COURSE

SageMaker Built-in Algorithms and Frameworks

INR 29

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📂 AWS Certifications

Description

Comprehensive understanding of SageMaker's built-in algorithms, their applications, and framework integrations for various ML use cases.

Learning Objectives

Learners will master SageMaker's extensive collection of built-in algorithms including supervised learning, unsupervised learning, and specialized algorithms. They will understand algorithm selection criteria, parameter configuration, input data requirements, and practical implementation for various business use cases. Students will also learn framework integration patterns and when to use built-in versus custom solutions.

Topics (13)

XGBoost Algorithm Implementation and Optimization

Comprehensive XGBoost usage including algorithm configuration, hyperparameter optimization, feature importance analysis, and performance tuning for tabular data problems.

Linear Learner for Classification and Regression

Advanced Linear Learner implementation including algorithm configuration, regularization techniques, multi-class classification, and performance optimization strategies.

K-Means Clustering and Unsupervised Learning

Comprehensive K-means clustering including optimal cluster selection, initialization strategies, cluster validation metrics, and practical applications for customer segmentation.

Principal Component Analysis (PCA) for Dimensionality Reduction

Advanced PCA implementation including component selection, explained variance analysis, feature interpretation, and integration with downstream ML algorithms.

Random Cut Forest for Anomaly Detection

Comprehensive RCF implementation including anomaly scoring, threshold setting, streaming anomaly detection, and integration with monitoring systems.

DeepAR for Time Series Forecasting

Comprehensive DeepAR implementation including time series preprocessing, probabilistic forecasting, confidence intervals, and multi-series forecasting.

BlazingText for Text Classification and Word Embeddings

Advanced BlazingText usage including text preprocessing, embedding generation, classification model training, and NLP pipeline integration.

Object2Vec for Embeddings and Similarity Learning

Comprehensive Object2Vec implementation including object relationship modeling, embedding generation, similarity computation, and downstream task integration.

Image Classification and Computer Vision Algorithms

Advanced computer vision implementation including image preprocessing, transfer learning, multi-label classification, and model optimization for visual tasks.

LDA and Neural Topic Modeling

Advanced topic modeling including document preprocessing, topic extraction, model evaluation, and practical applications for content analysis and recommendation.

Algorithm Selection and Comparison Framework

Comprehensive algorithm selection methodology including use case analysis, performance benchmarking, cross-validation strategies, and decision frameworks.

Custom vs Built-in Algorithm Decision Making

Strategic decision framework including cost-benefit analysis, performance requirements, development timeline, and maintenance considerations for algorithm selection.

Factorization Machines for Recommendation Systems

Advanced Factorization Machines including sparse feature handling, interaction modeling, recommendation system implementation, and performance optimization.