AWS Certified Machine Learning Specialty 2026 - Hands On!

Updated for the latest SageMaker features, and Generative AI (LLM's / Bedrock). Happy learning! Nervous about passing the **AWS Certified Machine Learning - Specialty exam** (MLS-C01)? You should be! There's no doubt it's one of the most difficult and coveted AWS certifications. A deep knowledge of AWS and SageMaker isn't enough to pass this one - you also need deep knowledge of machine learning, and the nuances of feature engineering and model tuning that generally aren't taught in books or classrooms. You just can't prepare enough for this one. This certification prep course is taught by Frank Kane, who spent nine years working at Amazon itself in the field of machine learning. Frank took and passed this exam on the first try, and knows exactly what it takes for you to pass it yourself. Joining Frank in this course is Stephane Maarek, an AWS expert and popular AWS certification instructor on Udemy. In addition to the 15-hour video course, a 30-minute quick assessment practice exam is included that consists of the same topics and style as the real exam. You'll also get four hands-on labs that allow you to practice what you've learned, and gain valuable experience in model tuning, feature engineering, and data engineering. This course is structured into the four domains tested by this exam: data engineering, exploratory data analysis, modeling, and machine learning implementation and operations. Just some of the topics we'll cover include: - How generative AI and large language models (LLM's) work, including the Transformer architecture (GPT) and attention-based neural networks (masked self-attention) - Amazon's newest generative AI services: Bedrock, SageMaker JumpStart for Generative AI, CodeWhisperer, and SageMaker Foundation Models - S3 data lakes - AWS Glue and Glue ETL - Kinesis data streams, firehose, and video streams - DynamoDB - Data Pipelines, AWS Batch, and Step Functions - Using scikit_learn - Data science basics - Athena and Quicksight - Elastic MapReduce (EMR) - Apache Spark and MLLib - Feature engineering (imputation, outliers, binning, transforms, encoding, and normalization) - Ground Truth - Deep Learning basics - Tuning neural networks and avoiding overfitting - Amazon SageMaker, including SageMaker Studio, SageMaker Model Monitor, SageMaker Autopilot, and SageMaker Debugger. - Regularization techniques - Evaluating machine learning models (precision, recall, F1, confusion matrix, etc.) - High-level ML services: Comprehend, Translate, Polly, Transcribe, Lex, Rekognition, and more - Building recommender systems with Amazon Personalize - Monitoring industrial equipment with Lookout and Monitron - Security best practices with machine learning on AWS Machine learning is an advanced certification, and it's best tackled by students who have already obtained associate-level certification in AWS and have some real-world industry experience. This exam is not intended for AWS beginners. If there's a more comprehensive prep course for the *AWS Certified Machine Learning - Specialty exam*, we haven't seen it. Enroll now, and gain confidence as you walk into that testing center.