AWS Certified Machine Learning Specialty 2022 - Hands On!

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, 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

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 11-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:

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.

his 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.