Are you 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. 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 tutorial 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 tutorial is Stephane Maarek, an AWS expert and popular AWS certification instructor on Udemy.
In addition to the 11-hour video tutorial, 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 tutorial is structured into the four domains tested by this exam: data engineering, exploratory data analysis, modeling, and machine learning implementation and operations.
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. AWS machine learning certification preparation – learn SageMaker, feature engineering, data engineering, modeling & more.
What you’ll learn:
- What to expect on the AWS Certified Machine Learning Specialty exam.
- Amazon SageMaker’s built-in machine learning algorithms (XGBoost, BlazingText, Object Detection).
- Feature engineering techniques, including imputation, outliers, binning, and normalization.
- High-level ML services: Comprehend, Translate, Polly, Transcribe, Lex, Rekognition.
- Data engineering with S3, Glue, Kinesis, and DynamoDB.
- Exploratory data analysis with scikit_learn, Athena, Apache Spark, and EMR.
- Deep learning and hyperparameter tuning of deep neural networks.
- Automatic model tuning and operations with SageMaker.
- L1 and L2 regularization.
- Applying security best practices to machine learning pipelines.
Requirements:
- Associate-level knowledge of AWS services such as EC2.
- Some existing familiarity with machine learning.
- An AWS account is needed to perform the hands-on lab exercises.
This tutorial also includes:
- 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).
- High-level ML services: Comprehend, Translate, Polly, Transcribe, Lex, Rekognition.
- Building recommender systems with Amazon Personalize.
- Monitoring industrial equipment with Lookout and Monitron.
- Security best practices with machine learning on AWS.
Who this tutorial is for:
- Individuals performing a development or data science role seeking certification in machine learning and AWS.
