sagemaker...
► AWS SageMaker Pricing: https://aws.amazon.com/sagemaker/pricing
► Previous Video - Intro to SageMaker: [Link]

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⏱️ Timestamps Below 👇
00:00 Introduction
00:16 Why Data Preparation Matters (80/20 Rule)
02:11 The ML Data Pipeline Overview
05:20 Data Collection: Sources & Challenges
07:30 Exploratory Data Analysis (EDA)
06:54 Data Preprocessing Techniques
13:38 Feature Engineering Fundamentals
16:24 What is SageMaker Data Wrangler?
19:11 Data Wrangler Key Features
21:34 Data Wrangler Workflow
25:20 Best Practices for Data Preparation
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