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

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 2 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 - Q26 - Square Root Transformation for Poisson Distributed Data
1:26 - Q27 - Interpreting t-SNE Sensitivity to Perplexity
1:54 - Q28 - Assessing Imputation Distortion (Histogram Comparison)
2:22 - Q29 - Focusing Model Learning on Class Boundaries (ADASYN)
2:50 - Q30 - Handling High Cardinality in SageMaker Data Wrangler
3:18 - Q31 - Standardization vs. Normalization for Deep Learning Convergence
3:46 - Q32 - Feature Selection in the Presence of Multicollinearity
4:14 - Q33 - Handling Skewed Word Frequencies (TF-IDF)
4:42 - Q34 - Feature Engineering Rolling Averages for Time Series
5:10 - Q35 - Identifying Missing At Random (MAR) Mechanism
5:38 - Q36 - Interpreting High Variance Inflation Factor (VIF)
6:06 - Q37 - Cyclical Encoding for Time-Based Features (Hour of Day)
6:34 - Q38 - Interpreting Zero Pearson Correlation
7:02 - Q39 - Visualizing Cluster Separation (t-SNE / UMAP)
7:30 - Q40 - Robust Imputation with K-NN for MAR Data
7:58 - Q41 - Resolving Data Type Mismatches in Data Preparation
8:26 - Q42 - Feature Engineering: Capturing Vocabulary Richness (TTR)
8:54 - Q43 - Root Cause Analysis for Distribution Shift (Data Drift)
9:22 - Q44 - Redundancy of Interaction Features in Tree-Based Models
9:50 - Q45 - Assessing Label Quality with Inter-Rater Reliability (Kappa)
10:18 - Q46 - Binning Skewed Ordinal Features for Classification
10:46 - Q47 - Statistically Sound Sampling on Redshift (TABLESAMPLE)
11:14 - Q48 - Limitation of the Box-Cox Transformation
11:42 - Q49 - Analyzing Temporal Dependencies (Autocorrelation Function - ACF)
12:10 - Q50 - Handling Unseen Categories During Inference (OHE / Target Encoding)

12:38 - Playlist + PDF Access