Скачать или смотреть MLS-C01 EDA Mastery! 25 Essential AWS Machine Learning Specialty Questions (Domain 2, Part 1)

Welcome to the most critical domain for your AWS Certified Machine Learning - Specialty (MLS-C01) recertification: Domain 2: Exploratory Data Analysis (24% weight)!

In this video, Part 1 of 3, we tackle 25 essential, hands-on practice questions focusing on techniques and services used to prepare, visualize, and clean your datasets for model training. EDA is where you identify biases, missing values, and the right features to maximize model performance—a skill the MLS-C01 exam tests heavily!

This session covers scenario-based questions across key EDA concepts:
• Data Cleaning & Imputation: Techniques for handling missing, corrupted, or inconsistent data.
• Feature Engineering: Creating meaningful features, including normalization and one-hot encoding.
• Visualization: Choosing appropriate plots and charts to detect trends, anomalies, and outliers.
• Dimensionality Reduction: Understanding and applying techniques like PCA (Principal Component Analysis).
• Bias and Fairness: Identifying and mitigating data bias early in the ML lifecycle using Amazon SageMaker Clarify.

#MLSC01 #EDA #AWSMachineLearning #SageMaker #ExploratoryDataAnalysis #AWSML #CertPrep #featureengineering

TIMESTAMP
0:00 - Disclaimer
0:14 - Session Format
0:35 - What to Expect
0:44 - Exam + Domain Scope

0:58 - Q1 - Log Transformation for Skewed Data & Outliers
1:26 - Q2 - Handling Class Imbalance with SMOTE
1:54 - Q3 - Efficiently Visualizing Large Correlation Matrices
2:22 - Q4 - Imputation Strategy for Missing Not At Random (MNAR) Data
2:50 - Q5 - Encoding High-Cardinality Features (Grouping Rare Categories)
3:18 - Q6 - Verifying Distribution for IQR Outlier Detection
3:46 - Q7 - Scaling PCA for Massive Datasets (EMR & Spark MLlib)
4:14 - Q8 - Statistical Tests to Compare Group Means (t-test / Mann-Whitney U)
4:42 - Q9 - Identifying and Addressing Concept Drift (Data Drift)
5:10 - Q10 - Feature Engineering with Log Transformation for Non-Linearity
5:38 - Q11 - Importance of Scaling for Distance-Based Algorithms (kNN)
6:06 - Q12 - Cost-Effective Statistical Summary on S3 Data (Amazon Athena)
6:34 - Q13 - Target Encoding (Mean Encoding) for High-Cardinality Features
7:02 - Q14 - Aligning Metrics with Business Needs (Recall/F1-score for FN)
7:30 - Q15 - Investigating Bimodal Feature Distributions
7:58 - Q16 - Drawback of Feature Hashing (Collision & Interpretability)
8:26 - Q17 - Preprocessing for Unsupervised Anomaly Detection (Standardization)
8:54 - Q18 - Visualizing Non-Linear Monotonic Relationships (Scatter Plot)
9:22 - Q19 - Cost-Effective Filtering on S3 Data (Amazon S3 Select)
9:50 - Q20 - Encoding Seasonal Time Series Data (Sine/Cosine Transformation)
10:18 - Q21 - Feature Engineering from Unique Identifiers (UUID Substrings)
10:46 - Q22 - Benefit of PCA for Dimensionality Reduction
11:14 - Q23 - Identifying and Handling Data Leakage
11:42 - Q24 - Mitigating Multicollinearity with PCA
12:10 - Q25 - Interactive EDA on Moderate Data Samples (SageMaker Notebook)

12:38 - Playlist + PDF Access