AWS Certified Machine Learning Specialty 2020 – Hands On! Course Site
File Size: 3.45 GB
Learn SageMaker, feature engineering, model tuning, and the AWS machine learning ecosystem. Be prepared for the exam!
What you’ll learn
AWS Certified Machine Learning Specialty 2020 – Hands-On! Course Site
- What to expect on the AWS Certified Machine Learning Specialty exam
- Amazon SageMaker’s built-in machine learning algorithms (XGBoost, BlazingText, Object Detection, etc.)
- Feature engineering techniques, including imputation, outliers, binning, and normalization
- High-level ML services: Comprehend, Translate, Polly, Transcribe, Lex, Rekognition, and more
- 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
Description
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.
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.
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.
Just some of the topics we’ll cover include:
- 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, in-depth
- Regularization techniques
- Evaluating machine learning models (precision, recall, F1, confusion matrix, etc.)
- High-level ML services: Comprehend, Translate, Polly, Transcribe, Lex, Rekognition, and more
- Security best practices with machine learning on AWS
