Focusing on practical applications of machine learning (ML) within the Amazon Web Services ecosystem. The content systematically covers the exam syllabus, starting with ML fundamentals like modeling pipelines, supervised and unsupervised learning, and data splitting strategies to prevent overfitting and underfitting. It then details various AWS services for AI/ML, including Amazon Rekognition for image/video analysis, Amazon Polly for text-to-speech, Amazon Transcribe for speech-to-text, and Amazon Comprehend for natural language processing (NLP), alongside storage solutions like Amazon S3, RDS, and Redshift. The guide also explains data preparation and transformation techniques, such as handling missing values, outliers, and unbalanced datasets, and explores different ML algorithms (e.g., linear regression, XGBoost, K-means) as well as their evaluation and optimization through metrics like precision, recall, and hyperparameter tuning using Amazon SageMaker.
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Published on 16 hours ago
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